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# Conflicts:
#	bb_trade_log_20260228.txt
This commit is contained in:
27942
2026-03-02 10:56:08 +08:00
parent aec9fc7830 9a383143e0
commit 79bf079548
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WXAUTOX_LICENSE
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~Ew3IKb0y_M#*JAU9ygsGw-+|!kbIFmkfKZT(pO^f!Wx=@u,!48kV5E|{lAtnoMWEYhbW!/hLD^,#_.(U%nT!?gke-xZLW{le

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backtest_20250227.py
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"""
BB策略回测脚本 - 2025年2月27日单日回测
参数: 逐仓 200U 1% 100倍 万五手续费 90%返佣
"""
import sys
from pathlib import Path
import pandas as pd
import numpy as np
from datetime import datetime
sys.path.insert(0, str(Path(__file__).resolve().parent))
from strategy.bb_backtest import run_bb_backtest, BBConfig
from strategy.data_loader import load_klines
def main():
# 加载2025年2月27日的5分钟K线数据
print("=" * 80)
print("加载 2025年2月27日 5分钟K线数据...")
print("=" * 80)
try:
df = load_klines(
period='5m',
start_date='2025-02-27',
end_date='2025-02-28',
tz='Asia/Shanghai',
source='bitmart'
)
print(f"✓ 数据加载成功: {len(df)} 根K线")
if len(df) > 0:
print(f" 时间范围: {df.index[0]} ~ {df.index[-1]}")
else:
print("✗ 无数据可用")
return
except Exception as e:
print(f"✗ 数据加载失败: {e}")
return
# 配置回测参数
cfg = BBConfig(
# 布林带参数 (来自 bb_trade.py)
bb_period=10,
bb_std=2.5,
# 仓位管理
initial_capital=200.0, # 200U本金
margin_pct=0.01, # 每次开仓用权益的1%
leverage=100.0, # 100倍杠杆
# 逐仓配置
cross_margin=False, # 逐仓模式
maint_margin_rate=0.005, # 0.5% 维持保证金率
# 手续费和返佣
fee_rate=0.0005, # 万五手续费
rebate_rate=0.0, # 无即时返佣
rebate_pct=0.90, # 次日返佣90%
rebate_hour_utc=0, # UTC 0 点 (北京时间8点)
# 滑点
slippage_pct=0.0005, # 0.05% 滑点
# 成交模式
fill_at_close=False, # 用触轨价成交(理想化)
# 风控
max_daily_loss=50.0, # 日最大亏损50U
max_daily_loss_pct=0.0, # 不启用百分比
# 破产模型
liq_enabled=True, # 启用强平
# 加仓配置 (递增模式)
pyramid_enabled=True,
pyramid_step=0.01, # 1%增量: 开1%, 加2%, 加3%, 加4%
pyramid_max=3, # 最多加3次
)
print("\n" + "=" * 80)
print("回测参数配置")
print("=" * 80)
print(f"初始资本: {cfg.initial_capital} USDT")
print(f"杠杆: {cfg.leverage}x {'逐仓' if not cfg.cross_margin else '全仓'}")
print(f"开仓比例: {cfg.margin_pct:.0%}（递增: {cfg.pyramid_step:.0%}/次, 最多{cfg.pyramid_max}次）")
print(f"手续费: {cfg.fee_rate:.0%}")
print(f"返佣: {cfg.rebate_pct:.0%} 次日 UTC-0 {cfg.rebate_hour_utc}点")
print(f"布林带: BB({cfg.bb_period}, {cfg.bb_std})")
print(f"滑点: {cfg.slippage_pct:.0%}")
print(f"日亏损限制: {cfg.max_daily_loss} USDT")
print(f"强平: {'启用' if cfg.liq_enabled else '禁用'}")
# 运行回测
print("\n" + "=" * 80)
print("运行回测...")
print("=" * 80)
try:
result = run_bb_backtest(df, cfg)
# 打印详细结果
print_backtest_results(result)
# 保存结果
output_dir = Path(__file__).parent / "strategy" / "results"
output_dir.mkdir(parents=True, exist_ok=True)
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
# 保存交易明细
trades_df = pd.DataFrame([
{
"entry_time": t.entry_time,
"exit_time": t.exit_time,
"side": t.side,
"entry_price": f"{t.entry_price:.2f}",
"exit_price": f"{t.exit_price:.2f}",
"qty": f"{t.qty:.4f}",
"margin": f"{t.margin:.2f}",
"gross_pnl": f"{t.gross_pnl:.2f}",
"fee": f"{t.fee:.2f}",
"net_pnl": f"{t.net_pnl:.2f}",
}
for t in result.trades
])
trades_file = output_dir / f"bb_20250227_trades_{timestamp}.csv"
trades_df.to_csv(trades_file, index=False, encoding="utf-8-sig")
print(f"\n✓ 交易明细已保存: {trades_file}")
# 保存完整权益曲线
equity_file = output_dir / f"bb_20250227_equity_{timestamp}.csv"
result.equity_curve.to_csv(equity_file, encoding="utf-8-sig")
print(f"✓ 权益曲线已保存: {equity_file}")
except Exception as e:
print(f"✗ 回测失败: {e}")
import traceback
traceback.print_exc()
def print_backtest_results(result):
"""打印回测结果统计"""
print("\n" + "=" * 80)
print("回测结果汇总 (2025年2月27日)")
print("=" * 80)
# 基础统计
trades = result.trades
equity = result.equity_curve
config = result.config
initial_eq = config.initial_capital
final_eq = equity["equity"].iloc[-1]
total_pnl = final_eq - initial_eq
pnl_pct = (total_pnl / initial_eq) * 100
print(f"\n📊 核心指标")
print(f" 初始权益: {initial_eq:.2f} USDT")
print(f" 最终权益: {final_eq:.2f} USDT")
print(f" 日度收益: {total_pnl:+.2f} USDT ({pnl_pct:+.2f}%)")
print(f" 最高权益: {equity['equity'].max():.2f} USDT")
print(f" 最低权益: {equity['equity'].min():.2f} USDT")
if len(trades) > 0:
print(f"\n📈 交易统计")
print(f" 总交易数: {len(trades)} 笔")
long_trades = [t for t in trades if t.side == "long"]
short_trades = [t for t in trades if t.side == "short"]
print(f" 多头交易: {len(long_trades)} 笔")
print(f" 空头交易: {len(short_trades)} 笔")
# 胜率
win_trades = [t for t in trades if t.net_pnl > 0]
loss_trades = [t for t in trades if t.net_pnl < 0]
print(f" 盈利交易: {len(win_trades)} 笔 ({len(win_trades)/len(trades)*100:.1f}%)")
print(f" 亏损交易: {len(loss_trades)} 笔 ({len(loss_trades)/len(trades)*100:.1f}%)")
if len(win_trades) > 0:
avg_win = sum(t.net_pnl for t in win_trades) / len(win_trades)
total_win = sum(t.net_pnl for t in win_trades)
print(f" 平均盈利: {avg_win:+.2f} USDT")
print(f" 总盈利: {total_win:+.2f} USDT")
if len(loss_trades) > 0:
avg_loss = sum(t.net_pnl for t in loss_trades) / len(loss_trades)
total_loss = sum(t.net_pnl for t in loss_trades)
print(f" 平均亏损: {avg_loss:+.2f} USDT")
print(f" 总亏损: {total_loss:+.2f} USDT")
# 风险指标
total_net_pnl = sum(t.net_pnl for t in trades)
if len(trades) > 0:
max_loss_trade = min(trades, key=lambda t: t.net_pnl)
max_win_trade = max(trades, key=lambda t: t.net_pnl)
print(f"\n💰 盈亏规模")
print(f" 总手续费支出: {result.total_fee:+.2f} USDT")
print(f" 返佣总额: +{result.total_rebate:.2f} USDT")
print(f" 交易净损益: {total_net_pnl:+.2f} USDT")
print(f" 单笔最大亏损: {max_loss_trade.net_pnl:+.2f} USDT")
print(f" 单笔最大盈利: {max_win_trade.net_pnl:+.2f} USDT")
else:
print(f"\n⚠️ 本日无交易触发")
# 波动率统计
if len(equity) > 0:
print(f"\n📉 风险指标")
equity_series = equity["equity"].astype(float)
running_max = equity_series.expanding().max()
drawdown = (equity_series / running_max - 1)
max_dd = drawdown.min() * 100
print(f" 最大回撤: {max_dd:.2f}%")
print(f" 日均价格: {equity['price'].mean():.2f}")
print(f" 日最高价: {equity['price'].max():.2f}")
print(f" 日最低价: {equity['price'].min():.2f}")
print("\n" + "=" * 80)
if __name__ == "__main__":
main()

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backtest_feb2025.py
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"""
BB策略回测脚本 - 2025年2月份
参数: 逐仓 200U 1% 100倍 万五手续费 90%返佣
"""
import sys
from pathlib import Path
import pandas as pd
import numpy as np
from datetime import datetime
sys.path.insert(0, str(Path(__file__).resolve().parent))
from strategy.bb_backtest import run_bb_backtest, BBConfig
from strategy.data_loader import load_klines
def main():
# 加载2025年2月份的5分钟K线数据
print("=" * 70)
print("加载 2025年2月 5分钟K线数据...")
print("=" * 70)
try:
df = load_klines(
period='5m',
start_date='2025-02-01',
end_date='2025-03-01',
tz='Asia/Shanghai',
source='bitmart'
)
print(f"✓ 数据加载成功: {len(df)} 根K线")
print(f" 时间范围: {df.index[0]} ~ {df.index[-1]}")
except Exception as e:
print(f"✗ 数据加载失败: {e}")
return
# 配置回测参数
cfg = BBConfig(
# 布林带参数 (来自 bb_trade.py)
bb_period=10,
bb_std=2.5,
# 仓位管理
initial_capital=200.0, # 200U本金
margin_pct=0.01, # 每次开仓用权益的1%
leverage=100.0, # 100倍杠杆
# 逐仓配置
cross_margin=False, # 逐仓模式
maint_margin_rate=0.005, # 0.5% 维持保证金率
# 手续费和返佣
fee_rate=0.0005, # 万五手续费
rebate_rate=0.0, # 无即时返佣
rebate_pct=0.90, # 次日返佣90%
rebate_hour_utc=0, # UTC 0 点 (北京时间8点)
# 滑点
slippage_pct=0.0005, # 0.05% 滑点
# 成交模式
fill_at_close=False, # 用触轨价成交(理想化)，False=触轨价，True=收盘价
# 风控
max_daily_loss=50.0, # 日最大亏损50U(不启用百分比限制)
max_daily_loss_pct=0.0, # 不启用百分比
# 破产模型
liq_enabled=True, # 启用强平
# 加仓配置 (递增模式)
pyramid_enabled=True,
pyramid_step=0.01, # 1%增量: 开1%, 加2%, 加3%, 加4%
pyramid_max=3, # 最多加3次
)
print("\n" + "=" * 70)
print("回测参数配置")
print("=" * 70)
print(f"初始资本: {cfg.initial_capital} USDT")
print(f"杠杆: {cfg.leverage}x {'逐仓' if not cfg.cross_margin else '全仓'}")
print(f"开仓比例: {cfg.margin_pct:.0%}（递增: {cfg.pyramid_step:.0%}/次, 最多{cfg.pyramid_max}次）")
print(f"手续费: {cfg.fee_rate:.0%}")
print(f"返佣: {cfg.rebate_pct:.0%} 次日 UTC-0 {cfg.rebate_hour_utc}点")
print(f"布林带: BB({cfg.bb_period}, {cfg.bb_std})")
print(f"滑点: {cfg.slippage_pct:.0%}")
print(f"日亏损限制: {cfg.max_daily_loss} USDT")
print(f"强平: {'启用' if cfg.liq_enabled else '禁用'}")
# 运行回测
print("\n" + "=" * 70)
print("运行回测...")
print("=" * 70)
try:
result = run_bb_backtest(df, cfg)
# 打印详细结果
print_backtest_results(result)
# 保存结果
output_dir = Path(__file__).parent / "strategy" / "results"
output_dir.mkdir(parents=True, exist_ok=True)
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
# 保存交易明细
trades_df = pd.DataFrame([
{
"entry_time": t.entry_time,
"exit_time": t.exit_time,
"side": t.side,
"entry_price": f"{t.entry_price:.2f}",
"exit_price": f"{t.exit_price:.2f}",
"qty": f"{t.qty:.4f}",
"margin": f"{t.margin:.2f}",
"gross_pnl": f"{t.gross_pnl:.2f}",
"fee": f"{t.fee:.2f}",
"net_pnl": f"{t.net_pnl:.2f}",
}
for t in result.trades
])
trades_file = output_dir / f"bb_feb2025_trades_{timestamp}.csv"
trades_df.to_csv(trades_file, index=False, encoding="utf-8-sig")
print(f"\n✓ 交易明细已保存: {trades_file}")
# 保存日行情
daily_file = output_dir / f"bb_feb2025_daily_{timestamp}.csv"
result.daily_stats.to_csv(daily_file, encoding="utf-8-sig")
print(f"✓ 日行情已保存: {daily_file}")
# 保存完整权益曲线
equity_file = output_dir / f"bb_feb2025_equity_{timestamp}.csv"
result.equity_curve.to_csv(equity_file, encoding="utf-8-sig")
print(f"✓ 权益曲线已保存: {equity_file}")
except Exception as e:
print(f"✗ 回测失败: {e}")
import traceback
traceback.print_exc()
def print_backtest_results(result):
"""打印回测结果统计"""
print("\n" + "=" * 70)
print("回测结果汇总")
print("=" * 70)
# 基础统计
trades = result.trades
equity = result.equity_curve
daily = result.daily_stats
config = result.config
initial_eq = config.initial_capital
final_eq = equity["equity"].iloc[-1]
total_pnl = final_eq - initial_eq
pnl_pct = (total_pnl / initial_eq) * 100
print(f"\n📊 核心指标")
print(f" 初始权益: {initial_eq:.2f} USDT")
print(f" 最终权益: {final_eq:.2f} USDT")
print(f" 总损益: {total_pnl:+.2f} USDT ({pnl_pct:+.2f}%)")
print(f" 最大权益: {equity['equity'].max():.2f} USDT")
print(f" 最小权益: {equity['equity'].min():.2f} USDT")
if len(trades) > 0:
print(f"\n📈 交易统计")
print(f" 总交易数: {len(trades)} 笔")
long_trades = [t for t in trades if t.side == "long"]
short_trades = [t for t in trades if t.side == "short"]
print(f" 多头交易: {len(long_trades)} 笔")
print(f" 空头交易: {len(short_trades)} 笔")
# 胜率
win_trades = [t for t in trades if t.net_pnl > 0]
loss_trades = [t for t in trades if t.net_pnl < 0]
print(f" 盈利交易: {len(win_trades)} 笔 ({len(win_trades)/len(trades)*100:.1f}%)")
print(f" 亏损交易: {len(loss_trades)} 笔 ({len(loss_trades)/len(trades)*100:.1f}%)")
if len(win_trades) > 0:
avg_win = sum(t.net_pnl for t in win_trades) / len(win_trades)
print(f" 平均盈利: {avg_win:+.2f} USDT")
if len(loss_trades) > 0:
avg_loss = sum(t.net_pnl for t in loss_trades) / len(loss_trades)
print(f" 平均亏损: {avg_loss:+.2f} USDT")
# 风险指标
total_net_pnl = sum(t.net_pnl for t in trades)
max_loss_trade = min(trades, key=lambda t: t.net_pnl)
max_win_trade = max(trades, key=lambda t: t.net_pnl)
print(f"\n💰 盈亏规模")
print(f" 总手续费: {result.total_fee:+.2f} USDT")
print(f" 总返佣: {result.total_rebate:+.2f} USDT")
print(f" 交易净损益: {total_net_pnl:+.2f} USDT")
print(f" 单笔最大亏损: {max_loss_trade.net_pnl:+.2f} USDT")
print(f" 单笔最大盈利: {max_win_trade.net_pnl:+.2f} USDT")
# 日行情统计
if len(daily) > 0:
print(f"\n📅 日度统计")
print(f" 交易天数: {len(daily)} 天")
daily_wins = len(daily[daily["pnl"] > 0])
daily_loss = len(daily[daily["pnl"] < 0])
print(f" 日赢利天数: {daily_wins} 天")
print(f" 日亏损天数: {daily_loss} 天")
max_dd = (equity["equity"] / equity["equity"].expanding().max() - 1).min()
print(f" 最大回撤: {max_dd*100:.2f}%")
if max_dd != 0:
print(f" 回撤恢复指标: {total_pnl / (max_dd * initial_eq):.2f}")
print("\n" + "=" * 70)
if __name__ == "__main__":
main()

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bb_trade_log_20260225.txt
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============================================================
时间: 2026-02-25 02:15:20
操作: 开多
价格: 1845.39
BB上轨: 1859.97 | 中轨: 1852.40 | 下轨: 1844.82
原因: 价格最低1844.82触及下轨1844.82，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-25 02:19:03
操作: 开多
价格: 1848.84
BB上轨: 1859.97 | 中轨: 1852.40 | 下轨: 1844.82
原因: 价格最低1844.50触及下轨1844.82，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-25 02:41:34
操作: 翻转: 平多→开空
价格: 1858.71
BB上轨: 1858.17 | 中轨: 1851.64 | 下轨: 1845.11
原因: 价格最高1859.14触及上轨1858.17，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-25 03:14:27
操作: 翻转: 平空→开多
价格: 1848.69
BB上轨: 1860.75 | 中轨: 1854.64 | 下轨: 1848.52
原因: 价格最低1848.42触及下轨1848.52，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-25 04:16:34
操作: 开空
价格: 1855.43
BB上轨: 1854.87 | 中轨: 1851.18 | 下轨: 1847.48
原因: 价格最高1855.43触及上轨1854.87，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-25 05:02:18
操作: 翻转: 平空→开多
价格: 1850.52
BB上轨: 1861.45 | 中轨: 1856.03 | 下轨: 1850.62
原因: 价格最低1850.51触及下轨1850.62，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-25 06:02:28
操作: 翻转: 平多→开空
价格: 1856.46
BB上轨: 1855.53 | 中轨: 1850.22 | 下轨: 1844.90
原因: 价格最高1856.46触及上轨1855.53，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-25 06:05:10
操作: 加仓空#1
价格: 1859.51
BB上轨: 1859.33 | 中轨: 1850.90 | 下轨: 1842.47
原因: 价格最高1859.51触及上轨1859.33，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-25 06:14:11
操作: 加仓空#2
价格: 1862.50
BB上轨: 1862.15 | 中轨: 1852.06 | 下轨: 1841.96
原因: 价格最高1862.50触及上轨1862.15，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-25 06:54:01
操作: 翻转: 平空→开多
价格: 1854.88
BB上轨: 1861.07 | 中轨: 1858.10 | 下轨: 1855.13
原因: 价格最低1854.16触及下轨1855.13，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-25 07:05:18
操作: 加仓多#1
价格: 1854.10
BB上轨: 1860.27 | 中轨: 1857.26 | 下轨: 1854.24
原因: 价格最低1854.11触及下轨1854.24，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-25 07:12:14
操作: 加仓多#2
价格: 1851.97
BB上轨: 1861.45 | 中轨: 1856.76 | 下轨: 1852.08
原因: 价格最低1851.84触及下轨1852.08，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-25 08:02:28
操作: 加仓多#3
价格: 1848.66
BB上轨: 1861.19 | 中轨: 1854.98 | 下轨: 1848.77
原因: 价格最低1849.00触及下轨1848.77，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-25 08:44:44
操作: 翻转: 平多→开空
价格: 1854.51
BB上轨: 1853.90 | 中轨: 1849.13 | 下轨: 1844.37
原因: 价格最高1854.50触及上轨1853.90，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-25 09:07:53
操作: 加仓空#1
价格: 1859.00
BB上轨: 1857.89 | 中轨: 1851.08 | 下轨: 1844.26
原因: 价格最高1859.00触及上轨1857.89，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-25 09:10:34
操作: 加仓空#2
价格: 1865.87
BB上轨: 1864.29 | 中轨: 1852.44 | 下轨: 1840.58
原因: 价格最高1865.87触及上轨1864.29，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-25 09:15:11
操作: 加仓空#3
价格: 1904.57
BB上轨: 1897.79 | 中轨: 1857.93 | 下轨: 1818.07
原因: 价格最高1905.40触及上轨1897.79，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-25 11:16:23
操作: 翻转: 平空→开多
价格: 1913.80
BB上轨: 1932.38 | 中轨: 1922.22 | 下轨: 1912.06
原因: 价格最低1911.26触及下轨1912.06，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-25 11:22:00
操作: 加仓多#1
价格: 1909.05
BB上轨: 1931.22 | 中轨: 1920.13 | 下轨: 1909.05
原因: 价格最低1909.00触及下轨1909.05，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-25 12:03:03
操作: 加仓多#2
价格: 1899.08
BB上轨: 1916.51 | 中轨: 1908.20 | 下轨: 1899.90
原因: 价格最低1898.72触及下轨1899.90，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-25 12:05:24
操作: 加仓多#3
价格: 1899.32
BB上轨: 1913.50 | 中轨: 1906.53 | 下轨: 1899.57
原因: 价格最低1899.32触及下轨1899.57，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-25 12:22:56
操作: 翻转: 平多→开空
价格: 1911.00
BB上轨: 1911.84 | 中轨: 1904.99 | 下轨: 1898.14
原因: 价格最高1912.38触及上轨1911.84，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-25 13:14:17
操作: 翻转: 平空→开多
价格: 1901.32
BB上轨: 1916.59 | 中轨: 1909.31 | 下轨: 1902.03
原因: 价格最低1901.17触及下轨1902.03，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-25 13:22:05
操作: 加仓多#1
价格: 1896.12
BB上轨: 1917.52 | 中轨: 1907.31 | 下轨: 1897.10
原因: 价格最低1895.85触及下轨1897.10，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-25 14:21:26
操作: 翻转: 平多→开空
价格: 1894.08
BB上轨: 1893.64 | 中轨: 1887.38 | 下轨: 1881.12
原因: 价格最高1894.41触及上轨1893.64，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-25 18:58:38
操作: 开多
价格: 1909.81
BB上轨: 1918.84 | 中轨: 1914.21 | 下轨: 1909.58
原因: 价格最低1909.45触及下轨1909.58，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-25 19:05:46
操作: 加仓多#1
价格: 1909.10
BB上轨: 1917.52 | 中轨: 1913.25 | 下轨: 1908.99
原因: 价格最低1908.56触及下轨1908.99，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-25 19:11:58
操作: 翻转: 平多→开空
价格: 1916.64
BB上轨: 1916.02 | 中轨: 1912.97 | 下轨: 1909.93
原因: 价格最高1917.00触及上轨1916.02，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-25 19:22:05
操作: 加仓空#1
价格: 1916.71
BB上轨: 1916.29 | 中轨: 1913.25 | 下轨: 1910.22
原因: 价格最高1916.72触及上轨1916.29，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-25 19:25:13
操作: 加仓空#2
价格: 1918.97
BB上轨: 1918.78 | 中轨: 1913.93 | 下轨: 1909.07
原因: 价格最高1918.99触及上轨1918.78，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-25 19:33:38
操作: 加仓空#3
价格: 1920.52
BB上轨: 1920.45 | 中轨: 1914.37 | 下轨: 1908.30
原因: 价格最高1920.52触及上轨1920.45，BB(10,2.5) (加仓#3/3)
============================================================

192
bb_trade_log_20260226.txt
View File

@@ -1,192 +0,0 @@
============================================================
时间: 2026-02-26 15:37:31
操作: 翻转: 平空→开多
价格: 2057.35
BB上轨: 2067.10 | 中轨: 2062.49 | 下轨: 2057.87
原因: 价格最低2057.32触及下轨2057.87，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-26 15:40:57
操作: 加仓多#1
价格: 2056.25
BB上轨: 2068.33 | 中轨: 2062.01 | 下轨: 2055.69
原因: 价格最低2055.40触及下轨2055.69，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-26 15:51:35
操作: 加仓多#2
价格: 2053.30
BB上轨: 2069.08 | 中轨: 2061.27 | 下轨: 2053.46
原因: 价格最低2053.17触及下轨2053.46，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-26 16:09:24
操作: 加仓多#3
价格: 2048.33
BB上轨: 2066.37 | 中轨: 2057.64 | 下轨: 2048.91
原因: 价格最低2047.81触及下轨2048.91，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-26 16:51:34
操作: 开空
价格: 2056.74
BB上轨: 2056.68 | 中轨: 2050.00 | 下轨: 2043.32
原因: 价格最高2056.74触及上轨2056.68，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-26 17:06:46
操作: 加仓空#1
价格: 2058.39
BB上轨: 2058.12 | 中轨: 2050.99 | 下轨: 2043.87
原因: 价格最高2058.64触及上轨2058.12，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-26 17:11:06
操作: 加仓空#2
价格: 2060.60
BB上轨: 2061.02 | 中轨: 2051.98 | 下轨: 2042.95
原因: 价格最高2061.23触及上轨2061.02，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-26 17:15:14
操作: 加仓空#3
价格: 2069.55
BB上轨: 2068.81 | 中轨: 2053.53 | 下轨: 2038.25
原因: 价格最高2069.08触及上轨2068.81，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-26 18:26:26
操作: 翻转: 平空→开多
价格: 2069.00
BB上轨: 2078.82 | 中轨: 2073.78 | 下轨: 2068.74
原因: 价格最低2068.60触及下轨2068.74，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-26 18:30:18
操作: 加仓多#1
价格: 2065.64
BB上轨: 2080.83 | 中轨: 2072.96 | 下轨: 2065.08
原因: 价格最低2064.90触及下轨2065.08，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-26 19:18:18
操作: 加仓多#1
价格: 2056.57
BB上轨: 2073.88 | 中轨: 2065.36 | 下轨: 2056.83
原因: 价格最低2056.55触及下轨2056.83，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-26 19:47:23
操作: 加仓多#2
价格: 2054.27
BB上轨: 2066.70 | 中轨: 2060.46 | 下轨: 2054.23
原因: 价格最低2053.94触及下轨2054.23，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-26 20:09:37
操作: 翻转: 平多→开空
价格: 2063.02
BB上轨: 2063.19 | 中轨: 2058.15 | 下轨: 2053.11
原因: 价格最高2063.55触及上轨2063.19，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-26 20:18:32
操作: 加仓空#1
价格: 2066.00
BB上轨: 2065.69 | 中轨: 2059.14 | 下轨: 2052.58
原因: 价格最高2066.00触及上轨2065.69，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-26 20:49:59
操作: 加仓空#2
价格: 2069.54
BB上轨: 2070.82 | 中轨: 2063.52 | 下轨: 2056.21
原因: 价格最高2070.84触及上轨2070.82，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-26 21:22:34
操作: 翻转: 平空→开多
价格: 2060.33
BB上轨: 2071.65 | 中轨: 2066.19 | 下轨: 2060.73
原因: 价格最低2060.33触及下轨2060.73，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-26 21:47:47
操作: 翻转: 平多→开空
价格: 2070.41
BB上轨: 2072.64 | 中轨: 2066.47 | 下轨: 2060.31
原因: 价格最高2072.75触及上轨2072.64，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-26 22:03:03
操作: 翻转: 平空→开多
价格: 2060.65
BB上轨: 2072.69 | 中轨: 2066.27 | 下轨: 2059.84
原因: 价格最低2059.40触及下轨2059.84，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-26 22:06:42
操作: 加仓多#1
价格: 2059.82
BB上轨: 2072.86 | 中轨: 2066.15 | 下轨: 2059.44
原因: 价格最低2059.00触及下轨2059.44，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-26 22:30:56
操作: 加仓多#1
价格: 2054.88
BB上轨: 2073.40 | 中轨: 2065.42 | 下轨: 2057.43
原因: 价格最低2052.00触及下轨2057.43，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-26 22:35:22
操作: 翻转: 平多→开空
价格: 2073.86
BB上轨: 2074.10 | 中轨: 2065.63 | 下轨: 2057.16
原因: 价格最高2076.97触及上轨2074.10，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-26 22:45:42
操作: 翻转: 平空→开多
价格: 2052.53
BB上轨: 2073.67 | 中轨: 2064.05 | 下轨: 2054.44
原因: 价格最低2053.71触及下轨2054.44，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-26 22:50:13
操作: 加仓多#1
价格: 2039.79
BB上轨: 2082.56 | 中轨: 2061.54 | 下轨: 2040.53
原因: 价格最低2038.54触及下轨2040.53，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-26 23:16:31
操作: 加仓多#2
价格: 2028.67
BB上轨: 2079.34 | 中轨: 2052.02 | 下轨: 2024.71
原因: 价格最低2023.71触及下轨2024.71，BB(10,2.5) (加仓#2/3)
============================================================

440
bb_trade_log_20260227.txt
View File

@@ -1,440 +0,0 @@
============================================================
时间: 2026-02-27 00:34:52
操作: 加仓多#3
价格: 2019.27
BB上轨: 2038.07 | 中轨: 2029.31 | 下轨: 2020.55
原因: 价格最低2018.47触及下轨2020.55，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-27 00:45:54
操作: 开多
价格: 1995.09
BB上轨: 2041.76 | 中轨: 2024.21 | 下轨: 2006.67
原因: 价格最低1991.10触及下轨2006.67，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 00:50:23
操作: 加仓多#1
价格: 1997.38
BB上轨: 2044.57 | 中轨: 2021.06 | 下轨: 1997.54
原因: 价格最低1997.37触及下轨1997.54，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 00:58:13
操作: 加仓多#2
价格: 1981.63
BB上轨: 2052.12 | 中轨: 2016.68 | 下轨: 1981.24
原因: 价格最低1980.90触及下轨1981.24，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-27 01:50:45
操作: 加仓多#3
价格: 1981.96
BB上轨: 1996.89 | 中轨: 1989.38 | 下轨: 1981.88
原因: 价格最低1981.72触及下轨1981.88，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-27 02:33:12
操作: 翻转: 平多→开空
价格: 1993.72
BB上轨: 1994.06 | 中轨: 1986.40 | 下轨: 1978.74
原因: 价格最高1994.73触及上轨1994.06，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 02:36:47
操作: 加仓空#1
价格: 1999.68
BB上轨: 1998.39 | 中轨: 1987.39 | 下轨: 1976.39
原因: 价格最高1999.68触及上轨1998.39，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 02:43:43
操作: 加仓空#2
价格: 2006.48
BB上轨: 2004.93 | 中轨: 1989.32 | 下轨: 1973.70
原因: 价格最高2006.75触及上轨2004.93，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-27 03:59:44
操作: 加仓空#1
价格: 2026.68
BB上轨: 2032.27 | 中轨: 2024.20 | 下轨: 2016.12
原因: 价格最高2032.94触及上轨2032.27，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 04:03:33
操作: 加仓空#2
价格: 2032.81
BB上轨: 2032.74 | 中轨: 2025.19 | 下轨: 2017.64
原因: 价格最高2032.60触及上轨2032.74，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-27 04:05:12
操作: 加仓空#3
价格: 2038.22
BB上轨: 2038.99 | 中轨: 2027.23 | 下轨: 2015.47
原因: 价格最高2039.70触及上轨2038.99，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-27 05:10:18
操作: 翻转: 平空→开多
价格: 2017.56
BB上轨: 2032.98 | 中轨: 2025.96 | 下轨: 2018.93
原因: 价格最低2017.55触及下轨2018.93，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 05:48:57
操作: 翻转: 平多→开空
价格: 2033.77
BB上轨: 2033.55 | 中轨: 2026.29 | 下轨: 2019.02
原因: 价格最高2033.78触及上轨2033.55，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 06:36:53
操作: 加仓空#1
价格: 2034.32
BB上轨: 2033.90 | 中轨: 2030.08 | 下轨: 2026.26
原因: 价格最高2034.32触及上轨2033.90，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 06:41:38
操作: 加仓空#2
价格: 2033.41
BB上轨: 2033.33 | 中轨: 2029.99 | 下轨: 2026.64
原因: 价格最高2033.41触及上轨2033.33，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-27 06:45:52
操作: 加仓空#3
价格: 2036.11
BB上轨: 2036.02 | 中轨: 2030.57 | 下轨: 2025.11
原因: 价格最高2036.12触及上轨2036.02，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-27 07:05:56
操作: 翻转: 平空→开多
价格: 2023.39
BB上轨: 2038.77 | 中轨: 2031.22 | 下轨: 2023.66
原因: 价格最低2023.29触及下轨2023.66，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 07:51:41
操作: 加仓多#1
价格: 2023.26
BB上轨: 2032.74 | 中轨: 2028.23 | 下轨: 2023.72
原因: 价格最低2023.26触及下轨2023.72，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 08:04:43
操作: 加仓多#2
价格: 2023.45
BB上轨: 2032.80 | 中轨: 2028.16 | 下轨: 2023.52
原因: 价格最低2023.45触及下轨2023.52，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-27 08:18:36
操作: 加仓多#3
价格: 2020.68
BB上轨: 2032.86 | 中轨: 2027.04 | 下轨: 2021.22
原因: 价格最低2020.68触及下轨2021.22，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-27 10:20:03
操作: 翻转: 平多→开空
价格: 2028.97
BB上轨: 2028.81 | 中轨: 2017.30 | 下轨: 2005.80
原因: 价格最高2029.00触及上轨2028.81，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 10:20:40
操作: 加仓空#1
价格: 2029.67
BB上轨: 2030.85 | 中轨: 2019.44 | 下轨: 2008.03
原因: 价格最高2031.42触及上轨2030.85，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 11:06:08
操作: 翻转: 平空→开多
价格: 2021.85
BB上轨: 2032.13 | 中轨: 2027.36 | 下轨: 2022.58
原因: 价格最低2021.85触及下轨2022.58，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 11:11:59
操作: 加仓多#1
价格: 2020.95
BB上轨: 2032.35 | 中轨: 2026.75 | 下轨: 2021.14
原因: 价格最低2020.82触及下轨2021.14，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 11:45:07
操作: 翻转: 平多→开空
价格: 2029.42
BB上轨: 2030.01 | 中轨: 2023.47 | 下轨: 2016.93
原因: 价格最高2030.16触及上轨2030.01，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 11:49:54
操作: 加仓空#1
价格: 2031.58
BB上轨: 2031.24 | 中轨: 2023.72 | 下轨: 2016.19
原因: 价格最高2031.59触及上轨2031.24，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 11:52:39
操作: 加仓空#2
价格: 2034.61
BB上轨: 2033.42 | 中轨: 2024.27 | 下轨: 2015.12
原因: 价格最高2034.83触及上轨2033.42，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-27 11:56:42
操作: 加仓空#3
价格: 2038.24
BB上轨: 2037.16 | 中轨: 2025.24 | 下轨: 2013.32
原因: 价格最高2038.24触及上轨2037.16，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-27 12:58:23
操作: 翻转: 平空→开多
价格: 2040.81
BB上轨: 2063.10 | 中轨: 2051.08 | 下轨: 2039.06
原因: 价格最低2035.83触及下轨2039.06，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 13:23:15
操作: 加仓多#1
价格: 2040.84
BB上轨: 2050.50 | 中轨: 2045.50 | 下轨: 2040.51
原因: 价格最低2040.21触及下轨2040.51，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 13:57:37
操作: 加仓多#1
价格: 2040.17
BB上轨: 2047.74 | 中轨: 2043.81 | 下轨: 2039.88
原因: 价格最低2039.84触及下轨2039.88，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 14:01:28
操作: 加仓多#2
价格: 2039.00
BB上轨: 2048.08 | 中轨: 2043.60 | 下轨: 2039.11
原因: 价格最低2038.43触及下轨2039.11，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-27 14:08:09
操作: 加仓多#3
价格: 2038.54
BB上轨: 2047.75 | 中轨: 2043.19 | 下轨: 2038.63
原因: 价格最低2038.30触及下轨2038.63，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-27 15:09:03
操作: 翻转: 平多→开空
价格: 2039.58
BB上轨: 2039.40 | 中轨: 2034.52 | 下轨: 2029.64
原因: 价格最高2039.71触及上轨2039.40，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 15:21:53
操作: 翻转: 平空→开多
价格: 2031.37
BB上轨: 2039.67 | 中轨: 2035.57 | 下轨: 2031.46
原因: 价格最低2031.25触及下轨2031.46，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 15:29:08
操作: 加仓多#1
价格: 2031.29
BB上轨: 2039.73 | 中轨: 2035.54 | 下轨: 2031.35
原因: 价格最低2031.20触及下轨2031.35，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 15:37:53
操作: 加仓多#2
价格: 2028.64
BB上轨: 2039.96 | 中轨: 2034.30 | 下轨: 2028.65
原因: 价格最低2028.64触及下轨2028.65，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-27 15:40:26
操作: 加仓多#3
价格: 2023.35
BB上轨: 2042.08 | 中轨: 2033.08 | 下轨: 2024.09
原因: 价格最低2023.35触及下轨2024.09，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-27 16:09:03
操作: 翻转: 平多→开空
价格: 2037.82
BB上轨: 2038.00 | 中轨: 2029.64 | 下轨: 2021.28
原因: 价格最高2038.44触及上轨2038.00，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 16:10:11
操作: 加仓空#1
价格: 2040.95
BB上轨: 2041.45 | 中轨: 2030.26 | 下轨: 2019.08
原因: 价格最高2042.00触及上轨2041.45，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 17:17:59
操作: 加仓空#2
价格: 2037.56
BB上轨: 2037.39 | 中轨: 2033.42 | 下轨: 2029.45
原因: 价格最高2037.56触及上轨2037.39，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-27 17:20:15
操作: 加仓空#3
价格: 2038.29
BB上轨: 2038.51 | 中轨: 2033.60 | 下轨: 2028.70
原因: 价格最高2038.54触及上轨2038.51，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-27 17:37:16
操作: 翻转: 平空→开多
价格: 2030.27
BB上轨: 2039.23 | 中轨: 2034.69 | 下轨: 2030.15
原因: 价格最低2029.69触及下轨2030.15，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 17:42:11
操作: 加仓多#1
价格: 2027.07
BB上轨: 2040.89 | 中轨: 2034.17 | 下轨: 2027.44
原因: 价格最低2026.84触及下轨2027.44，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 17:47:31
操作: 开多
价格: 2015.02
BB上轨: 2046.09 | 中轨: 2032.76 | 下轨: 2019.42
原因: 价格最低2011.72触及下轨2019.42，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 17:51:12
操作: 加仓多#1
价格: 2012.12
BB上轨: 2049.50 | 中轨: 2031.08 | 下轨: 2012.65
原因: 价格最低2011.75触及下轨2012.65，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 18:50:47
操作: 开多
价格: 1981.75
BB上轨: 2008.92 | 中轨: 1993.81 | 下轨: 1978.71
原因: 价格最低1977.18触及下轨1978.71，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 19:17:27
操作: 加仓多#1
价格: 1972.22
BB上轨: 1999.72 | 中轨: 1985.80 | 下轨: 1971.88
原因: 价格最低1970.22触及下轨1971.88，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 19:39:39
操作: 加仓多#2
价格: 1961.41
BB上轨: 1993.01 | 中轨: 1977.21 | 下轨: 1961.41
原因: 价格最低1961.36触及下轨1961.41，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-27 19:41:51
操作: 加仓多#3
价格: 1956.50
BB上轨: 1991.23 | 中轨: 1974.42 | 下轨: 1957.61
原因: 价格最低1956.50触及下轨1957.61，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-27 20:27:33
操作: 翻转: 平多→开空
价格: 1965.94
BB上轨: 1965.48 | 中轨: 1960.08 | 下轨: 1954.68
原因: 价格最高1965.94触及上轨1965.48，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 20:42:26
操作: 翻转: 平空→开多
价格: 1955.65
BB上轨: 1965.42 | 中轨: 1960.60 | 下轨: 1955.77
原因: 价格最低1955.64触及下轨1955.77，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-27 20:45:33
操作: 加仓多#1
价格: 1952.42
BB上轨: 1966.37 | 中轨: 1960.12 | 下轨: 1953.87
原因: 价格最低1952.42触及下轨1953.87，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-27 21:36:10
操作: 加仓多#2
价格: 1949.38
BB上轨: 1967.58 | 中轨: 1958.42 | 下轨: 1949.27
原因: 价格最低1949.23触及下轨1949.27，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-27 22:43:04
操作: 加仓多#3
价格: 1945.85
BB上轨: 1965.93 | 中轨: 1955.68 | 下轨: 1945.44
原因: 价格最低1945.00触及下轨1945.44，BB(10,2.5) (加仓#3/3)
============================================================

232
bb_trade_log_20260228.txt
View File

@@ -1,232 +0,0 @@
============================================================
时间: 2026-02-28 01:19:27
操作: 开多
价格: 1916.03
BB上轨: 1946.64 | 中轨: 1931.71 | 下轨: 1916.78
原因: 价格最低1915.60触及下轨1916.78，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-28 01:23:07
操作: 加仓多#1
价格: 1912.34
BB上轨: 1945.77 | 中轨: 1929.22 | 下轨: 1912.67
原因: 价格最低1912.34触及下轨1912.67，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-28 01:28:21
操作: 加仓多#2
价格: 1911.23
BB上轨: 1942.48 | 中轨: 1926.75 | 下轨: 1911.02
原因: 价格最低1910.24触及下轨1911.02，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-28 01:31:41
操作: 加仓多#3
价格: 1906.88
BB上轨: 1941.53 | 中轨: 1924.30 | 下轨: 1907.06
原因: 价格最低1906.99触及下轨1907.06，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-28 06:34:30
操作: 加仓多#1
价格: 1908.38
BB上轨: 1927.72 | 中轨: 1918.60 | 下轨: 1909.47
原因: 价格最低1908.31触及下轨1909.47，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-28 06:35:11
操作: 加仓多#2
价格: 1892.16
BB上轨: 1937.73 | 中轨: 1914.97 | 下轨: 1892.22
原因: 价格最低1889.17触及下轨1892.22，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-28 08:07:34
操作: 翻转: 平多→开空
价格: 1931.86
BB上轨: 1931.43 | 中轨: 1929.13 | 下轨: 1926.83
原因: 价格最高1931.86触及上轨1931.43，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-28 08:11:19
操作: 加仓空#1
价格: 1931.98
BB上轨: 1931.89 | 中轨: 1929.38 | 下轨: 1926.86
原因: 价格最高1931.99触及上轨1931.89，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-28 08:15:12
操作: 加仓空#2
价格: 1935.46
BB上轨: 1935.14 | 中轨: 1929.96 | 下轨: 1924.79
原因: 价格最高1935.80触及上轨1935.14，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-28 09:28:30
操作: 开多
价格: 1925.76
BB上轨: 1935.12 | 中轨: 1930.99 | 下轨: 1926.86
原因: 价格最低1925.54触及下轨1926.86，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-28 09:30:09
操作: 加仓多#1
价格: 1924.35
BB上轨: 1936.62 | 中轨: 1930.70 | 下轨: 1924.79
原因: 价格最低1924.35触及下轨1924.79，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-28 11:31:58
操作: 开多
价格: 1924.43
BB上轨: 1931.27 | 中轨: 1928.33 | 下轨: 1925.38
原因: 价格最低1923.19触及下轨1925.38，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-28 11:36:12
操作: 加仓多#1
价格: 1923.24
BB上轨: 1932.37 | 中轨: 1927.89 | 下轨: 1923.42
原因: 价格最低1923.23触及下轨1923.42，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-28 11:40:41
操作: 加仓多#2
价格: 1921.27
BB上轨: 1933.00 | 中轨: 1927.10 | 下轨: 1921.20
原因: 价格最低1921.17触及下轨1921.20，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-28 12:29:22
操作: 翻转: 平多→开空
价格: 1927.86
BB上轨: 1925.70 | 中轨: 1923.42 | 下轨: 1921.14
原因: 价格最高1928.16触及上轨1925.70，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-28 12:30:20
操作: 翻转: 平空→开多
价格: 1919.85
BB上轨: 1926.62 | 中轨: 1923.20 | 下轨: 1919.79
原因: 价格最低1919.32触及下轨1919.79，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-28 13:28:35
操作: 加仓多#1
价格: 1923.17
BB上轨: 1927.09 | 中轨: 1925.14 | 下轨: 1923.20
原因: 价格最低1923.17触及下轨1923.20，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-28 13:30:10
操作: 加仓多#2
价格: 1921.55
BB上轨: 1927.86 | 中轨: 1924.83 | 下轨: 1921.80
原因: 价格最低1921.55触及下轨1921.80，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-28 13:35:11
操作: 加仓多#3
价格: 1917.77
BB上轨: 1930.09 | 中轨: 1923.98 | 下轨: 1917.87
原因: 价格最低1917.50触及下轨1917.87，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-28 18:22:52
操作: 开空
价格: 1858.77
BB上轨: 1858.59 | 中轨: 1852.48 | 下轨: 1846.38
原因: 价格最高1858.77触及上轨1858.59，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-28 18:31:47
操作: 加仓空#1
价格: 1860.51
BB上轨: 1860.09 | 中轨: 1852.96 | 下轨: 1845.83
原因: 价格最高1860.04触及上轨1860.09，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-28 19:01:57
操作: 加仓空#2
价格: 1870.00
BB上轨: 1870.20 | 中轨: 1859.65 | 下轨: 1849.09
原因: 价格最高1874.12触及上轨1870.20，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-28 19:28:41
操作: 加仓空#3
价格: 1877.07
BB上轨: 1877.05 | 中轨: 1866.53 | 下轨: 1856.00
原因: 价格最高1877.10触及上轨1877.05，BB(10,2.5) (加仓#3/3)
============================================================
============================================================
时间: 2026-02-28 19:57:38
操作: 翻转: 平空→开多
价格: 1862.42
BB上轨: 1878.16 | 中轨: 1870.54 | 下轨: 1862.92
原因: 价格最低1862.41触及下轨1862.92，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-28 20:53:41
操作: 加仓多#1
价格: 1861.17
BB上轨: 1872.96 | 中轨: 1867.11 | 下轨: 1861.25
原因: 价格最低1861.17触及下轨1861.25，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-28 21:19:58
操作: 开空
价格: 1870.39
BB上轨: 1872.99 | 中轨: 1867.66 | 下轨: 1862.32
原因: 价格最高1876.19触及上轨1872.99，BB(10,2.5)
============================================================
============================================================
时间: 2026-02-28 21:46:18
操作: 加仓空#1
价格: 1873.28
BB上轨: 1873.28 | 中轨: 1868.72 | 下轨: 1864.17
原因: 价格最高1873.28触及上轨1873.28，BB(10,2.5) (加仓#1/3)
============================================================
============================================================
时间: 2026-02-28 21:50:07
操作: 加仓空#2
价格: 1890.51
BB上轨: 1887.97 | 中轨: 1871.04 | 下轨: 1854.10
原因: 价格最高1890.30触及上轨1887.97，BB(10,2.5) (加仓#2/3)
============================================================
============================================================
时间: 2026-02-28 21:59:21
操作: 加仓空#3
价格: 1902.91
BB上轨: 1901.44 | 中轨: 1874.44 | 下轨: 1847.43
原因: 价格最高1903.50触及上轨1901.44，BB(10,2.5) (加仓#3/3)
============================================================

36
check_db.py
View File

@@ -1,36 +0,0 @@
import sqlite3
from pathlib import Path
from datetime import datetime
db_path = Path("/Users/ddrwode/code/codex_jxs_code/models/database.db")
conn = sqlite3.connect(str(db_path))
cursor = conn.cursor()
# 列出所有表
cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
tables = cursor.fetchall()
print("数据库表列表:")
for table in tables:
print(f" - {table[0]}")
# 检查bitmart_eth_5m表和binance_eth_5m表
for table_name in ['bitmart_eth_5m', 'binance_eth_5m']:
try:
print(f"\n检查 {table_name} 表:")
cursor.execute(f"SELECT COUNT(*) FROM {table_name}")
count = cursor.fetchone()[0]
print(f" 记录数: {count}")
if count > 0:
# 获取时间范围
cursor.execute(f"SELECT MIN(id), MAX(id) FROM {table_name}")
result = cursor.fetchone()
if result[0] and result[1]:
min_id, max_id = result
min_date = datetime.fromtimestamp(min_id/1000).strftime('%Y-%m-%d %H:%M:%S')
max_date = datetime.fromtimestamp(max_id/1000).strftime('%Y-%m-%d %H:%M:%S')
print(f" 时间范围: {min_date} ~ {max_date}")
except Exception as e:
print(f" 错误: {e}")
conn.close()

131
feb2025_report.py
View File

@@ -1,131 +0,0 @@
"""
2025年2月 BB策略回测报告
逐仓交易 | 200U本金 | 100倍杠杆 | 万五手续费 | 90%返佣
"""
import pandas as pd
from pathlib import Path
# 加载结果
results_dir = Path("/Users/ddrwode/code/codex_jxs_code/strategy/results")
trades_file = results_dir / "bb_feb2025_trades_20260228_122306.csv"
daily_file = results_dir / "bb_feb2025_daily_20260228_122306.csv"
equity_file = results_dir / "bb_feb2025_equity_20260228_122306.csv"
trades_df = pd.read_csv(trades_file)
daily_df = pd.read_csv(daily_file)
equity_df = pd.read_csv(equity_file)
print("=" * 80)
print("2025年2月 BB策略回测报告")
print("=" * 80)
print("\n【 配置参数 】")
print(f" 时间周期： 2025年2月（1个月）")
print(f" 初始资本： 200.00 USDT")
print(f" 杠杆倍数： 100倍（逐仓）")
print(f" 开仓比例： 1%（首次开仓1%, 递增加仓2%-4%, 最多3次）")
print(f" 手续费率： 0.05%（万五）")
print(f" 返佣政策： 90% 次日早上8点到账")
print(f" 布林带参数： BB(10, 2.5)")
print(f" K线周期： 5分钟")
print(f" 数据来源： BitMart")
# 主要收益指标
print("\n【 收益指标 】")
initial_equity = 200.0
final_equity = 986.17
monthly_pnl = final_equity - initial_equity
monthly_return = (monthly_pnl / initial_equity) * 100
print(f" 初始权益： {initial_equity:.2f} USDT")
print(f" 最终权益： {final_equity:.2f} USDT")
print(f" 月度收益： +{monthly_pnl:.2f} USDT")
print(f" 月度收益率： {monthly_return:+.2f}%")
print(f" 最高权益： {equity_df['equity'].max():.2f} USDT")
print(f" 最低权益： {equity_df['equity'].min():.2f} USDT")
# 交易统计
print("\n【 交易统计 】")
total_trades = len(trades_df)
long_trades = len(trades_df[trades_df['side'] == 'long'])
short_trades = len(trades_df[trades_df['side'] == 'short'])
# 交易PnL
trades_df['net_pnl_float'] = trades_df['net_pnl'].astype(float)
profitable_trades = len(trades_df[trades_df['net_pnl_float'] > 0])
losing_trades = len(trades_df[trades_df['net_pnl_float'] < 0])
win_rate = (profitable_trades / total_trades * 100) if total_trades > 0 else 0
print(f" 总交易数： {total_trades} 笔")
print(f" 多头交易： {long_trades} 笔 ({long_trades/total_trades*100:.1f}%)")
print(f" 空头交易： {short_trades} 笔 ({short_trades/total_trades*100:.1f}%)")
print(f" 盈利交易： {profitable_trades} 笔 ({win_rate:.1f}%)")
print(f" 亏损交易： {losing_trades} 笔 ({100-win_rate:.1f}%)")
# 交易规模
if profitable_trades > 0:
avg_win = trades_df[trades_df['net_pnl_float'] > 0]['net_pnl_float'].mean()
max_win = trades_df[trades_df['net_pnl_float'] > 0]['net_pnl_float'].max()
print(f" 平均盈利： +{avg_win:.2f} USDT")
print(f" 最大盈利： +{max_win:.2f} USDT")
if losing_trades > 0:
avg_loss = trades_df[trades_df['net_pnl_float'] < 0]['net_pnl_float'].mean()
max_loss = trades_df[trades_df['net_pnl_float'] < 0]['net_pnl_float'].min()
print(f" 平均亏损： {avg_loss:.2f} USDT")
print(f" 最大亏损： {max_loss:.2f} USDT")
# 风险指标
print("\n【 风险指标 】")
equity_series = equity_df['equity'].astype(float)
running_max = equity_series.expanding().max()
drawdown = (equity_series / running_max - 1)
max_dd = drawdown.min() * 100
max_dd_from_peak = ((equity_series.min() - equity_series.max()) / equity_series.max()) * 100
print(f" 最大回撤： {max_dd:.2f}%")
print(f" 最大回撤额： {equity_series.min() - equity_series.max():.2f} USDT")
# 日度统计
print("\n【 日度统计 】")
trading_days = len(daily_df)
daily_df['pnl_float'] = daily_df['pnl'].astype(float)
profitable_days = len(daily_df[daily_df['pnl_float'] > 0])
losing_days = len(daily_df[daily_df['pnl_float'] < 0])
daily_win_rate = (profitable_days / trading_days * 100) if trading_days > 0 else 0
print(f" 交易天数： {trading_days} 天")
print(f" 盈利天数： {profitable_days} 天 ({profitable_days/trading_days*100:.1f}%)")
print(f" 亏损天数： {losing_days} 天 ({losing_days/trading_days*100:.1f}%)")
print(f" 平均日收益： {monthly_pnl/trading_days:.2f} USDT")
# 手续费与返佣统计
print("\n【 手续费与返佣 】")
total_fee_paid = abs(trades_df['fee'].astype(float).sum())
total_rebate = 468.68 # 从回测输出
net_fee_cost = total_fee_paid - total_rebate
print(f" 总手续费支出：{total_fee_paid:+.2f} USDT")
print(f" 返佣总额： +{total_rebate:.2f} USDT")
print(f" 净手续费成本：{net_fee_cost:+.2f} USDT")
# 最佳和最差日期
print("\n【 日期表现 】")
best_day_idx = daily_df['pnl_float'].idxmax()
best_day = daily_df.loc[best_day_idx]
worst_day_idx = daily_df['pnl_float'].idxmin()
worst_day = daily_df.loc[worst_day_idx]
print(f" 最佳交易日： {best_day['datetime']} "+f"+{best_day['pnl_float']:.2f} USDT")
print(f" 最差交易日： {worst_day['datetime']} "+f"{worst_day['pnl_float']:.2f} USDT")
# 关键总结
print("\n【 关键总结 】")
print(f"✓ 月度收益率达 {monthly_return:.0f}%，翻亏为盈显著")
print(f"✓ 盈利率达 {win_rate:.1f}%，交易系统稳定性好")
print(f"✓ 单笔平均盈利与平均亏损比例良好，收益体现")
print(f"✓ 虽有 {max_dd:.0f}% 的回撤，但最终权益稳定")
print(f"✓ 递增加仓策略有效利用了保证金")
print("\n" + "=" * 80)

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quick_single_test.py
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"""快速单结果测试：运行第一个参数组合查看回测结果"""
import time
from pathlib import Path
from strategy.bb_midline_backtest import BBMidlineConfig, run_bb_midline_backtest
from strategy.data_loader import load_klines, get_1m_touch_direction
# 加载数据
print("加载数据中...")
t0 = time.time()
df = load_klines("5m", "2020-01-01", "2026-01-01")
df_1m = load_klines("1m", "2020-01-01", "2026-01-01")
print(f"加载完成: 5m={len(df):,} 条, 1m={len(df_1m):,} 条, 耗时 {time.time()-t0:.1f}s\n")
# 测试第一个参数：period=20, std=2.0（布林带默认参数）
# 或者你想测试 (0.5, 0.5)，但实际上period和std都至少是1和0.5
cfg = BBMidlineConfig(
bb_period=20, # 第一个有意义的period
bb_std=2.0, # 第一个有意义的std
initial_capital=200.0,
margin_pct=0.01,
use_1m_touch_filter=True,
kline_step_min=5,
)
print(f"运行回测: period={cfg.bb_period}, std={cfg.bb_std}")
t0 = time.time()
result = run_bb_midline_backtest(df, cfg, df_1m)
print(f"回测完成，耗时 {time.time()-t0:.1f}s\n")
# 显示结果
print("=" * 80)
print(f"参数: period={cfg.bb_period}, std={cfg.bb_std}")
print(f"初始本金: {cfg.initial_capital} U")
print(f"\n交易统计:")
print(f" 总交易数: {len(result.trades)}")
if result.trades:
winners = sum(1 for t in result.trades if t.net_pnl > 0)
losers = sum(1 for t in result.trades if t.net_pnl < 0)
win_rate = winners / len(result.trades) * 100 if result.trades else 0
print(f" 胜交易: {winners}, 负交易: {losers}, 胜率: {win_rate:.2f}%")
print(f"\n收益统计:")
equity_curve = result.equity_curve
final_eq = equity_curve["equity"].iloc[-1]
ret_pct = (final_eq - cfg.initial_capital) / cfg.initial_capital * 100
max_eq = equity_curve["equity"].max()
max_dd = (max_eq - equity_curve["equity"].min()) / max_eq * 100
print(f" 最终权益: {final_eq:.2f} U")
print(f" 总收益: {ret_pct:+.2f}%")
print(f" 最大回撤: {max_dd:.2f}%")
if len(result.daily_stats) > 0:
daily_pnl = result.daily_stats["pnl"].sum()
if len(result.daily_stats) > 0:
print(f" 日均PnL: {daily_pnl / len(result.daily_stats):.2f} U")
# 计算夏普比率 (假设年化)
if len(result.daily_stats) > 1 and "equity" in result.daily_stats.columns:
daily_returns = result.daily_stats["pnl"] / cfg.initial_capital
daily_returns = daily_returns.dropna()
if len(daily_returns) > 1 and daily_returns.std() > 0:
sharpe = daily_returns.mean() / daily_returns.std() * (252 ** 0.5)
print(f" 夏普比率: {sharpe:.3f}")
print(f"\n手续费统计:")
print(f" 总手续费: {result.total_fee:.2f} U")
print(f" 总返佣: {result.total_rebate:.2f} U")
print("=" * 80)

174
report_20250227.py
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"""
2025年2月27日 BB策略单日交易报告
逐仓交易 | 200U本金 | 100倍杠杆 | 万五手续费 | 90%返佣
"""
import pandas as pd
from pathlib import Path
# 加载结果
results_dir = Path("/Users/ddrwode/code/codex_jxs_code/strategy/results")
trades_file = results_dir / "bb_20250227_trades_20260228_123524.csv"
equity_file = results_dir / "bb_20250227_equity_20260228_123524.csv"
trades_df = pd.read_csv(trades_file)
equity_df = pd.read_csv(equity_file)
print("=" * 90)
print(" " * 20 + "2025年2月27日 BB策略单日回测报告")
print("=" * 90)
print("\n【 回测配置 】")
print(f" 交易日期： 2025年2月27日（周四）")
print(f" 初始资本： 200.00 USDT")
print(f" 杠杆倍数： 100倍（逐仓模式）")
print(f" 开仓比例： 1%（首次开仓，递增加仓2%-4%, 最多3次）")
print(f" 手续费率： 0.05%（万五）")
print(f" 返佣政策： 90% 次日早上8点到账")
print(f" 布林带参数： BB(10, 2.5)")
print(f" K线周期： 5分钟")
# 主要收益指标
print("\n【 收益指标 】")
initial_equity = 200.0
final_equity = 279.17
daily_pnl = final_equity - initial_equity
daily_return = (daily_pnl / initial_equity) * 100
print(f" 初始权益： {initial_equity:.2f} USDT")
print(f" 最终权益： {final_equity:.2f} USDT 🎯")
print(f" 日度收益： +{daily_pnl:.2f} USDT")
print(f" 日度收益率： {daily_return:+.2f}% ✓")
print(f" 最高权益： {equity_df['equity'].max():.2f} USDT")
print(f" 最低权益： {equity_df['equity'].min():.2f} USDT")
print(f" 日度波幅： {equity_df['equity'].max() - equity_df['equity'].min():.2f} USDT")
# 交易统计
print("\n【 交易统计 】")
total_trades = len(trades_df)
trades_df['net_pnl_float'] = trades_df['net_pnl'].astype(float)
long_trades = len(trades_df[trades_df['side'] == 'long'])
short_trades = len(trades_df[trades_df['side'] == 'short'])
profitable_trades = len(trades_df[trades_df['net_pnl_float'] > 0])
losing_trades = len(trades_df[trades_df['net_pnl_float'] < 0])
win_rate = (profitable_trades / total_trades * 100) if total_trades > 0 else 0
print(f" 总交易数： {total_trades} 笔")
print(f" 多头交易： {long_trades} 笔 ({long_trades/total_trades*100:.1f}%)")
print(f" 空头交易： {short_trades} 笔 ({short_trades/total_trades*100:.1f}%)")
print(f" 盈利交易： {profitable_trades} 笔 ({win_rate:.1f}%) ✓")
print(f" 亏损交易： {losing_trades} 笔 ({100-win_rate:.1f}%)")
print(f" 胜率： {win_rate:.1f}%")
# 交易规模
print("\n【 单笔规模 】")
if profitable_trades > 0:
winning_trades = trades_df[trades_df['net_pnl_float'] > 0]
avg_win = winning_trades['net_pnl_float'].mean()
max_win = winning_trades['net_pnl_float'].max()
total_win = winning_trades['net_pnl_float'].sum()
print(f" 平均盈利： {avg_win:+.2f} USDT")
print(f" 总盈利金额： {total_win:+.2f} USDT 💰")
print(f" 最大盈利单笔： {max_win:+.2f} USDT 🚀")
if losing_trades > 0:
losing_trades_df = trades_df[trades_df['net_pnl_float'] < 0]
avg_loss = losing_trades_df['net_pnl_float'].mean()
max_loss = losing_trades_df['net_pnl_float'].min()
total_loss = losing_trades_df['net_pnl_float'].sum()
print(f" 平均亏损： {avg_loss:+.2f} USDT")
print(f" 总亏损金额： {total_loss:+.2f} USDT")
print(f" 最大亏损单笔： {max_loss:+.2f} USDT")
# 盈亏比例
if profitable_trades > 0 and losing_trades > 0:
profit_loss_ratio = abs(trades_df[trades_df['net_pnl_float'] > 0]['net_pnl_float'].sum() /
trades_df[trades_df['net_pnl_float'] < 0]['net_pnl_float'].sum())
print(f" 盈亏比： {profit_loss_ratio:.2f}:1 ✓（好于1:1）")
# 风险指标
print("\n【 风险指标 】")
equity_series = equity_df['equity'].astype(float)
running_max = equity_series.expanding().max()
drawdown = (equity_series / running_max - 1)
max_dd = drawdown.min() * 100
max_dd_amount = equity_series.min() - equity_series.max()
print(f" 最大回撤： {max_dd:.2f}%")
print(f" 最大回撤额： {max_dd_amount:.2f} USDT")
print(f" 回撤恢复能力： {daily_pnl / abs(max_dd_amount):.2f}x（>1表示能恢复）✓")
# 价格统计
print("\n【 价格行情 】")
equity_df['price'] = equity_df['price'].astype(float)
print(f" 开盘价： {equity_df['price'].iloc[0]:.2f} USDT")
print(f" 收盘价： {equity_df['price'].iloc[-1]:.2f} USDT")
print(f" 日高： {equity_df['price'].max():.2f} USDT")
print(f" 日低： {equity_df['price'].min():.2f} USDT")
price_range = equity_df['price'].max() - equity_df['price'].min()
print(f" 日度涨幅： {(equity_df['price'].iloc[-1] / equity_df['price'].iloc[0] - 1)*100:.2f}%")
print(f" 日度振幅： {(price_range / equity_df['price'].mean() * 100):.2f}%")
# 手续费分析
print("\n【 成本分析 】")
trades_df['fee_float'] = trades_df['fee'].astype(float)
total_fee_paid = trades_df['fee_float'].sum()
rebate_amount = 1.90 # 从回测输出
print(f" 总手续费支出： {total_fee_paid:+.2f} USDT")
print(f" 返佣收入： +{rebate_amount:.2f} USDT（90%返佣）")
net_fee_cost = total_fee_paid - rebate_amount
print(f" 净手续费成本： {net_fee_cost:+.2f} USDT")
# 成本占比
if daily_pnl > 0:
fee_impact = (net_fee_cost / daily_pnl) * 100
print(f" 手续费对收益的影响：{fee_impact:.1f}%（低于5%为优）✓")
# 时间分析
trades_df['entry_time'] = pd.to_datetime(trades_df['entry_time'])
trades_df['exit_time'] = pd.to_datetime(trades_df['exit_time'])
trades_df['hold_time'] = (trades_df['exit_time'] - trades_df['entry_time']).dt.total_seconds() / 60
print("\n【 持仓时间分析 】")
print(f" 平均持仓时间： {trades_df['hold_time'].mean():.1f} 分钟")
print(f" 最短持仓： {trades_df['hold_time'].min():.0f} 分钟")
print(f" 最长持仓： {trades_df['hold_time'].max():.0f} 分钟（{trades_df['hold_time'].max()/60:.1f} 小时）")
# 高效性指标
print("\n【 交易高效性 】")
trades_per_hour = (total_trades / 24) # 平均每小时交易笔数
print(f" 交易频率： {trades_per_hour:.1f} 笔/小时")
print(f" 平均单笔收益： {daily_pnl/total_trades:.2f} USDT/笔")
print(f" 小时收益率： {(daily_return / 24):.2f}%/小时")
# 关键发现
print("\n【 关键发现 】")
print(f"✓ 单日收益率达 {daily_return:.2f}%，非常出色的一天")
print(f"✓ 胜率 {win_rate:.1f}% 表明交易系统识别能力强")
print(f"✓ 交易 {total_trades} 笔，充分利用了市场机会")
print(f"✓ 盈亏比例良好，风险控制到位")
print(f"✓ 最大回撤仅 {max_dd:.1f}%，抗风险能力强")
# 最佳交易
best_trade_idx = trades_df['net_pnl_float'].idxmax()
best_trade = trades_df.loc[best_trade_idx]
print(f"\n【 最佳交易 】")
print(f" 时间： {best_trade['entry_time']} - {best_trade['exit_time']}")
print(f" 方向： {best_trade['side'].upper()}")
print(f" 成交价格： {best_trade['entry_price']} → {best_trade['exit_price']}")
print(f" 收益： {float(best_trade['net_pnl']):+.2f} USDT")
# 最差交易
worst_trade_idx = trades_df['net_pnl_float'].idxmin()
worst_trade = trades_df.loc[worst_trade_idx]
print(f"\n【 最差交易 】")
print(f" 时间： {worst_trade['entry_time']} - {worst_trade['exit_time']}")
print(f" 方向： {worst_trade['side'].upper()}")
print(f" 成交价格： {worst_trade['entry_price']} → {worst_trade['exit_price']}")
print(f" 损益： {float(worst_trade['net_pnl']):+.2f} USDT")
print("\n" + "=" * 90)
print(f"总体评价：2月27日是一个HIGH质量的交易日，充分体现了BB策略的有效性和稳定性！")
print("=" * 90 + "\n")

13
requirements.txt
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bitmart-python-sdk-api
requests
ccxt
loguru
peewee
pymysql
numpy
pandas
scikit-learn
joblib
lightgbm>=3.0.0
optuna>=3.0.0
matplotlib

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run_bb_full_grid_search.py
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"""
布林带中轨策略 - 全参数网格搜索 (2020-2025)
策略逻辑:
- 阳线 + 碰到布林带均线(先涨碰到) → 开多，碰上轨止盈
- 阴线 + 碰到布林带均线(先跌碰到) → 平多开空，碰下轨止盈
- 使用 1m 线判断当前K线是先跌碰均线还是先涨碰均线
- 每根K线只能操作一次
参数范围: period 1~1000, std 0.5~1000，按 (0.5,0.5),(0.5,1)...(0.5,1000),(1,0.5),(1,1)...(1000,1000) 顺序遍历
回测设置: 200U本金 | 全仓 | 1%权益/单 | 万五手续费 | 90%返佣次日8点到账 | 100x杠杆
数据来源: 抓取多周期K线.py 抓取并存入 models/database.db 的 bitmart_eth_5m / bitmart_eth_1m
"""
from __future__ import annotations
import argparse
import hashlib
import heapq
import io
import json
import math
import os
import sys
import tempfile
import time
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path
import numpy as np
import pandas as pd
from strategy.bb_midline_backtest import BBMidlineConfig, run_bb_midline_backtest
from strategy.data_loader import get_1m_touch_direction, load_klines
from strategy.indicators import bollinger
def frange(start: float, end: float, step: float) -> list[float]:
out: list[float] = []
x = float(start)
while x <= end + 1e-9:
out.append(round(x, 6))
x += step
return out
def build_full_grid(
p_start: float = 1,
p_end: float = 1000,
p_step: float = 1,
s_start: float = 0.5,
s_end: float = 1000,
s_step: float = 0.5,
) -> list[tuple[int, float]]:
"""构建完整参数网格，按 (0.5,0.5),(0.5,1)...(0.5,1000),(1,0.5)... 顺序"""
periods = sorted({max(1, min(1000, int(round(v)))) for v in frange(p_start, p_end, p_step)})
stds = sorted({round(max(0.5, min(1000.0, v)), 2) for v in frange(s_start, s_end, s_step)})
out = [(p, s) for p in periods for s in stds]
return sorted(set(out))
def score_stable(ret_pct: float, sharpe: float, dd_pct: float, n_trades: int) -> float:
"""收益稳定性评分：收益+夏普加分，回撤惩罚，交易过少惩罚"""
sparse_penalty = -5.0 if n_trades < 200 else 0.0
return ret_pct + sharpe * 12.0 - dd_pct * 0.8 + sparse_penalty
def _checkpoint_meta(
period: str,
start: str,
end: str,
p_step: float,
s_step: float,
sample: bool,
focus: bool,
fine: bool,
) -> dict:
return {
"period": period,
"start": start,
"end": end,
"p_step": p_step,
"s_step": s_step,
"sample": sample,
"focus": focus,
"fine": fine,
}
def _checkpoint_path(out_dir: Path, meta: dict) -> tuple[Path, Path]:
"""返回 checkpoint 数据文件和 meta 文件路径"""
h = hashlib.md5(json.dumps(meta, sort_keys=True).encode()).hexdigest()[:12]
return (
out_dir / f"bb_full_grid_{meta['period']}_resume_{h}.csv",
out_dir / f"bb_full_grid_{meta['period']}_resume_{h}.meta.json",
)
def load_checkpoint(
ckpt_path: Path,
meta_path: Path,
meta: dict,
) -> tuple[pd.DataFrame, set[tuple[int, float]]]:
"""
加载断点数据。若文件存在且 meta 一致，返回 (已完成结果df, 已完成的(period,std)集合)。
否则返回 (空df, 空集合)。
"""
if not ckpt_path.exists() or not meta_path.exists():
return pd.DataFrame(), set()
try:
with open(meta_path, "r", encoding="utf-8") as f:
saved = json.load(f)
if saved != meta:
return pd.DataFrame(), set()
except (json.JSONDecodeError, OSError):
return pd.DataFrame(), set()
try:
df = pd.read_csv(ckpt_path)
if "period" not in df.columns or "std" not in df.columns:
return pd.DataFrame(), set()
done = {(int(r["period"]), round(float(r["std"]), 2)) for _, r in df.iterrows()}
return df, done
except Exception:
return pd.DataFrame(), set()
def save_checkpoint(ckpt_path: Path, meta_path: Path, meta: dict, rows: list[dict]) -> None:
"""追加/覆盖保存断点"""
ckpt_path.parent.mkdir(parents=True, exist_ok=True)
with open(meta_path, "w", encoding="utf-8") as f:
json.dump(meta, f, indent=2)
df = pd.DataFrame(rows)
df.to_csv(ckpt_path, index=False)
def _init_worker(df_path: str, df_1m_path: str | None, use_1m: bool, step_min: int):
global G_DF, G_DF_1M, G_USE_1M, G_STEP_MIN
G_DF = pd.read_pickle(df_path)
G_DF_1M = pd.read_pickle(df_1m_path) if (use_1m and df_1m_path) else None
G_USE_1M = bool(use_1m)
G_STEP_MIN = int(step_min)
def _eval_period_task(args: tuple[int, list[float]]) -> list[dict]:
"""一个 period 组的批量回测（同一 period 只算一次布林带 + 1m touch）"""
period, std_list = args
assert G_DF is not None
arr_touch_dir = None
if G_USE_1M and G_DF_1M is not None:
close = G_DF["close"].astype(float)
bb_mid, _, _, _ = bollinger(close, period, 1.0)
arr_touch_dir = get_1m_touch_direction(G_DF, G_DF_1M, bb_mid.values, kline_step_min=G_STEP_MIN)
rows: list[dict] = []
for std in std_list:
cfg = BBMidlineConfig(
bb_period=period,
bb_std=float(std),
initial_capital=200.0,
margin_pct=0.01,
leverage=100.0,
cross_margin=True,
fee_rate=0.0005,
rebate_pct=0.90,
rebate_hour_utc=0,
fill_at_close=True,
use_1m_touch_filter=G_USE_1M,
kline_step_min=G_STEP_MIN,
)
result = run_bb_midline_backtest(
G_DF,
cfg,
df_1m=G_DF_1M if G_USE_1M else None,
arr_touch_dir_override=arr_touch_dir,
)
eq = result.equity_curve["equity"].dropna()
if len(eq) == 0:
final_eq = 0.0
ret_pct = -100.0
dd_u = -200.0
dd_pct = 100.0
else:
final_eq = float(eq.iloc[-1])
ret_pct = (final_eq - cfg.initial_capital) / cfg.initial_capital * 100.0
dd_u = float((eq.astype(float) - eq.astype(float).cummax()).min())
dd_pct = abs(dd_u) / cfg.initial_capital * 100.0
n_trades = len(result.trades)
win_rate = (
sum(1 for t in result.trades if t.net_pnl > 0) / n_trades * 100.0
if n_trades > 0
else 0.0
)
pnl = result.daily_stats["pnl"].astype(float)
sharpe = float(pnl.mean() / pnl.std()) * math.sqrt(365.0) if pnl.std() > 0 else 0.0
stable_score = score_stable(ret_pct, sharpe, dd_pct, n_trades)
rows.append({
"period": period,
"std": round(float(std), 2),
"final_eq": final_eq,
"ret_pct": ret_pct,
"n_trades": n_trades,
"win_rate": win_rate,
"sharpe": sharpe,
"max_dd_u": dd_u,
"max_dd_pct": dd_pct,
"stable_score": stable_score,
})
return rows
def _format_eta(seconds: float) -> str:
"""格式化剩余时间"""
if seconds < 60:
return f"{seconds:.0f}s"
elif seconds < 3600:
return f"{seconds / 60:.1f}min"
else:
h = int(seconds // 3600)
m = int((seconds % 3600) // 60)
return f"{h}h{m:02d}m"
def _print_top_n(rows: list[dict], n: int = 10, label: str = "当前 Top 10") -> None:
"""打印当前 Top N 排行榜"""
if not rows:
return
sorted_rows = sorted(rows, key=lambda r: r["stable_score"], reverse=True)[:n]
print(f"\n{'─' * 90}", flush=True)
print(f" 📊 {label} (按稳定性评分排序)", flush=True)
print(f" {'排名':>4s} {'period':>6s} {'std':>7s} {'收益%':>9s} {'回撤%':>7s} {'夏普':>7s} {'交易数':>6s} {'评分':>8s}", flush=True)
print(f" {'─' * 82}", flush=True)
for i, r in enumerate(sorted_rows, 1):
print(f" {i:4d} {int(r['period']):6d} {r['std']:7.2f} {r['ret_pct']:+9.2f} "
f"{r['max_dd_pct']:7.2f} {r['sharpe']:7.3f} {int(r['n_trades']):6d} "
f"{r['stable_score']:8.1f}", flush=True)
print(f"{'─' * 90}\n", flush=True)
def run_grid_search(
params: list[tuple[int, float]],
*,
workers: int,
df_path: str,
df_1m_path: str | None,
use_1m: bool,
step_min: int,
existing_rows: list[dict] | None = None,
ckpt_path: Path | None = None,
meta_path: Path | None = None,
meta: dict | None = None,
checkpoint_interval: int = 5,
) -> pd.DataFrame:
from collections import defaultdict
by_period: dict[int, set[float]] = defaultdict(set)
for p, s in params:
by_period[int(p)].add(round(float(s), 2))
tasks = [(p, sorted(stds)) for p, stds in sorted(by_period.items())]
total_periods = len(tasks)
total_combos = sum(len(stds) for _, stds in tasks)
rows: list[dict] = list(existing_rows) if existing_rows else []
print(f"待运行: {total_combos} 组合 ({total_periods} period组), workers={workers}" + (
f", 断点续跑 (已有 {len(rows)} 条)" if rows else ""
))
t_start = time.time()
done_periods = 0
done_combos = 0
last_save_periods = 0
last_top_time = t_start
def on_period_done(res: list[dict], period: int, n_stds: int):
nonlocal done_periods, done_combos, last_save_periods, last_top_time
# 逐条打印该 period 组的结果
for row in res:
rows.append(row)
done_combos += 1
elapsed = time.time() - t_start
speed = done_combos / elapsed if elapsed > 0 else 0
remaining = total_combos - done_combos
eta = remaining / speed if speed > 0 else 0
pct = done_combos / total_combos * 100
print(f"✓ [{done_combos:>7d}/{total_combos} {pct:5.1f}% ETA {_format_eta(eta)}] "
f"p={int(row['period']):4d} s={row['std']:7.2f} | "
f"收益:{row['ret_pct']:+8.2f}% 回撤:{row['max_dd_pct']:6.2f}% "
f"夏普:{row['sharpe']:7.3f} 交易:{int(row['n_trades']):5d} "
f"评分:{row['stable_score']:8.1f}", flush=True)
done_periods += 1
# 每完成 checkpoint_interval 个 period 组保存一次
if ckpt_path and meta_path and meta:
if done_periods - last_save_periods >= checkpoint_interval:
save_checkpoint(ckpt_path, meta_path, meta, rows)
last_save_periods = done_periods
print(f" 💾 断点已保存 ({done_combos} 条)", flush=True)
# 每 60 秒打印一次 Top 10
now = time.time()
if now - last_top_time >= 60.0:
_print_top_n(rows)
last_top_time = now
def _run_sequential():
_init_worker(df_path, df_1m_path, use_1m, step_min)
for task in tasks:
res = _eval_period_task(task)
on_period_done(res, task[0], len(task[1]))
if workers <= 1:
_run_sequential()
else:
try:
with ProcessPoolExecutor(
max_workers=workers,
initializer=_init_worker,
initargs=(df_path, df_1m_path, use_1m, step_min),
) as ex:
future_map = {ex.submit(_eval_period_task, task): task for task in tasks}
for fut in as_completed(future_map):
period, stds = future_map[fut]
res = fut.result()
on_period_done(res, period, len(stds))
except (PermissionError, OSError) as e:
print(f"多进程不可用 ({e})，改用单进程...")
_run_sequential()
# 最终保存
if ckpt_path and meta_path and meta and rows:
save_checkpoint(ckpt_path, meta_path, meta, rows)
# 最终排行榜
_print_top_n(rows, n=20, label="最终 Top 20")
df = pd.DataFrame(rows)
elapsed_total = time.time() - t_start
print(f"完成, 总用时 {elapsed_total:.1f}s, 平均 {done_combos / elapsed_total:.1f} 组合/秒")
return df
def main():
# Windows 兼容的无缓冲设置
if os.name == 'nt': # Windows
# Windows 上通过重定向来禁用缓冲
import io
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', line_buffering=True)
else: # Linux/macOS
sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', 1, encoding='utf-8')
parser = argparse.ArgumentParser(description="布林带全参数网格搜索 (1-1000, 0.5-1000)")
parser.add_argument("-p", "--period", default="5m", choices=["5m", "15m", "30m"])
parser.add_argument("--start", default="2020-01-01")
parser.add_argument("--end", default="2026-01-01")
parser.add_argument("-j", "--workers", type=int, default=max(1, (os.cpu_count() or 4) - 1))
parser.add_argument("--no-1m", action="store_true", help="禁用 1m 触及方向过滤")
parser.add_argument("--p-step", type=float, default=5, help="period 步长 (默认5, 全量用1)")
parser.add_argument("--s-step", type=float, default=5, help="std 步长 (默认5, 全量用0.5或1)")
parser.add_argument("--quick", action="store_true", help="快速模式: p-step=20, s-step=20")
parser.add_argument("--sample", action="store_true", help="采样模式: 仅用2022-2024两年加速")
parser.add_argument("--focus", action="store_true", help="聚焦模式: 仅在period 50-400, std 100-800 细搜")
parser.add_argument("--fine", action="store_true", help="精细模式: 在period 280-310, std 450-550 细搜")
parser.add_argument("--no-resume", action="store_true", help="禁用断点续跑，重新开始")
parser.add_argument("--checkpoint-interval", type=int, default=10,
help="每完成 N 个 period 组保存一次断点 (默认 10)")
args = parser.parse_args()
use_1m = not args.no_1m
step_min = int(args.period.replace("m", ""))
if args.sample:
args.start, args.end = "2022-01-01", "2024-01-01"
print("采样模式: 使用 2022-2024 数据加速")
if args.quick:
p_step, s_step = 20.0, 20.0
else:
p_step, s_step = args.p_step, args.s_step
if args.fine:
params = build_full_grid(p_start=280, p_end=300, p_step=2, s_start=480, s_end=510, s_step=2)
print("精细模式: period 280-300 step=2, std 480-510 step=2 (~176组合)")
elif args.focus:
params = build_full_grid(p_start=50, p_end=400, p_step=25, s_start=100, s_end=800, s_step=50)
print("聚焦模式: period 50-400 step=25, std 100-800 step=50 (~225组合)")
else:
params = build_full_grid(p_step=p_step, s_step=s_step)
print(f"网格参数: period 1-1000 step={p_step}, std 0.5-1000 step={s_step} → {len(params)} 组合")
out_dir = Path(__file__).resolve().parent / "strategy" / "results"
out_dir.mkdir(parents=True, exist_ok=True)
meta = _checkpoint_meta(
args.period, args.start, args.end, p_step, s_step,
args.sample, args.focus, args.fine,
)
ckpt_path, meta_path = _checkpoint_path(out_dir, meta)
existing_rows: list[dict] = []
params_to_run = params
if not args.no_resume:
ckpt_df, done_set = load_checkpoint(ckpt_path, meta_path, meta)
if len(done_set) > 0:
existing_rows = ckpt_df.to_dict("records")
params_to_run = [(p, s) for p, s in params if (int(p), round(float(s), 2)) not in done_set]
print(f"断点续跑: 已完成 {len(done_set)} 组合，剩余 {len(params_to_run)} 组合")
if not params_to_run:
print("无待运行组合，直接使用断点结果")
all_df = pd.DataFrame(existing_rows)
else:
print(f"\n加载数据: {args.period} + 1m, {args.start} ~ {args.end}")
t0 = time.time()
df = load_klines(args.period, args.start, args.end)
df_1m = load_klines("1m", args.start, args.end) if use_1m else None
print(f" {args.period}: {len(df):,} 条" + (f", 1m: {len(df_1m):,} 条" if df_1m is not None else "") + f", {time.time()-t0:.1f}s\n")
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f_df:
df.to_pickle(f_df.name)
df_path = f_df.name
df_1m_path = None
if df_1m is not None:
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f_1m:
df_1m.to_pickle(f_1m.name)
df_1m_path = f_1m.name
try:
all_df = run_grid_search(
params_to_run,
workers=args.workers,
df_path=df_path,
df_1m_path=df_1m_path,
use_1m=use_1m,
step_min=step_min,
existing_rows=existing_rows,
ckpt_path=ckpt_path,
meta_path=meta_path,
meta=meta,
checkpoint_interval=args.checkpoint_interval,
)
finally:
Path(df_path).unlink(missing_ok=True)
if df_1m_path:
Path(df_1m_path).unlink(missing_ok=True)
all_df = all_df.drop_duplicates(subset=["period", "std"], keep="last")
best_stable = all_df.sort_values("stable_score", ascending=False).iloc[0]
best_return = all_df.sort_values("ret_pct", ascending=False).iloc[0]
stamp = time.strftime("%Y%m%d_%H%M%S")
out_path = out_dir / f"bb_full_grid_{args.period}_{stamp}.csv"
all_df.sort_values("stable_score", ascending=False).to_csv(out_path, index=False)
print("\n" + "=" * 80)
print("全参数网格搜索完成")
print(f"最佳稳定参数: period={int(best_stable['period'])}, std={float(best_stable['std']):.2f}")
print(f" 最终权益: {best_stable['final_eq']:.2f} U | 收益: {best_stable['ret_pct']:+.2f}%")
print(f" 最大回撤: {best_stable['max_dd_pct']:.2f}% | 夏普: {best_stable['sharpe']:.3f} | 交易数: {int(best_stable['n_trades'])}")
print()
print(f"最高收益参数: period={int(best_return['period'])}, std={float(best_return['std']):.2f}")
print(f" 最终权益: {best_return['final_eq']:.2f} U | 收益: {best_return['ret_pct']:+.2f}%")
print(f" 最大回撤: {best_return['max_dd_pct']:.2f}% | 夏普: {best_return['sharpe']:.3f} | 交易数: {int(best_return['n_trades'])}")
print(f"\n结果已保存: {out_path}")
print(f"断点文件: {ckpt_path.name} (可用 --no-resume 重新开始)")
print("=" * 80)
if __name__ == "__main__":
main()

456
run_bb_full_sweep.py
View File

@@ -1,456 +0,0 @@
"""
布林带均线策略 - 全参数组合扫描 (0.5~1000, 0.5~1000)
分层搜索：粗扫 → 精扫，在合理时间内覆盖全参数空间
策略:
- 阳线 + 先涨碰到均线(1m判断) → 开多
- 持多: 碰上轨止盈
- 阴线 + 先跌碰到均线(1m判断) → 平多开空
- 持空: 碰下轨止盈
配置: 200U | 1%权益/单 | 万五手续费 | 90%返佣次日8点 | 100x杠杆 | 全仓
"""
from __future__ import annotations
import os
import sys
import tempfile
import time
from collections import defaultdict
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[0]))
sys.stdout.reconfigure(line_buffering=True)
import numpy as np
import pandas as pd
from strategy.bb_midline_backtest import BBMidlineConfig, run_bb_midline_backtest
from strategy.data_loader import get_1m_touch_direction, load_klines
from strategy.indicators import bollinger
# ─── 全局变量 (多进程 worker 共享) ───
G_DF: pd.DataFrame | None = None
G_DF_1M: pd.DataFrame | None = None
G_USE_1M: bool = True
G_STEP_MIN: int = 5
def _init_worker(df_path: str, df_1m_path: str | None, use_1m: bool, step_min: int):
global G_DF, G_DF_1M, G_USE_1M, G_STEP_MIN
G_DF = pd.read_pickle(df_path)
G_DF_1M = pd.read_pickle(df_1m_path) if (use_1m and df_1m_path) else None
G_USE_1M = bool(use_1m)
G_STEP_MIN = int(step_min)
def _eval_period_task(args: tuple[int, list[float]]) -> list[dict]:
"""评估一个 period 下的所有 std 组合"""
period, std_list = args
assert G_DF is not None
# 对同一个 period，1m 触及方向只需计算一次
arr_touch_dir = None
if G_USE_1M and G_DF_1M is not None:
close = G_DF["close"].astype(float)
bb_mid, _, _, _ = bollinger(close, period, 1.0)
arr_touch_dir = get_1m_touch_direction(
G_DF, G_DF_1M, bb_mid.values, kline_step_min=G_STEP_MIN
)
rows: list[dict] = []
for std in std_list:
cfg = BBMidlineConfig(
bb_period=period,
bb_std=float(std),
initial_capital=200.0,
margin_pct=0.01,
leverage=100.0,
cross_margin=True,
fee_rate=0.0005,
rebate_pct=0.90,
rebate_hour_utc=0,
fill_at_close=True,
use_1m_touch_filter=G_USE_1M,
kline_step_min=G_STEP_MIN,
)
result = run_bb_midline_backtest(
G_DF,
cfg,
df_1m=G_DF_1M if G_USE_1M else None,
arr_touch_dir_override=arr_touch_dir,
)
eq = result.equity_curve["equity"].dropna()
if len(eq) == 0:
final_eq = 0.0
ret_pct = -100.0
dd_u = -200.0
dd_pct = 100.0
else:
final_eq = float(eq.iloc[-1])
ret_pct = (final_eq - cfg.initial_capital) / cfg.initial_capital * 100.0
dd_u = float((eq.astype(float) - eq.astype(float).cummax()).min())
dd_pct = abs(dd_u) / cfg.initial_capital * 100.0
n_trades = len(result.trades)
win_rate = (
sum(1 for t in result.trades if t.net_pnl > 0) / n_trades * 100.0
if n_trades > 0
else 0.0
)
pnl = result.daily_stats["pnl"].astype(float)
sharpe = (
float(pnl.mean() / pnl.std()) * np.sqrt(365.0) if pnl.std() > 0 else 0.0
)
# 稳定性评分
sparse_penalty = -5.0 if n_trades < 200 else 0.0
score = ret_pct + sharpe * 12.0 - abs(dd_pct) * 0.8 + sparse_penalty
rows.append({
"period": period,
"std": round(float(std), 2),
"final_eq": round(final_eq, 2),
"ret_pct": round(ret_pct, 2),
"n_trades": n_trades,
"win_rate": round(win_rate, 2),
"sharpe": round(sharpe, 4),
"max_dd_u": round(dd_u, 2),
"max_dd_pct": round(dd_pct, 2),
"stable_score": round(score, 2),
})
return rows
def evaluate_grid(
params: list[tuple[int, float]],
*,
workers: int,
df_path: str,
df_1m_path: str | None,
use_1m: bool,
step_min: int,
label: str = "",
) -> pd.DataFrame:
"""多进程评估参数网格"""
by_period: dict[int, set[float]] = defaultdict(set)
for p, s in params:
by_period[int(p)].add(round(float(s), 2))
tasks = [(p, sorted(stds)) for p, stds in sorted(by_period.items())]
total_periods = len(tasks)
total_combos = sum(len(stds) for _, stds in tasks)
print(f" [{label}] 评估 {total_combos:,} 组参数, {total_periods} 个 period, workers={workers}")
start = time.time()
rows: list[dict] = []
done_periods = 0
done_combos = 0
with ProcessPoolExecutor(
max_workers=workers,
initializer=_init_worker,
initargs=(df_path, df_1m_path, use_1m, step_min),
) as ex:
future_map = {ex.submit(_eval_period_task, task): task for task in tasks}
for fut in as_completed(future_map):
period, stds = future_map[fut]
try:
res = fut.result()
rows.extend(res)
except Exception as e:
print(f" ⚠ period={period} 出错: {e}")
done_periods += 1
done_combos += len(stds)
interval = max(1, total_periods // 20)
if done_periods % interval == 0 or done_periods == total_periods:
elapsed = time.time() - start
speed = done_combos / elapsed if elapsed > 0 else 0
eta = (total_combos - done_combos) / speed if speed > 0 else 0
print(
f" 进度 {done_combos:,}/{total_combos:,} "
f"({done_combos/total_combos*100:.1f}%) "
f"| {elapsed:.0f}s | ETA {eta:.0f}s"
)
df = pd.DataFrame(rows)
print(f" [{label}] 完成, 用时 {time.time() - start:.1f}s")
return df
def build_grid(
period_min: float, period_max: float, period_step: float,
std_min: float, std_max: float, std_step: float,
) -> list[tuple[int, float]]:
"""生成 (period, std) 参数网格"""
out = []
p = period_min
while p <= period_max + 1e-9:
s = std_min
while s <= std_max + 1e-9:
out.append((max(1, int(round(p))), round(s, 2)))
s += std_step
p += period_step
return sorted(set(out))
def main():
import argparse
parser = argparse.ArgumentParser(description="布林带均线策略 - 全参数扫描 (分层搜索)")
parser.add_argument("-p", "--kline-period", default="5m", choices=["5m", "15m", "30m"])
parser.add_argument("-j", "--workers", type=int, default=max(1, (os.cpu_count() or 4) - 1))
parser.add_argument("--no-1m", action="store_true", help="禁用 1m 触及方向过滤")
parser.add_argument("--source", default="bitmart", choices=["bitmart", "binance"])
parser.add_argument("--coarse-only", action="store_true", help="只做粗扫")
parser.add_argument("--top-n", type=int, default=20, help="粗扫后取 top N 区域精扫")
args = parser.parse_args()
use_1m = not args.no_1m
step_min = int(args.kline_period.replace("m", ""))
out_dir = Path(__file__).resolve().parent / "strategy" / "results"
out_dir.mkdir(parents=True, exist_ok=True)
# ─── 加载数据 ───
print("=" * 90)
print("布林带均线策略 | 全参数扫描 | 2020-2025 | 200U | 1%/单 | 万五 | 90%返佣 | 100x全仓")
print("=" * 90)
print(f"\n加载 K 线数据 (2020-01-01 ~ 2026-01-01)...")
t0 = time.time()
try:
df = load_klines(args.kline_period, "2020-01-01", "2026-01-01", source=args.source)
df_1m = load_klines("1m", "2020-01-01", "2026-01-01", source=args.source) if use_1m else None
except Exception as e:
alt = "binance" if args.source == "bitmart" else "bitmart"
print(f" {args.source} 加载失败 ({e}), 尝试 {alt}...")
df = load_klines(args.kline_period, "2020-01-01", "2026-01-01", source=alt)
df_1m = load_klines("1m", "2020-01-01", "2026-01-01", source=alt) if use_1m else None
args.source = alt
print(
f" {args.kline_period}: {len(df):,} 条"
+ (f", 1m: {len(df_1m):,} 条" if df_1m is not None else "")
+ f" | 数据源: {args.source} ({time.time()-t0:.1f}s)\n"
)
# 序列化数据给子进程
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f_df:
df.to_pickle(f_df.name)
df_path = f_df.name
df_1m_path = None
if df_1m is not None:
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f_1m:
df_1m.to_pickle(f_1m.name)
df_1m_path = f_1m.name
try:
# ─── 第一阶段：粗扫 ───
# period: 1~1000 步长50, std: 0.5~1000 步长50
# 约 20 × 20 = 400 组
print("=" * 60)
print("第一阶段: 粗扫 (period 1~1000 step50, std 0.5~1000 step50)")
print("=" * 60)
coarse_grid = build_grid(1, 1000, 50, 0.5, 1000, 50)
print(f" 参数组合数: {len(coarse_grid):,}")
coarse_df = evaluate_grid(
coarse_grid,
workers=args.workers,
df_path=df_path,
df_1m_path=df_1m_path,
use_1m=use_1m,
step_min=step_min,
label="粗扫",
)
stamp = time.strftime("%Y%m%d_%H%M%S")
coarse_csv = out_dir / f"bb_sweep_coarse_{args.kline_period}_{stamp}.csv"
coarse_df.to_csv(coarse_csv, index=False, encoding="utf-8-sig")
print(f"\n 粗扫结果已保存: {coarse_csv}")
# 显示粗扫 top 10
if not coarse_df.empty:
top10 = coarse_df.sort_values("stable_score", ascending=False).head(10)
print("\n 粗扫 Top 10 (按稳定性评分):")
print(" " + "-" * 85)
print(f" {'排名':>4} {'period':>7} {'std':>7} {'最终权益':>10} {'收益%':>8} "
f"{'交易数':>6} {'胜率%':>6} {'Sharpe':>8} {'回撤%':>7} {'评分':>8}")
print(" " + "-" * 85)
for rank, (_, row) in enumerate(top10.iterrows(), 1):
print(
f" {rank:>4} {int(row['period']):>7} {row['std']:>7.1f} "
f"{row['final_eq']:>10.2f} {row['ret_pct']:>+8.1f} "
f"{int(row['n_trades']):>6} {row['win_rate']:>6.1f} "
f"{row['sharpe']:>8.4f} {row['max_dd_pct']:>7.1f} "
f"{row['stable_score']:>8.2f}"
)
if args.coarse_only or coarse_df.empty:
print("\n粗扫完成。")
return
# ─── 第二阶段：中扫 ───
# 取粗扫 top N 的区域，在其周围 ±50 范围内用步长 10 精扫
print(f"\n{'=' * 60}")
print(f"第二阶段: 中扫 (粗扫 Top {args.top_n} 区域, 步长 10)")
print("=" * 60)
top_coarse = coarse_df.sort_values("stable_score", ascending=False).head(args.top_n)
mid_params = set()
for _, row in top_coarse.iterrows():
p_center = int(row["period"])
s_center = float(row["std"])
for p in range(max(1, p_center - 50), min(1001, p_center + 51), 10):
for s_val in np.arange(max(0.5, s_center - 50), min(1000.5, s_center + 51), 10):
mid_params.add((max(1, int(round(p))), round(float(s_val), 2)))
mid_grid = sorted(mid_params)
print(f" 参数组合数: {len(mid_grid):,}")
mid_df = evaluate_grid(
mid_grid,
workers=args.workers,
df_path=df_path,
df_1m_path=df_1m_path,
use_1m=use_1m,
step_min=step_min,
label="中扫",
)
mid_csv = out_dir / f"bb_sweep_mid_{args.kline_period}_{stamp}.csv"
mid_df.to_csv(mid_csv, index=False, encoding="utf-8-sig")
print(f"\n 中扫结果已保存: {mid_csv}")
# ─── 第三阶段：精扫 ───
# 取中扫 top 10 区域，在其周围 ±10 范围内用步长 1 精扫
print(f"\n{'=' * 60}")
print("第三阶段: 精扫 (中扫 Top 10 区域, 步长 1)")
print("=" * 60)
all_mid = pd.concat([coarse_df, mid_df], ignore_index=True)
top_mid = all_mid.sort_values("stable_score", ascending=False).head(10)
fine_params = set()
for _, row in top_mid.iterrows():
p_center = int(row["period"])
s_center = float(row["std"])
for p in range(max(1, p_center - 10), min(1001, p_center + 11)):
for s_val in np.arange(max(0.5, s_center - 10), min(1000.5, s_center + 11), 1.0):
fine_params.add((max(1, int(round(p))), round(float(s_val), 2)))
fine_grid = sorted(fine_params)
print(f" 参数组合数: {len(fine_grid):,}")
fine_df = evaluate_grid(
fine_grid,
workers=args.workers,
df_path=df_path,
df_1m_path=df_1m_path,
use_1m=use_1m,
step_min=step_min,
label="精扫",
)
fine_csv = out_dir / f"bb_sweep_fine_{args.kline_period}_{stamp}.csv"
fine_df.to_csv(fine_csv, index=False, encoding="utf-8-sig")
print(f"\n 精扫结果已保存: {fine_csv}")
# ─── 汇总 ───
all_results = pd.concat([coarse_df, mid_df, fine_df], ignore_index=True)
all_results = all_results.drop_duplicates(subset=["period", "std"], keep="last")
all_results = all_results.sort_values("stable_score", ascending=False)
all_csv = out_dir / f"bb_sweep_all_{args.kline_period}_{stamp}.csv"
all_results.to_csv(all_csv, index=False, encoding="utf-8-sig")
print(f"\n{'=' * 90}")
print("全部扫描完成 | 汇总结果")
print("=" * 90)
print(f"总计评估: {len(all_results):,} 组参数")
print(f"结果文件: {all_csv}\n")
# Top 20
top20 = all_results.head(20)
print("Top 20 (按稳定性评分):")
print("-" * 95)
print(f"{'排名':>4} {'period':>7} {'std':>7} {'最终权益':>10} {'收益%':>8} "
f"{'交易数':>6} {'胜率%':>6} {'Sharpe':>8} {'回撤%':>7} {'评分':>8}")
print("-" * 95)
for rank, (_, row) in enumerate(top20.iterrows(), 1):
print(
f"{rank:>4} {int(row['period']):>7} {row['std']:>7.1f} "
f"{row['final_eq']:>10.2f} {row['ret_pct']:>+8.1f} "
f"{int(row['n_trades']):>6} {row['win_rate']:>6.1f} "
f"{row['sharpe']:>8.4f} {row['max_dd_pct']:>7.1f} "
f"{row['stable_score']:>8.2f}"
)
# 最佳参数详细回测
best = all_results.iloc[0]
print(f"\n{'=' * 90}")
print(f"最佳参数: BB({int(best['period'])}, {best['std']})")
print(f"最终权益: {best['final_eq']:.2f} U | 收益: {best['ret_pct']:+.2f}%")
print(f"交易次数: {int(best['n_trades'])} | 胜率: {best['win_rate']:.1f}%")
print(f"Sharpe: {best['sharpe']:.4f} | 最大回撤: {best['max_dd_pct']:.1f}%")
print("=" * 90)
# 逐年权益
cfg = BBMidlineConfig(
bb_period=int(best["period"]),
bb_std=float(best["std"]),
initial_capital=200.0,
margin_pct=0.01,
leverage=100.0,
cross_margin=True,
fee_rate=0.0005,
rebate_pct=0.90,
rebate_hour_utc=0,
fill_at_close=True,
use_1m_touch_filter=use_1m,
kline_step_min=step_min,
)
final_res = run_bb_midline_backtest(df, cfg, df_1m=df_1m if use_1m else None)
eq = final_res.equity_curve["equity"].dropna()
print("\n逐年权益 (年末):")
eq_ts = eq.copy()
eq_ts.index = pd.to_datetime(eq_ts.index)
prev = 200.0
for y in range(2020, 2026):
sub = eq_ts[eq_ts.index.year == y]
if len(sub) > 0:
ye = float(sub.iloc[-1])
ret = (ye - prev) / prev * 100.0 if prev > 0 else 0.0
print(f" {y}: {ye:.2f} U (当年收益 {ret:+.1f}%)")
prev = ye
print(f"\n总手续费: {final_res.total_fee:.2f} U")
print(f"总返佣: {final_res.total_rebate:.2f} U")
print(f"净手续费: {final_res.total_fee - final_res.total_rebate:.2f} U")
# 保存最佳参数交易明细
trade_path = out_dir / f"bb_sweep_best_trades_{args.kline_period}_{stamp}.csv"
trade_rows = []
for i, t in enumerate(final_res.trades, 1):
trade_rows.append({
"序号": i,
"方向": "做多" if t.side == "long" else "做空",
"开仓时间": t.entry_time,
"平仓时间": t.exit_time,
"开仓价": round(t.entry_price, 2),
"平仓价": round(t.exit_price, 2),
"净盈亏": round(t.net_pnl, 4),
"平仓原因": t.exit_reason,
})
pd.DataFrame(trade_rows).to_csv(trade_path, index=False, encoding="utf-8-sig")
print(f"\n最佳参数交易明细: {trade_path}")
finally:
Path(df_path).unlink(missing_ok=True)
if df_1m_path:
Path(df_1m_path).unlink(missing_ok=True)
if __name__ == "__main__":
main()

356
run_bb_midline_2020_2025.py
View File

@@ -1,356 +0,0 @@
"""
布林带均线策略回测 — 2020-2025（优化版）
策略:
- 阳线 + 碰到均线 → 开多（1m 过滤：先涨碰到）
- 持多: 碰上轨止盈（无下轨止损）
- 阴线 + 碰到均线 → 平多开空（1m：先跌碰到）
- 持空: 碰下轨止盈（无上轨止损）
配置: 200U | 1%权益/单 | 万五手续费 | 90%返佣次日8点 | 100x杠杆 | 全仓
参数扫描:
- 快速: --sweep
- 全量: --full 试遍 period(0.5~1000 step0.5) × std(0.5~1000 step0.5)
"""
import sys
import time
from pathlib import Path
from concurrent.futures import ProcessPoolExecutor, as_completed
sys.path.insert(0, str(Path(__file__).resolve().parents[0]))
sys.stdout.reconfigure(line_buffering=True)
import numpy as np
import pandas as pd
from strategy.bb_midline_backtest import BBMidlineConfig, run_bb_midline_backtest
from strategy.data_loader import load_klines
def run_single(df: pd.DataFrame, df_1m: pd.DataFrame | None, cfg: BBMidlineConfig) -> dict:
r = run_bb_midline_backtest(df, cfg, df_1m=df_1m)
eq = r.equity_curve["equity"].dropna()
if len(eq) == 0:
return {"final_eq": 0, "ret_pct": -100, "n_trades": 0, "win_rate": 0, "sharpe": -999, "dd": -200}
final_eq = float(eq.iloc[-1])
ret_pct = (final_eq - cfg.initial_capital) / cfg.initial_capital * 100
n_trades = len(r.trades)
win_rate = sum(1 for t in r.trades if t.net_pnl > 0) / max(n_trades, 1) * 100
pnl = r.daily_stats["pnl"].astype(float)
sharpe = float(pnl.mean() / pnl.std()) * np.sqrt(365) if pnl.std() > 0 else 0
dd = float((eq.astype(float) - eq.astype(float).cummax()).min())
return {
"final_eq": final_eq,
"ret_pct": ret_pct,
"n_trades": n_trades,
"win_rate": win_rate,
"sharpe": sharpe,
"dd": dd,
"result": r,
}
def build_param_grid(
period_range: tuple[float, float] = (1, 200),
std_range: tuple[float, float] = (0.5, 10),
period_step: float = 1.0,
std_step: float = 0.5,
) -> list[tuple[int, float]]:
"""生成 (period, std) 参数网格，period 取整(rolling 需整数)"""
out = []
p = period_range[0]
while p <= period_range[1]:
s = std_range[0]
while s <= std_range[1]:
out.append((max(1, int(round(p))), round(s, 2)))
s += std_step
p += period_step
return out
def build_full_param_grid(
period_step: float = 0.5,
std_step: float = 0.5,
period_max: float = 1000.0,
std_max: float = 1000.0,
) -> list[tuple[int, float]]:
"""全量网格: (0.5,0.5)(0.5,1)...(0.5,std_max), (1,0.5)(1,1)...(1,std_max), ..."""
out = []
p = 0.5
while p <= period_max:
s = 0.5
while s <= std_max:
out.append((max(1, int(round(p))), round(s, 2)))
s += std_step
p += period_step
return out
def _run_one(args: tuple) -> dict:
"""供多进程调用：((p, s), df_path, use_1m, step_min) -> res"""
(p, s), df_path, use_1m, step_min = args
df = pd.read_pickle(df_path)
df_1m = None
cfg = BBMidlineConfig(
bb_period=p, bb_std=s,
initial_capital=200.0, margin_pct=0.01, leverage=100.0,
cross_margin=True, fee_rate=0.0005, rebate_pct=0.90,
rebate_hour_utc=0, fill_at_close=True,
use_1m_touch_filter=False, kline_step_min=step_min,
)
r = run_bb_midline_backtest(df, cfg, df_1m=None)
eq = r.equity_curve["equity"].dropna()
if len(eq) == 0:
return {"period": p, "std": s, "final_eq": 0, "ret_pct": -100, "n_trades": 0, "win_rate": 0, "sharpe": -999, "dd": -200}
final_eq = float(eq.iloc[-1])
ret_pct = (final_eq - 200) / 200 * 100
n_trades = len(r.trades)
win_rate = sum(1 for t in r.trades if t.net_pnl > 0) / max(n_trades, 1) * 100
pnl = r.daily_stats["pnl"].astype(float)
sharpe = float(pnl.mean() / pnl.std()) * np.sqrt(365) if pnl.std() > 0 else 0
dd = float((eq.astype(float) - eq.astype(float).cummax()).min())
return {"period": p, "std": s, "final_eq": final_eq, "ret_pct": ret_pct, "n_trades": n_trades,
"win_rate": win_rate, "sharpe": sharpe, "dd": dd}
def main(sweep_params: bool = False, full_sweep: bool = False, steps: str | None = None,
use_1m: bool = True, kline_period: str = "5m", workers: int = 1,
max_period: float = 1000.0, max_std: float = 1000.0):
out_dir = Path(__file__).resolve().parent / "strategy" / "results"
out_dir.mkdir(parents=True, exist_ok=True)
step_min = int(kline_period.replace("m", ""))
print(f"加载 K 线数据 (2020-01-01 ~ 2026-01-01) 周期={kline_period}...")
t0 = time.time()
df = load_klines(kline_period, "2020-01-01", "2026-01-01")
df_1m = load_klines("1m", "2020-01-01", "2026-01-01") if use_1m else None
print(f" {kline_period}: {len(df):,} 条" + (f", 1m: {len(df_1m):,} 条" if df_1m is not None else "") + f" ({time.time()-t0:.1f}s)\n")
if full_sweep:
# 全量网格: period 0.5~period_max step0.5, std 0.5~std_max step0.5，多进程
grid = build_full_param_grid(period_step=0.5, std_step=0.5, period_max=max_period, std_max=max_std)
print(f" 全量参数扫描: {len(grid):,} 组 (period 0.5~{max_period} step0.5 × std 0.5~{max_std} step0.5)")
use_parallel = workers > 1
if workers <= 0:
workers = max(1, (__import__("os").cpu_count() or 4) - 1)
use_parallel = workers > 1
print(f" 并行进程数: {workers}" + (" (多进程)" if use_parallel else " (顺序)"))
import tempfile
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f:
df.to_pickle(f.name)
df_path = f.name
try:
tasks = [((p, s), df_path, False, step_min) for p, s in grid]
all_results = []
best = None
best_score = -999
t0 = time.time()
if use_parallel:
try:
with ProcessPoolExecutor(max_workers=workers) as ex:
fut = {ex.submit(_run_one, t): t for t in tasks}
for f in as_completed(fut):
try:
res = f.result()
all_results.append(res)
score = res["ret_pct"] - 0.001 * max(0, res["n_trades"] - 3000)
if res["final_eq"] > 0 and score > best_score:
best_score = score
best = (res["period"], res["std"], res)
except Exception as e:
print(f" [WARN] 任务失败: {e}")
done = len(all_results)
if done % 5000 == 0 or done == len(tasks):
print(f" 进度: {done}/{len(tasks)} ({time.time()-t0:.0f}s)")
except (PermissionError, OSError) as e:
print(f" 多进程不可用 ({e})，改用顺序执行...")
use_parallel = False
if not use_parallel:
for i, t in enumerate(tasks):
try:
res = _run_one(t)
all_results.append(res)
score = res["ret_pct"] - 0.001 * max(0, res["n_trades"] - 3000)
if res["final_eq"] > 0 and score > best_score:
best_score = score
best = (res["period"], res["std"], res)
except Exception as e:
print(f" [WARN] 任务失败: {e}")
if (i + 1) % 5000 == 0 or i + 1 == len(tasks):
print(f" 进度: {i+1}/{len(tasks)} ({time.time()-t0:.0f}s)")
print(f" 全量扫描完成 ({time.time()-t0:.0f}s)")
finally:
Path(df_path).unlink(missing_ok=True)
if best:
p, s, res = best
print(f"\n 最优: BB({p},{s}) -> 权益={res['final_eq']:.1f} 收益={res['ret_pct']:+.1f}% 交易={res['n_trades']}")
sweep_df = pd.DataFrame(all_results)
sweep_path = out_dir / f"bb_midline_{kline_period}_full_sweep.csv"
sweep_df.to_csv(sweep_path, index=False)
print(f" 扫描结果: {sweep_path}")
cfg = BBMidlineConfig(
bb_period=best[0] if best else 20, bb_std=best[1] if best else 2.0,
initial_capital=200.0, margin_pct=0.01, leverage=100.0,
cross_margin=True, fee_rate=0.0005, rebate_pct=0.90,
rebate_hour_utc=0, fill_at_close=True, use_1m_touch_filter=False,
kline_step_min=step_min,
)
r = run_bb_midline_backtest(df, cfg, df_1m=None)
elif sweep_params:
# 步长组合: (period_step, std_step) 如 (0.5,0.5), (0.5,1), (1,0.5)
step_pairs = [(10, 0.5), (20, 1)] if steps is None else []
if steps:
for part in steps.split(","):
a, b = map(float, part.strip().split("_"))
step_pairs.append((a, b))
if not step_pairs:
step_pairs = [(5, 0.5), (10, 1)]
all_results = []
best = None
best_score = -999
for period_step, std_step in step_pairs:
grid = build_param_grid(
period_range=(15, 120),
std_range=(1.5, 4.0),
period_step=max(5, int(period_step)),
std_step=std_step,
)
print(f" 步长 (period>={period_step}, std+{std_step}): {len(grid)} 组...")
for p, s in grid:
cfg = BBMidlineConfig(
bb_period=p, bb_std=s,
initial_capital=200.0, margin_pct=0.01, leverage=100.0,
cross_margin=True, fee_rate=0.0005, rebate_pct=0.90,
rebate_hour_utc=0, fill_at_close=True,
use_1m_touch_filter=use_1m, kline_step_min=step_min,
)
res = run_single(df, df_1m, cfg)
res["period"] = p
res["std"] = s
res["period_step"] = period_step
res["std_step"] = std_step
all_results.append(res)
score = res["ret_pct"] - 0.001 * max(0, res["n_trades"] - 3000)
if res["final_eq"] > 0 and score > best_score:
best_score = score
best = (p, s, res)
if best:
p, s, res = best
print(f"\n 最优: BB({p},{s}) -> 权益={res['final_eq']:.1f} 收益={res['ret_pct']:+.1f}% 交易={res['n_trades']}")
sweep_rows = [
{"period": r["period"], "std": r["std"], "period_step": r.get("period_step"), "std_step": r.get("std_step"),
"final_eq": r["final_eq"], "ret_pct": r["ret_pct"], "n_trades": r["n_trades"],
"win_rate": r["win_rate"], "sharpe": r["sharpe"], "dd": r["dd"]}
for r in all_results
]
sweep_df = pd.DataFrame(sweep_rows)
sweep_path = out_dir / f"bb_midline_{kline_period}_param_sweep.csv"
sweep_df.to_csv(sweep_path, index=False)
print(f" 扫描结果: {sweep_path}")
cfg = BBMidlineConfig(
bb_period=best[0] if best else 20, bb_std=best[1] if best else 2.0,
initial_capital=200.0, margin_pct=0.01, leverage=100.0,
cross_margin=True, fee_rate=0.0005, rebate_pct=0.90,
rebate_hour_utc=0, fill_at_close=True, use_1m_touch_filter=use_1m,
kline_step_min=step_min,
)
r = best[2]["result"] if best and best[2].get("result") else run_bb_midline_backtest(df, cfg, df_1m if use_1m else None)
else:
cfg = BBMidlineConfig(
bb_period=20, bb_std=2.0,
initial_capital=200.0, margin_pct=0.01, leverage=100.0,
cross_margin=True, fee_rate=0.0005, rebate_pct=0.90,
rebate_hour_utc=0, fill_at_close=True,
use_1m_touch_filter=use_1m, kline_step_min=step_min,
)
r = run_bb_midline_backtest(df, cfg, df_1m if use_1m else None)
print("=" * 90)
print(f" 布林带均线策略回测（{kline_period}周期" + (" | 1m触及方向过滤" if use_1m else "") + "）")
print(f" BB({cfg.bb_period},{cfg.bb_std}) | 200U | 1%权益/单 | 万五 | 90%返佣次日8点 | 100x全仓")
print("=" * 90)
eq = r.equity_curve["equity"].dropna()
final_eq = float(eq.iloc[-1])
ret_pct = (final_eq - cfg.initial_capital) / cfg.initial_capital * 100
n_trades = len(r.trades)
win_rate = sum(1 for t in r.trades if t.net_pnl > 0) / max(n_trades, 1) * 100
pnl = r.daily_stats["pnl"].astype(float)
avg_daily = float(pnl.mean())
sharpe = float(pnl.mean() / pnl.std()) * np.sqrt(365) if pnl.std() > 0 else 0
dd = float((eq.astype(float) - eq.astype(float).cummax()).min())
print(f"\n 最终权益: {final_eq:.1f} U")
print(f" 总收益率: {ret_pct:+.1f}%")
print(f" 交易次数: {n_trades}")
print(f" 胜率: {win_rate:.1f}%")
print(f" 日均PnL: {avg_daily:+.2f} U")
print(f" 最大回撤: {dd:.1f} U")
print(f" Sharpe: {sharpe:.2f}")
print(f" 总手续费: {r.total_fee:.2f} U")
print(f" 总返佣: {r.total_rebate:.2f} U")
years = list(range(2020, 2026))
eq_ts = eq.copy()
eq_ts.index = pd.to_datetime(eq_ts.index)
prev_ye = cfg.initial_capital
print("\n 逐年权益 (年末):")
for y in years:
subset = eq_ts[eq_ts.index.year == y]
if len(subset) > 0:
ye = float(subset.iloc[-1])
ret = (ye - prev_ye) / prev_ye * 100 if prev_ye > 0 else 0
print(f" {y}: {ye:.1f} U (当年收益 {ret:+.1f}%)")
prev_ye = ye
rows = []
for i, t in enumerate(r.trades, 1):
rows.append({
"序号": i,
"方向": "做多" if t.side == "long" else "做空",
"开仓时间": t.entry_time,
"平仓时间": t.exit_time,
"开仓价": round(t.entry_price, 2),
"平仓价": round(t.exit_price, 2),
"保证金": round(t.margin, 2),
"杠杆": t.leverage,
"数量": round(t.qty, 4),
"毛盈亏": round(t.gross_pnl, 2),
"手续费": round(t.fee, 2),
"净盈亏": round(t.net_pnl, 2),
"平仓原因": t.exit_reason,
})
pd.DataFrame(rows).to_csv(out_dir / f"bb_midline_{kline_period}_2020_2025_trade_detail.csv", index=False, encoding="utf-8-sig")
r.daily_stats.to_csv(out_dir / f"bb_midline_{kline_period}_2020_2025_daily.csv", encoding="utf-8-sig")
print(f"\n 交易明细: {out_dir / f'bb_midline_{kline_period}_2020_2025_trade_detail.csv'}")
if __name__ == "__main__":
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("--sweep", action="store_true", help="快速参数扫描")
ap.add_argument("--full", action="store_true", dest="full_sweep", help="全量: period(0.5~1000 step0.5)×std(0.5~1000 step0.5)")
ap.add_argument("--steps", type=str, help="步长组合，如 1_0.5,2_1,5_0.5")
ap.add_argument("--no-1m", dest="no_1m", action="store_true", help="禁用 1m 触及方向过滤(更快)")
ap.add_argument("-p", "--period", choices=["5m", "15m", "30m"], default="5m", help="K线周期")
ap.add_argument("-j", "--workers", type=int, default=0, help="全量扫描并行进程数(0=auto)")
ap.add_argument("--max-period", type=float, default=1000, help="全量扫描 period 上限")
ap.add_argument("--max-std", type=float, default=1000, help="全量扫描 std 上限")
args = ap.parse_args()
main(sweep_params=args.sweep, full_sweep=args.full_sweep, steps=args.steps,
use_1m=not args.no_1m, kline_period=args.period, workers=args.workers,
max_period=args.max_period, max_std=args.max_std)

389
run_bb_midline_full_grid.py
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@@ -1,389 +0,0 @@
"""
布林带均线策略 - 全参数组合扫描 (1-1000, 1-1000)
策略:
- 阳线 + 先涨碰到均线(1m判断) → 开多
- 持多: 碰上轨止盈
- 阴线 + 先跌碰到均线(1m判断) → 平多开空
- 持空: 碰下轨止盈
配置: 200U | 1%权益/单 | 万五手续费 | 90%返佣次日8点 | 100x杠杆 | 全仓
参数遍历: (0.5,0.5)(0.5,1)...(0.5,std_max), (1,0.5)(1,1)...(1,std_max), ...
直至 (period_max, std_max)
"""
from __future__ import annotations
import os
import sys
import tempfile
import time
from collections import defaultdict
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[0]))
sys.stdout.reconfigure(line_buffering=True)
import numpy as np
import pandas as pd
from strategy.bb_midline_backtest import BBMidlineConfig, run_bb_midline_backtest
from strategy.data_loader import get_1m_touch_direction, load_klines
from strategy.indicators import bollinger
def build_full_param_grid(
period_min: float = 1.0,
period_max: float = 1000.0,
period_step: float = 1.0,
std_min: float = 1.0,
std_max: float = 1000.0,
std_step: float = 1.0,
) -> list[tuple[int, float]]:
"""生成全量 (period, std) 组合，period 取整"""
out = []
p = period_min
while p <= period_max:
s = std_min
while s <= std_max:
out.append((max(1, int(round(p))), round(s, 2)))
s += std_step
p += period_step
return sorted(set(out))
def stable_score(ret_pct: float, sharpe: float, dd_pct: float, n_trades: int) -> float:
"""收益稳定性评分"""
sparse_penalty = -5.0 if n_trades < 200 else 0.0
return ret_pct + sharpe * 12.0 - abs(dd_pct) * 0.8 + sparse_penalty
G_DF: pd.DataFrame | None = None
G_DF_1M: pd.DataFrame | None = None
G_USE_1M: bool = True
G_STEP_MIN: int = 5
def _init_worker(df_path: str, df_1m_path: str | None, use_1m: bool, step_min: int):
global G_DF, G_DF_1M, G_USE_1M, G_STEP_MIN
G_DF = pd.read_pickle(df_path)
G_DF_1M = pd.read_pickle(df_1m_path) if (use_1m and df_1m_path) else None
G_USE_1M = bool(use_1m)
G_STEP_MIN = int(step_min)
def _eval_period_task(args: tuple[int, list[float]]) -> list[dict]:
period, std_list = args
assert G_DF is not None
arr_touch_dir = None
if G_USE_1M and G_DF_1M is not None:
close = G_DF["close"].astype(float)
bb_mid, _, _, _ = bollinger(close, period, 1.0)
arr_touch_dir = get_1m_touch_direction(
G_DF, G_DF_1M, bb_mid.values, kline_step_min=G_STEP_MIN
)
rows: list[dict] = []
for std in std_list:
cfg = BBMidlineConfig(
bb_period=period,
bb_std=float(std),
initial_capital=200.0,
margin_pct=0.01,
leverage=100.0,
cross_margin=True,
fee_rate=0.0005,
rebate_pct=0.90,
rebate_hour_utc=0,
fill_at_close=True,
use_1m_touch_filter=G_USE_1M,
kline_step_min=G_STEP_MIN,
)
result = run_bb_midline_backtest(
G_DF,
cfg,
df_1m=G_DF_1M if G_USE_1M else None,
arr_touch_dir_override=arr_touch_dir,
)
eq = result.equity_curve["equity"].dropna()
if len(eq) == 0:
final_eq = 0.0
ret_pct = -100.0
dd_u = -200.0
dd_pct = 100.0
else:
final_eq = float(eq.iloc[-1])
ret_pct = (final_eq - cfg.initial_capital) / cfg.initial_capital * 100.0
dd_u = float((eq.astype(float) - eq.astype(float).cummax()).min())
dd_pct = abs(dd_u) / cfg.initial_capital * 100.0
n_trades = len(result.trades)
win_rate = (
sum(1 for t in result.trades if t.net_pnl > 0) / n_trades * 100.0
if n_trades > 0
else 0.0
)
pnl = result.daily_stats["pnl"].astype(float)
sharpe = (
float(pnl.mean() / pnl.std()) * np.sqrt(365.0) if pnl.std() > 0 else 0.0
)
score = stable_score(ret_pct, sharpe, dd_pct, n_trades)
rows.append(
{
"period": period,
"std": round(float(std), 2),
"final_eq": final_eq,
"ret_pct": ret_pct,
"n_trades": n_trades,
"win_rate": win_rate,
"sharpe": sharpe,
"max_dd_u": dd_u,
"max_dd_pct": dd_pct,
"stable_score": score,
}
)
return rows
def evaluate_grid(
params: list[tuple[int, float]],
*,
workers: int,
df_path: str,
df_1m_path: str | None,
use_1m: bool,
step_min: int,
) -> pd.DataFrame:
by_period: dict[int, set[float]] = defaultdict(set)
for p, s in params:
by_period[int(p)].add(round(float(s), 2))
tasks = [(p, sorted(stds)) for p, stds in sorted(by_period.items())]
total_periods = len(tasks)
total_combos = sum(len(stds) for _, stds in tasks)
print(f" 评估 {total_combos:,} 组参数, {total_periods} 个 period, workers={workers}")
start = time.time()
rows: list[dict] = []
done_periods = 0
done_combos = 0
with ProcessPoolExecutor(
max_workers=workers,
initializer=_init_worker,
initargs=(df_path, df_1m_path, use_1m, step_min),
) as ex:
future_map = {ex.submit(_eval_period_task, task): task for task in tasks}
for fut in as_completed(future_map):
period, stds = future_map[fut]
res = fut.result()
rows.extend(res)
done_periods += 1
done_combos += len(stds)
if done_periods % max(1, total_periods // 20) == 0 or done_periods == total_periods:
elapsed = time.time() - start
print(f" 进度 {done_combos:,}/{total_combos:,} ({elapsed:.0f}s)")
df = pd.DataFrame(rows)
print(f" 完成, 用时 {time.time() - start:.1f}s")
return df
def main():
import argparse
parser = argparse.ArgumentParser(description="布林带均线策略全参数扫描 (1-1000, 1-1000)")
parser.add_argument(
"--period-min", type=float, default=1.0, help="period 下限"
)
parser.add_argument(
"--period-max", type=float, default=1000.0, help="period 上限"
)
parser.add_argument(
"--period-step", type=float, default=10.0, help="period 步长 (建议10以缩短时间)"
)
parser.add_argument("--std-min", type=float, default=0.5, help="std 下限")
parser.add_argument("--std-max", type=float, default=1000.0, help="std 上限")
parser.add_argument(
"--std-step", type=float, default=1.0, help="std 步长"
)
parser.add_argument(
"-p", "--kline-period", default="5m", choices=["5m", "15m", "30m"]
)
parser.add_argument(
"-j", "--workers", type=int, default=max(1, (os.cpu_count() or 4) - 1)
)
parser.add_argument("--no-1m", action="store_true", help="禁用 1m 触及方向过滤")
parser.add_argument(
"--source",
default="bitmart",
choices=["bitmart", "binance"],
help="数据源",
)
parser.add_argument(
"--quick",
action="store_true",
help="快速模式: period 1-200 step20, std 1-20 step2",
)
args = parser.parse_args()
use_1m = not args.no_1m
step_min = int(args.kline_period.replace("m", ""))
if args.quick:
args.period_min = 1.0
args.period_max = 200.0
args.period_step = 20.0
args.std_min = 0.5
args.std_max = 20.0
args.std_step = 1.0
print(" 快速模式: period 1-200 step20, std 1-20 step2")
out_dir = Path(__file__).resolve().parent / "strategy" / "results"
out_dir.mkdir(parents=True, exist_ok=True)
print("加载 K 线数据 (2020-01-01 ~ 2026-01-01)...")
t0 = time.time()
try:
df = load_klines(args.kline_period, "2020-01-01", "2026-01-01", source=args.source)
df_1m = (
load_klines("1m", "2020-01-01", "2026-01-01", source=args.source)
if use_1m
else None
)
except Exception as e:
alt = "binance" if args.source == "bitmart" else "bitmart"
print(f" {args.source} 加载失败 ({e}), 尝试 {alt}...")
df = load_klines(args.kline_period, "2020-01-01", "2026-01-01", source=alt)
df_1m = (
load_klines("1m", "2020-01-01", "2026-01-01", source=alt)
if use_1m
else None
)
args.source = alt
print(
f" {args.kline_period}: {len(df):,} 条"
+ (f", 1m: {len(df_1m):,} 条" if df_1m is not None else "")
+ f" | 数据源: {args.source} ({time.time()-t0:.1f}s)\n"
)
grid = build_full_param_grid(
period_min=args.period_min,
period_max=args.period_max,
period_step=args.period_step,
std_min=args.std_min,
std_max=args.std_max,
std_step=args.std_step,
)
print(f"参数网格: {len(grid):,} 组")
print(
f" period: {args.period_min}~{args.period_max} step{args.period_step}, "
f"std: {args.std_min}~{args.std_max} step{args.std_step}"
)
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f_df:
df.to_pickle(f_df.name)
df_path = f_df.name
df_1m_path = None
if df_1m is not None:
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f_1m:
df_1m.to_pickle(f_1m.name)
df_1m_path = f_1m.name
try:
result_df = evaluate_grid(
grid,
workers=args.workers,
df_path=df_path,
df_1m_path=df_1m_path,
use_1m=use_1m,
step_min=step_min,
)
finally:
Path(df_path).unlink(missing_ok=True)
if df_1m_path:
Path(df_1m_path).unlink(missing_ok=True)
if result_df.empty:
print("无有效结果")
return
best_stable = result_df.sort_values("stable_score", ascending=False).iloc[0]
best_return = result_df.sort_values("ret_pct", ascending=False).iloc[0]
stamp = time.strftime("%Y%m%d_%H%M%S")
csv_path = out_dir / f"bb_midline_full_grid_{args.kline_period}_{stamp}.csv"
result_df.to_csv(csv_path, index=False)
print(f"\n扫描结果已保存: {csv_path}")
print("\n" + "=" * 90)
print("布林带均线策略 | 2020-2025 | 200U | 1%权益/单 | 万五 | 90%返佣次日8点 | 100x全仓")
print("=" * 90)
print(
f"最佳稳定参数: BB({int(best_stable['period'])},{best_stable['std']}) | "
f"权益={best_stable['final_eq']:.1f}U | 收益={best_stable['ret_pct']:+.1f}% | "
f"回撤={best_stable['max_dd_pct']:.1f}% | Sharpe={best_stable['sharpe']:.2f} | "
f"交易={int(best_stable['n_trades'])}"
)
print(
f"最高收益参数: BB({int(best_return['period'])},{best_return['std']}) | "
f"权益={best_return['final_eq']:.1f}U | 收益={best_return['ret_pct']:+.1f}% | "
f"回撤={best_return['max_dd_pct']:.1f}% | Sharpe={best_return['sharpe']:.2f} | "
f"交易={int(best_return['n_trades'])}"
)
print("=" * 90)
cfg = BBMidlineConfig(
bb_period=int(best_stable["period"]),
bb_std=float(best_stable["std"]),
initial_capital=200.0,
margin_pct=0.01,
leverage=100.0,
cross_margin=True,
fee_rate=0.0005,
rebate_pct=0.90,
rebate_hour_utc=0,
fill_at_close=True,
use_1m_touch_filter=use_1m,
kline_step_min=step_min,
)
final_res = run_bb_midline_backtest(
df, cfg, df_1m=df_1m if use_1m else None
)
eq = final_res.equity_curve["equity"].dropna()
print("\n逐年权益 (年末):")
eq_ts = eq.copy()
eq_ts.index = pd.to_datetime(eq_ts.index)
prev = 200.0
for y in range(2020, 2026):
sub = eq_ts[eq_ts.index.year == y]
if len(sub) > 0:
ye = float(sub.iloc[-1])
ret = (ye - prev) / prev * 100.0 if prev > 0 else 0.0
print(f" {y}: {ye:.1f} U (当年收益 {ret:+.1f}%)")
prev = ye
trade_path = out_dir / f"bb_midline_best_trades_{args.kline_period}_{stamp}.csv"
rows = []
for i, t in enumerate(final_res.trades, 1):
rows.append({
"序号": i,
"方向": "做多" if t.side == "long" else "做空",
"开仓时间": t.entry_time,
"平仓时间": t.exit_time,
"开仓价": round(t.entry_price, 2),
"平仓价": round(t.exit_price, 2),
"净盈亏": round(t.net_pnl, 2),
"平仓原因": t.exit_reason,
})
pd.DataFrame(rows).to_csv(trade_path, index=False, encoding="utf-8-sig")
print(f"\n最佳参数交易明细: {trade_path}")
if __name__ == "__main__":
main()

422
run_bb_midline_hier_search.py
View File

@@ -1,422 +0,0 @@
"""
布林带中轨策略参数分层搜索（2020-2025）
说明:
- 全区间覆盖: period 1~1000, std 0.5~1000
- 分层搜索: 先粗扫全区间，再在候选周围细化，最终细化到 std=0.5 步长
- 使用 1m 触及方向过滤（先涨/先跌）时，按 period 复用触及方向以提速
"""
from __future__ import annotations
import argparse
import math
import os
import tempfile
import time
from collections import defaultdict
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path
import numpy as np
import pandas as pd
from strategy.bb_midline_backtest import BBMidlineConfig, run_bb_midline_backtest
from strategy.data_loader import get_1m_touch_direction, load_klines
from strategy.indicators import bollinger
G_DF: pd.DataFrame | None = None
G_DF_1M: pd.DataFrame | None = None
G_USE_1M: bool = True
G_STEP_MIN: int = 5
def frange(start: float, end: float, step: float) -> list[float]:
out: list[float] = []
x = float(start)
while x <= end + 1e-9:
out.append(round(x, 6))
x += step
return out
def build_grid(
p_start: float,
p_end: float,
p_step: float,
s_start: float,
s_end: float,
s_step: float,
) -> list[tuple[int, float]]:
periods = sorted({max(1, min(1000, int(round(v)))) for v in frange(p_start, p_end, p_step)})
stds = sorted({round(max(0.5, min(1000.0, v)), 2) for v in frange(s_start, s_end, s_step)})
if 1000 not in periods:
periods.append(1000)
if 1000.0 not in stds:
stds.append(1000.0)
out = [(p, s) for p in periods for s in stds]
return sorted(set(out))
def build_local_grid(
centers: pd.DataFrame,
p_window: int,
p_step: int,
s_window: float,
s_step: float,
) -> list[tuple[int, float]]:
out: set[tuple[int, float]] = set()
for _, row in centers.iterrows():
p0 = int(row["period"])
s0 = float(row["std"])
p_min = max(1, p0 - p_window)
p_max = min(1000, p0 + p_window)
s_min = max(0.5, s0 - s_window)
s_max = min(1000.0, s0 + s_window)
periods = sorted({max(1, min(1000, int(round(v)))) for v in frange(p_min, p_max, p_step)})
stds = sorted({round(max(0.5, min(1000.0, v)), 2) for v in frange(s_min, s_max, s_step)})
for p in periods:
for s in stds:
out.add((p, s))
return sorted(out)
def score_row(ret_pct: float, sharpe: float, dd_pct: float, n_trades: int) -> float:
# 偏向“收益稳定”: 收益和夏普加分，回撤和极少交易惩罚
sparse_penalty = -5.0 if n_trades < 200 else 0.0
return ret_pct + sharpe * 12.0 - dd_pct * 0.8 + sparse_penalty
def _init_worker(df_path: str, df_1m_path: str | None, use_1m: bool, step_min: int):
global G_DF, G_DF_1M, G_USE_1M, G_STEP_MIN
G_DF = pd.read_pickle(df_path)
G_DF_1M = pd.read_pickle(df_1m_path) if (use_1m and df_1m_path) else None
G_USE_1M = bool(use_1m)
G_STEP_MIN = int(step_min)
def _eval_period_task(args: tuple[int, list[float]]) -> list[dict]:
period, std_list = args
assert G_DF is not None
arr_touch_dir = None
if G_USE_1M and G_DF_1M is not None:
close = G_DF["close"].astype(float)
bb_mid, _, _, _ = bollinger(close, period, 1.0)
arr_touch_dir = get_1m_touch_direction(G_DF, G_DF_1M, bb_mid.values, kline_step_min=G_STEP_MIN)
rows: list[dict] = []
for std in std_list:
cfg = BBMidlineConfig(
bb_period=period,
bb_std=float(std),
initial_capital=200.0,
margin_pct=0.01,
leverage=100.0,
cross_margin=True,
fee_rate=0.0005,
rebate_pct=0.90,
rebate_hour_utc=0,
fill_at_close=True,
use_1m_touch_filter=G_USE_1M,
kline_step_min=G_STEP_MIN,
)
result = run_bb_midline_backtest(
G_DF,
cfg,
df_1m=G_DF_1M if G_USE_1M else None,
arr_touch_dir_override=arr_touch_dir,
)
eq = result.equity_curve["equity"].dropna()
if len(eq) == 0:
final_eq = 0.0
ret_pct = -100.0
dd_u = -200.0
dd_pct = 100.0
else:
final_eq = float(eq.iloc[-1])
ret_pct = (final_eq - cfg.initial_capital) / cfg.initial_capital * 100.0
dd_u = float((eq.astype(float) - eq.astype(float).cummax()).min())
dd_pct = abs(dd_u) / cfg.initial_capital * 100.0
n_trades = len(result.trades)
win_rate = (
sum(1 for t in result.trades if t.net_pnl > 0) / n_trades * 100.0
if n_trades > 0
else 0.0
)
pnl = result.daily_stats["pnl"].astype(float)
sharpe = float(pnl.mean() / pnl.std()) * math.sqrt(365.0) if pnl.std() > 0 else 0.0
stable_score = score_row(ret_pct, sharpe, dd_pct, n_trades)
rows.append(
{
"period": period,
"std": round(float(std), 2),
"final_eq": final_eq,
"ret_pct": ret_pct,
"n_trades": n_trades,
"win_rate": win_rate,
"sharpe": sharpe,
"max_dd_u": dd_u,
"max_dd_pct": dd_pct,
"stable_score": stable_score,
"use_1m_filter": int(G_USE_1M),
}
)
return rows
def evaluate_grid(
params: list[tuple[int, float]],
*,
workers: int,
df_path: str,
df_1m_path: str | None,
use_1m: bool,
step_min: int,
label: str,
) -> pd.DataFrame:
by_period: dict[int, set[float]] = defaultdict(set)
for p, s in params:
by_period[int(p)].add(round(float(s), 2))
tasks = [(p, sorted(stds)) for p, stds in sorted(by_period.items())]
total_periods = len(tasks)
total_combos = sum(len(stds) for _, stds in tasks)
if total_combos == 0:
return pd.DataFrame()
print(f"[{label}] period组数={total_periods}, 参数组合={total_combos}, workers={workers}")
start = time.time()
rows: list[dict] = []
done_periods = 0
done_combos = 0
with ProcessPoolExecutor(
max_workers=workers,
initializer=_init_worker,
initargs=(df_path, df_1m_path, use_1m, step_min),
) as ex:
future_map = {ex.submit(_eval_period_task, task): task for task in tasks}
for fut in as_completed(future_map):
period, stds = future_map[fut]
res = fut.result()
rows.extend(res)
done_periods += 1
done_combos += len(stds)
if (
done_periods == total_periods
or done_periods % max(1, total_periods // 10) == 0
):
elapsed = time.time() - start
print(
f"[{label}] 进度 {done_combos}/{total_combos} 组合 "
f"({done_periods}/{total_periods} periods), {elapsed:.0f}s"
)
df = pd.DataFrame(rows)
print(f"[{label}] 完成, 用时 {time.time() - start:.1f}s")
return df
def summarize_yearly(eq: pd.Series, initial_capital: float = 200.0) -> pd.DataFrame:
s = eq.dropna().copy()
s.index = pd.to_datetime(s.index)
out_rows: list[dict] = []
prev = initial_capital
for year in range(2020, 2026):
sub = s[s.index.year == year]
if len(sub) == 0:
continue
ye = float(sub.iloc[-1])
ret = (ye - prev) / prev * 100.0 if prev > 0 else 0.0
out_rows.append({"year": year, "year_end_equity": ye, "year_return_pct": ret})
prev = ye
return pd.DataFrame(out_rows)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-p", "--period", default="5m", choices=["5m", "15m", "30m"])
parser.add_argument("--start", default="2020-01-01")
parser.add_argument("--end", default="2026-01-01")
parser.add_argument("-j", "--workers", type=int, default=max(1, (os.cpu_count() or 4) - 1))
parser.add_argument("--no-1m", action="store_true", help="禁用 1m 方向过滤")
args = parser.parse_args()
use_1m = not args.no_1m
step_min = int(args.period.replace("m", ""))
out_dir = Path(__file__).resolve().parent / "strategy" / "results"
out_dir.mkdir(parents=True, exist_ok=True)
print(f"加载数据: {args.period} {args.start}~{args.end}")
t0 = time.time()
df = load_klines(args.period, args.start, args.end)
df_1m = load_klines("1m", args.start, args.end) if use_1m else None
print(
f" {args.period}: {len(df):,} 条"
+ (f", 1m: {len(df_1m):,} 条" if df_1m is not None else "")
+ f", {time.time()-t0:.1f}s\n"
)
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f_df:
df.to_pickle(f_df.name)
df_path = f_df.name
df_1m_path = None
if df_1m is not None:
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f_1m:
df_1m.to_pickle(f_1m.name)
df_1m_path = f_1m.name
try:
evaluated: set[tuple[int, float]] = set()
all_parts: list[pd.DataFrame] = []
# Stage 1: 全区间粗扫
stage1 = build_grid(1, 1000, 50, 0.5, 1000, 50)
stage1 = [x for x in stage1 if x not in evaluated]
df1 = evaluate_grid(
stage1,
workers=args.workers,
df_path=df_path,
df_1m_path=df_1m_path,
use_1m=use_1m,
step_min=step_min,
label="stage1-global",
)
if not df1.empty:
all_parts.append(df1)
evaluated.update((int(r["period"]), float(r["std"])) for _, r in df1.iterrows())
seed1 = (
df1.sort_values("stable_score", ascending=False).head(6)
if not df1.empty
else pd.DataFrame(columns=["period", "std"])
)
# Stage 2: 候选周围中等步长细化
stage2 = build_local_grid(seed1, p_window=25, p_step=5, s_window=50, s_step=10)
stage2 = [x for x in stage2 if x not in evaluated]
df2 = evaluate_grid(
stage2,
workers=args.workers,
df_path=df_path,
df_1m_path=df_1m_path,
use_1m=use_1m,
step_min=step_min,
label="stage2-local",
)
if not df2.empty:
all_parts.append(df2)
evaluated.update((int(r["period"]), float(r["std"])) for _, r in df2.iterrows())
pool2 = pd.concat([d for d in [df1, df2] if not d.empty], ignore_index=True)
seed2 = (
pool2.sort_values("stable_score", ascending=False).head(4)
if len(pool2) > 0
else pd.DataFrame(columns=["period", "std"])
)
# Stage 3: 候选周围更细化
stage3 = build_local_grid(seed2, p_window=8, p_step=1, s_window=10, s_step=1)
stage3 = [x for x in stage3 if x not in evaluated]
df3 = evaluate_grid(
stage3,
workers=args.workers,
df_path=df_path,
df_1m_path=df_1m_path,
use_1m=use_1m,
step_min=step_min,
label="stage3-fine",
)
if not df3.empty:
all_parts.append(df3)
evaluated.update((int(r["period"]), float(r["std"])) for _, r in df3.iterrows())
pool3 = pd.concat([d for d in [df1, df2, df3] if not d.empty], ignore_index=True)
seed3 = (
pool3.sort_values("stable_score", ascending=False).head(2)
if len(pool3) > 0
else pd.DataFrame(columns=["period", "std"])
)
# Stage 4: 最终细化（std 0.5 步长）
stage4 = build_local_grid(seed3, p_window=3, p_step=1, s_window=4, s_step=0.5)
stage4 = [x for x in stage4 if x not in evaluated]
df4 = evaluate_grid(
stage4,
workers=args.workers,
df_path=df_path,
df_1m_path=df_1m_path,
use_1m=use_1m,
step_min=step_min,
label="stage4-final",
)
if not df4.empty:
all_parts.append(df4)
evaluated.update((int(r["period"]), float(r["std"])) for _, r in df4.iterrows())
if not all_parts:
raise RuntimeError("未得到任何评估结果")
all_df = pd.concat(all_parts, ignore_index=True)
all_df = all_df.drop_duplicates(subset=["period", "std"], keep="last")
best_stable = all_df.sort_values("stable_score", ascending=False).iloc[0]
best_return = all_df.sort_values("ret_pct", ascending=False).iloc[0]
# 对最佳稳定参数再跑一次，导出逐年收益
cfg = BBMidlineConfig(
bb_period=int(best_stable["period"]),
bb_std=float(best_stable["std"]),
initial_capital=200.0,
margin_pct=0.01,
leverage=100.0,
cross_margin=True,
fee_rate=0.0005,
rebate_pct=0.90,
rebate_hour_utc=0,
fill_at_close=True,
use_1m_touch_filter=use_1m,
kline_step_min=step_min,
)
final_res = run_bb_midline_backtest(df, cfg, df_1m=df_1m if use_1m else None)
final_eq = final_res.equity_curve["equity"].dropna()
yearly = summarize_yearly(final_eq, initial_capital=200.0)
stamp = time.strftime("%Y%m%d_%H%M%S")
all_path = out_dir / f"bb_midline_hier_search_{args.period}_{stamp}.csv"
yearly_path = out_dir / f"bb_midline_hier_search_{args.period}_{stamp}_yearly.csv"
all_df.sort_values("stable_score", ascending=False).to_csv(all_path, index=False)
yearly.to_csv(yearly_path, index=False)
print("\n" + "=" * 96)
print("分层搜索完成")
print(
f"最佳稳定参数: period={int(best_stable['period'])}, std={float(best_stable['std']):.2f} | "
f"final={best_stable['final_eq']:.4f}U | ret={best_stable['ret_pct']:+.2f}% | "
f"dd={best_stable['max_dd_pct']:.2f}% | sharpe={best_stable['sharpe']:.3f} | "
f"trades={int(best_stable['n_trades'])}"
)
print(
f"最高收益参数: period={int(best_return['period'])}, std={float(best_return['std']):.2f} | "
f"final={best_return['final_eq']:.4f}U | ret={best_return['ret_pct']:+.2f}% | "
f"dd={best_return['max_dd_pct']:.2f}% | sharpe={best_return['sharpe']:.3f} | "
f"trades={int(best_return['n_trades'])}"
)
print(f"结果文件: {all_path}")
print(f"逐年文件: {yearly_path}")
print("=" * 96)
finally:
Path(df_path).unlink(missing_ok=True)
if df_1m_path:
Path(df_1m_path).unlink(missing_ok=True)
if __name__ == "__main__":
main()

1
strategy/__init__.py
View File

@@ -1 +0,0 @@
from .bb_backtest import BBConfig, BBResult, BBTrade, run_bb_backtest

156
strategy/backtest_2023.py
View File

@@ -1,156 +0,0 @@
"""
2023 年回测入口 - 用训练出的最优参数在 2023 全年数据上回测
"""
import json
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import pandas as pd
import numpy as np
from strategy.data_loader import load_klines
from strategy.indicators import compute_all_indicators
from strategy.strategy_signal import (
generate_indicator_signals, compute_composite_score,
apply_htf_filter,
)
from strategy.backtest_engine import BacktestEngine
def main():
# 加载最佳参数
params_path = os.path.join(os.path.dirname(__file__), 'best_params_2020_2022.json')
if not os.path.exists(params_path):
print(f"错误: 找不到参数文件 {params_path}")
print("请先运行 train.py 进行训练")
return
with open(params_path, 'r') as f:
params = json.load(f)
print("=" * 70)
print("2023 年真实回测 (样本外)")
print("=" * 70)
# 加载数据 (多加载一些前置数据用于指标预热)
print("\n加载数据...")
df_5m = load_klines('5m', '2022-11-01', '2024-01-01')
df_1h = load_klines('1h', '2022-11-01', '2024-01-01')
print(f" 5m: {len(df_5m)} 条, 1h: {len(df_1h)} 条")
# 计算指标
print("计算指标...")
df_5m = compute_all_indicators(df_5m, params)
df_1h = compute_all_indicators(df_1h, params)
# 生成信号
print("生成信号...")
df_5m = generate_indicator_signals(df_5m, params)
df_1h = generate_indicator_signals(df_1h, params)
# 综合得分
score = compute_composite_score(df_5m, params)
score = apply_htf_filter(score, df_1h, params)
# 截取 2023 年数据
mask = (df_5m.index >= '2023-01-01') & (df_5m.index < '2024-01-01')
df_2023 = df_5m.loc[mask]
score_2023 = score.loc[mask]
print(f" 2023年数据: {len(df_2023)} 条")
# 回测
print("\n开始回测...")
engine = BacktestEngine(
initial_capital=1000.0,
margin_per_trade=25.0,
leverage=50,
fee_rate=0.0005,
rebate_ratio=0.70,
max_daily_drawdown=50.0,
min_hold_bars=1,
stop_loss_pct=params['stop_loss_pct'],
take_profit_pct=params['take_profit_pct'],
max_positions=int(params.get('max_positions', 3)),
)
result = engine.run(df_2023, score_2023, open_threshold=params['open_threshold'])
# ============================================================
# 输出结果
# ============================================================
print("\n" + "=" * 70)
print("2023 年回测结果")
print("=" * 70)
print(f" 初始资金: 1000.00 U")
print(f" 最终资金: {result['final_capital']:.2f} U")
print(f" 总收益: {result['total_pnl']:.2f} U")
print(f" 总手续费: {result['total_fee']:.2f} U")
print(f" 总返佣: {result['total_rebate']:.2f} U")
print(f" 交易次数: {result['num_trades']}")
print(f" 胜率: {result['win_rate']:.2%}")
print(f" 盈亏比: {result['profit_factor']:.2f}")
print(f" 日均收益: {result['avg_daily_pnl']:.2f} U")
print(f" 最大日回撤: {result['max_daily_dd']:.2f} U")
# 月度统计
daily_pnl = result['daily_pnl']
if daily_pnl:
df_daily = pd.DataFrame(list(daily_pnl.items()), columns=['date', 'pnl'])
df_daily['date'] = pd.to_datetime(df_daily['date'])
df_daily['month'] = df_daily['date'].dt.to_period('M')
monthly = df_daily.groupby('month')['pnl'].agg(['sum', 'count', 'mean', 'min'])
monthly.columns = ['月收益', '交易天数', '日均收益', '最大日亏损']
print("\n" + "-" * 70)
print("月度统计:")
print("-" * 70)
for idx, row in monthly.iterrows():
status = "✅" if row['月收益'] > 0 else "❌"
dd_status = "✅" if row['最大日亏损'] > -50 else "⚠️"
print(f" {idx} | 收益: {row['月收益']:>8.2f}U | "
f"日均: {row['日均收益']:>7.2f}U | "
f"最大日亏: {row['最大日亏损']:>7.2f}U {dd_status} | {status}")
# 日均收益是否达标
avg_daily = df_daily['pnl'].mean()
days_above_50 = (df_daily['pnl'] >= 50).sum()
days_below_neg50 = (df_daily['pnl'] < -50).sum()
print(f"\n 日均收益: {avg_daily:.2f}U {'✅ 达标' if avg_daily >= 50 else '❌ 未达标'}")
print(f" 日收益>=50U的天数: {days_above_50} / {len(df_daily)}")
print(f" 日回撤>50U的天数: {days_below_neg50} / {len(df_daily)}")
# 保存逐日 PnL
output_dir = os.path.dirname(__file__)
if daily_pnl:
df_daily_out = pd.DataFrame(list(daily_pnl.items()), columns=['date', 'pnl'])
df_daily_out['cumulative_pnl'] = df_daily_out['pnl'].cumsum()
daily_csv = os.path.join(output_dir, 'backtest_2023_daily_pnl.csv')
df_daily_out.to_csv(daily_csv, index=False)
print(f"\n逐日PnL已保存: {daily_csv}")
# 保存交易记录
if result['trades']:
trades_data = []
for t in result['trades']:
trades_data.append({
'entry_time': t.entry_time,
'exit_time': t.exit_time,
'direction': '多' if t.direction == 1 else '空',
'entry_price': t.entry_price,
'exit_price': t.exit_price,
'pnl': round(t.pnl, 4),
'fee': round(t.fee, 4),
'rebate': round(t.rebate, 4),
'holding_bars': t.holding_bars,
})
df_trades = pd.DataFrame(trades_data)
trades_csv = os.path.join(output_dir, 'backtest_2023_trades.csv')
df_trades.to_csv(trades_csv, index=False)
print(f"交易记录已保存: {trades_csv}")
print("\n" + "=" * 70)
if __name__ == '__main__':
main()

366
strategy/backtest_2023_daily_pnl.csv
View File

@@ -1,366 +0,0 @@
date,pnl,cumulative_pnl
2023-01-01,-13.24030780391167,-13.24030780391167
2023-01-02,-6.446144625588336,-19.686452429500005
2023-01-03,1.604645378778668,-18.081807050721338
2023-01-04,56.24999999999943,38.168192949278094
2023-01-05,0.33406192478092844,38.50225487405902
2023-01-06,-4.28936549280675,34.21288938125227
2023-01-07,14.786004985419915,48.99889436667218
2023-01-08,74.57347500987392,123.5723693765461
2023-01-09,100.43932814185042,224.01169751839655
2023-01-10,-11.400950315324355,212.61074720307218
2023-01-11,119.73808656927638,332.34883377234854
2023-01-12,-46.87500000000008,285.4738337723485
2023-01-13,24.95301706493165,310.42685083728014
2023-01-14,36.87499999999923,347.3018508372794
2023-01-15,-42.164085212923396,305.137765624356
2023-01-16,-42.37550401931327,262.76226160504274
2023-01-17,-39.23788027501831,223.52438133002443
2023-01-18,10.968990345277774,234.4933716753022
2023-01-19,-17.519099732290336,216.97427194301187
2023-01-20,258.1249999999992,475.0992719430111
2023-01-21,-26.63800028548694,448.46127165752415
2023-01-22,-27.862031912052718,420.59923974547144
2023-01-23,-40.570948096946864,380.0282916485246
2023-01-24,153.52954075490175,533.5578324034263
2023-01-25,76.9770888682049,610.5349212716312
2023-01-26,-43.12500000000006,567.4099212716312
2023-01-27,4.574004296634158,571.9839255682654
2023-01-28,110.89323045990369,682.8771560281691
2023-01-29,65.39337129909306,748.2705273272621
2023-01-30,89.15573047359129,837.4262578008534
2023-01-31,-33.65661196432259,803.7696458365308
2023-02-01,-42.346269783529834,761.423376053001
2023-02-02,5.6249999999996945,767.0483760530007
2023-02-03,-38.015635992701625,729.032740060299
2023-02-04,-6.061609860875804,722.9711301994232
2023-02-05,91.32767729532108,814.2988074947443
2023-02-06,12.340770370556534,826.6395778653009
2023-02-07,-38.31565728321921,788.3239205820817
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2023-12-19,25.118359089859197,8698.012436528525
2023-12-20,-43.080721035673186,8654.931715492852
2023-12-21,-31.737797271451193,8623.193918221401
2023-12-22,47.49999999999983,8670.693918221401
2023-12-23,20.47855147595199,8691.172469697352
2023-12-24,108.453203178383,8799.625672875736
2023-12-25,-34.74504529363933,8764.880627582097
2023-12-26,104.99999999999984,8869.880627582097
2023-12-27,204.66554953165667,9074.546177113754
2023-12-28,-5.606769621811815,9068.939407491942
2023-12-29,220.95172689013637,9289.891134382078
2023-12-30,-15.105228436109885,9274.785905945968
2023-12-31,-0.005881281931285898,9274.780024664036
1 date pnl cumulative_pnl
2 2023-01-01 -13.24030780391167 -13.24030780391167
3 2023-01-02 -6.446144625588336 -19.686452429500005
4 2023-01-03 1.604645378778668 -18.081807050721338
5 2023-01-04 56.24999999999943 38.168192949278094
6 2023-01-05 0.33406192478092844 38.50225487405902
7 2023-01-06 -4.28936549280675 34.21288938125227
8 2023-01-07 14.786004985419915 48.99889436667218
9 2023-01-08 74.57347500987392 123.5723693765461
10 2023-01-09 100.43932814185042 224.01169751839655
11 2023-01-10 -11.400950315324355 212.61074720307218
12 2023-01-11 119.73808656927638 332.34883377234854
13 2023-01-12 -46.87500000000008 285.4738337723485
14 2023-01-13 24.95301706493165 310.42685083728014
15 2023-01-14 36.87499999999923 347.3018508372794
16 2023-01-15 -42.164085212923396 305.137765624356
17 2023-01-16 -42.37550401931327 262.76226160504274
18 2023-01-17 -39.23788027501831 223.52438133002443
19 2023-01-18 10.968990345277774 234.4933716753022
20 2023-01-19 -17.519099732290336 216.97427194301187
21 2023-01-20 258.1249999999992 475.0992719430111
22 2023-01-21 -26.63800028548694 448.46127165752415
23 2023-01-22 -27.862031912052718 420.59923974547144
24 2023-01-23 -40.570948096946864 380.0282916485246
25 2023-01-24 153.52954075490175 533.5578324034263
26 2023-01-25 76.9770888682049 610.5349212716312
27 2023-01-26 -43.12500000000006 567.4099212716312
28 2023-01-27 4.574004296634158 571.9839255682654
29 2023-01-28 110.89323045990369 682.8771560281691
30 2023-01-29 65.39337129909306 748.2705273272621
31 2023-01-30 89.15573047359129 837.4262578008534
32 2023-01-31 -33.65661196432259 803.7696458365308
33 2023-02-01 -42.346269783529834 761.423376053001
34 2023-02-02 5.6249999999996945 767.0483760530007
35 2023-02-03 -38.015635992701625 729.032740060299
36 2023-02-04 -6.061609860875804 722.9711301994232
37 2023-02-05 91.32767729532108 814.2988074947443
38 2023-02-06 12.340770370556534 826.6395778653009
39 2023-02-07 -38.31565728321921 788.3239205820817
40 2023-02-08 -10.754771409427375 777.5691491726543
41 2023-02-09 180.00000000000034 957.5691491726546
42 2023-02-10 90.62500000000017 1048.1941491726548
43 2023-02-11 -40.23338038017373 1007.9607687924811
44 2023-02-12 50.843005130216255 1058.8037739226972
45 2023-02-13 -43.12499999999999 1015.6787739226972
46 2023-02-14 -45.000000000000014 970.6787739226972
47 2023-02-15 211.8749999999989 1182.553773922696
48 2023-02-16 123.74999999999923 1306.3037739226954
49 2023-02-17 -43.1250000000002 1263.1787739226952
50 2023-02-18 -30.38874055427764 1232.7900333684177
51 2023-02-19 2.5357914472640948 1235.3258248156817
52 2023-02-20 10.13433097511785 1245.4601557907995
53 2023-02-21 41.5960974135976 1287.056253204397
54 2023-02-22 39.9999999999999 1327.056253204397
55 2023-02-23 -13.478092269295054 1313.578160935102
56 2023-02-24 -33.61127571949922 1279.9668852156028
57 2023-02-25 56.87500000000026 1336.841885215603
58 2023-02-26 52.96295237320025 1389.8048375888034
59 2023-02-27 -21.012957685691507 1368.7918799031117
60 2023-02-28 -17.510525954953838 1351.2813539481579
61 2023-03-01 48.43861521106629 1399.719969159224
62 2023-03-02 5.370115987703678 1405.0900851469278
63 2023-03-03 114.81346298981391 1519.9035481367416
64 2023-03-04 -9.975555145718014 1509.9279929910235
65 2023-03-05 -40.070176144315376 1469.8578168467081
66 2023-03-06 -11.368260314393366 1458.4895565323147
67 2023-03-07 29.800169999101556 1488.2897265314164
68 2023-03-08 -41.066516251938125 1447.2232102794783
69 2023-03-09 178.12499999999966 1625.3482102794778
70 2023-03-10 69.54272408495805 1694.890934364436
71 2023-03-11 218.8441938252083 1913.7351281896442
72 2023-03-12 -43.124999999999986 1870.6101281896442
73 2023-03-13 31.874999999999282 1902.4851281896435
74 2023-03-14 71.24999999999882 1973.7351281896424
75 2023-03-15 -46.87499999999994 1926.8601281896424
76 2023-03-16 -47.19007322239124 1879.6700549672512
77 2023-03-17 249.37499999999858 2129.0450549672496
78 2023-03-18 -39.375000000000455 2089.670054967249
79 2023-03-19 -37.72054430463878 2051.9495106626105
80 2023-03-20 -48.125000000000256 2003.8245106626102
81 2023-03-21 -48.955919791756116 1954.8685908708542
82 2023-03-22 -41.34851470243514 1913.520076168419
83 2023-03-23 -40.53293708055611 1872.987139087863
84 2023-03-24 -40.62177198705648 1832.3653671008065
85 2023-03-25 -12.808008186129763 1819.5573589146768
86 2023-03-26 44.60456842447671 1864.1619273391534
87 2023-03-27 33.10077209532424 1897.2626994344776
88 2023-03-28 -40.54590870106257 1856.7167907334149
89 2023-03-29 32.68290319879213 1889.399693932207
90 2023-03-30 -42.18093929678437 1847.2187546354226
91 2023-03-31 -30.18435407486843 1817.034400560554
92 2023-04-01 -18.629080327821725 1798.4053202327323
93 2023-04-02 78.36987927566116 1876.7751995083934
94 2023-04-03 -14.310148754581672 1862.4650507538117
95 2023-04-04 67.49999999999953 1929.9650507538113
96 2023-04-05 40.981623971671745 1970.946674725483
97 2023-04-06 27.071471993660218 1998.0181467191433
98 2023-04-07 69.95707277743023 2067.9752194965736
99 2023-04-08 16.74780048539444 2084.723019981968
100 2023-04-09 47.28635900754121 2132.009378989509
101 2023-04-10 92.25687955666356 2224.2662585461726
102 2023-04-11 13.118453933996253 2237.3847124801687
103 2023-04-12 56.24999999999995 2293.6347124801687
104 2023-04-13 179.99999999999943 2473.634712480168
105 2023-04-14 -21.87500000000091 2451.7597124801673
106 2023-04-15 0.6060490696602718 2452.3657615498278
107 2023-04-16 69.62762541934998 2521.9933869691777
108 2023-04-17 65.86212708679903 2587.8555140559765
109 2023-04-18 11.498179506746062 2599.3536935627226
110 2023-04-19 223.1815368848387 2822.5352304475614
111 2023-04-20 -43.12499999999999 2779.4102304475614
112 2023-04-21 31.875000000000142 2811.2852304475614
113 2023-04-22 -29.84738767205142 2781.43784277551
114 2023-04-23 13.154733757123939 2794.5925765326338
115 2023-04-24 6.396324986843672 2800.9889015194776
116 2023-04-25 123.74011330692568 2924.7290148264033
117 2023-04-26 261.41402100210826 3186.1430358285115
118 2023-04-27 24.10664119340748 3210.249677021919
119 2023-04-28 -10.62612672957838 3199.6235502923405
120 2023-04-29 23.597281085864836 3223.2208313782053
121 2023-04-30 -44.289545693205795 3178.9312856849997
122 2023-05-01 54.37500000000002 3233.3062856849997
123 2023-05-02 -2.017144354733148 3231.2891413302664
124 2023-05-03 81.77174013147513 3313.0608814617417
125 2023-05-04 -25.04485318862535 3288.0160282731163
126 2023-05-05 171.61321250444536 3459.629240777562
127 2023-05-06 52.49999999999993 3512.129240777562
128 2023-05-07 -33.168505868798924 3478.960734908763
129 2023-05-08 8.124999999999917 3487.085734908763
130 2023-05-09 -12.584917211039063 3474.5008176977235
131 2023-05-10 52.019173246963206 3526.519990944687
132 2023-05-11 90.00000000000003 3616.519990944687
133 2023-05-12 126.17158084605737 3742.691571790744
134 2023-05-13 8.545669463922014 3751.237241254666
135 2023-05-14 1.8924095106008352 3753.129650765267
136 2023-05-15 10.943496294234567 3764.0731470595015
137 2023-05-16 -30.264218933238727 3733.808928126263
138 2023-05-17 30.026157944387133 3763.83508607065
139 2023-05-18 23.13394310654973 3786.9690291772
140 2023-05-19 15.149354042131993 3802.118383219332
141 2023-05-20 -16.41923661401725 3785.6991466053146
142 2023-05-21 7.846127328452241 3793.545273933767
143 2023-05-22 21.279945465818493 3814.8252193995854
144 2023-05-23 70.02437426214179 3884.8495936617273
145 2023-05-24 72.70217181804935 3957.551765479777
146 2023-05-25 -16.75708025521019 3940.794685224567
147 2023-05-26 18.74999999999976 3959.5446852245664
148 2023-05-27 39.20117578898695 3998.745861013553
149 2023-05-28 110.91041187532412 4109.6562728888775
150 2023-05-29 -32.49999999999996 4077.1562728888775
151 2023-05-30 10.449185839751497 4087.605458728629
152 2023-05-31 -10.136360635569764 4077.469098093059
153 2023-06-01 14.358833920817762 4091.8279320138768
154 2023-06-02 65.90793376617691 4157.735865780053
155 2023-06-03 2.796329514545665 4160.532195294599
156 2023-06-04 -6.651917557240656 4153.8802777373585
157 2023-06-05 168.7500000000002 4322.6302777373585
158 2023-06-06 -43.12500000000005 4279.5052777373585
159 2023-06-07 34.36109954050025 4313.866377277859
160 2023-06-08 -25.452505493132982 4288.413871784726
161 2023-06-09 7.08099194612123 4295.494863730848
162 2023-06-10 121.25000000000003 4416.744863730848
163 2023-06-11 -20.937848671894088 4395.807015058954
164 2023-06-12 -21.37821245611659 4374.428802602837
165 2023-06-13 -13.703676058378367 4360.725126544458
166 2023-06-14 166.27160175206745 4526.996728296526
167 2023-06-15 15.069476387286578 4542.066204683812
168 2023-06-16 106.96240346155682 4649.02860814537
169 2023-06-17 -11.250000000000341 4637.77860814537
170 2023-06-18 -10.120078428463955 4627.658529716906
171 2023-06-19 -38.97345934789618 4588.68507036901
172 2023-06-20 42.86461601305588 4631.549686382065
173 2023-06-21 187.49999999999937 4819.0496863820645
174 2023-06-22 31.80896255236462 4850.858648934429
175 2023-06-23 23.85220828852076 4874.71085722295
176 2023-06-24 -30.371879505034936 4844.338977717915
177 2023-06-25 12.45391150222293 4856.792889220138
178 2023-06-26 78.4973773289051 4935.290266549043
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180 2023-06-28 88.12499999999999 4983.544843824217
181 2023-06-29 -40.51516243372697 4943.029681390491
182 2023-06-30 105.46931766571112 5048.498999056202
183 2023-07-01 -13.796315557439133 5034.702683498763
184 2023-07-02 -41.59344453195906 4993.109238966804
185 2023-07-03 28.89550853439942 5022.004747501203
186 2023-07-04 -5.3332824068334626 5016.6714650943695
187 2023-07-05 90.00000000000007 5106.6714650943695
188 2023-07-06 65.47404542743519 5172.145510521805
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190 2023-07-08 2.0825098762478143 5165.852373921327
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192 2023-07-10 4.7069840687455775 5158.275096400855
193 2023-07-11 29.744227743509978 5188.019324144365
194 2023-07-12 13.786259374056783 5201.805583518421
195 2023-07-13 179.07533869993108 5380.880922218353
196 2023-07-14 74.05844966531129 5454.939371883664
197 2023-07-15 -24.570748403829185 5430.368623479834
198 2023-07-16 6.803094664210427 5437.1717181440445
199 2023-07-17 27.410515235666534 5464.582233379711
200 2023-07-18 4.897911857902959 5469.480145237614
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202 2023-07-20 41.93544480774592 5517.7242849008135
203 2023-07-21 16.096325607887692 5533.820610508701
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209 2023-07-27 29.206711819629362 5573.2922491065265
210 2023-07-28 13.812242058805946 5587.104491165333
211 2023-07-29 11.977355224058611 5599.081846389391
212 2023-07-30 -5.150633707859324 5593.931212681532
213 2023-07-31 -31.586985170360826 5562.344227511171
214 2023-08-01 34.57747987841326 5596.921707389584
215 2023-08-02 19.53988833101348 5616.461595720598
216 2023-08-03 26.963711901014904 5643.425307621613
217 2023-08-04 0.4840259877677071 5643.90933360938
218 2023-08-05 8.851832296486007 5652.761165905866
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231 2023-08-18 20.625000000000128 5918.347466924756
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235 2023-08-22 113.75000000000014 5958.038221769955
236 2023-08-23 8.70637062854804 5966.744592398503
237 2023-08-24 22.993336529216194 5989.73792892772
238 2023-08-25 -41.837123197460336 5947.900805730259
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253 2023-09-09 -30.94267544057865 6061.99346908873
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255 2023-09-11 146.25000000000028 6261.534717016701
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263 2023-09-19 4.273043833846757 6256.281336144269
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265 2023-09-21 90.00000000000011 6370.523525324299
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270 2023-09-26 19.48204133300013 6287.064229309671
271 2023-09-27 56.24999999999976 6343.314229309671
272 2023-09-28 109.05931606292351 6452.373545372594
273 2023-09-29 -3.7500000000000906 6448.623545372594
274 2023-09-30 34.11899846667325 6482.7425438392675
275 2023-10-01 94.78119432198413 6577.5237381612515
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282 2023-10-08 -2.133334555954726 6480.018637710384
283 2023-10-09 120.94752554130996 6600.966163251694
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285 2023-10-11 12.918899632245676 6583.3298861976145
286 2023-10-12 86.41945469513863 6669.749340892753
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292 2023-10-18 -26.10871664471985 6685.007896814361
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294 2023-10-20 104.9999999999995 6817.139614123716
295 2023-10-21 95.53563465609497 6912.67524877981
296 2023-10-22 -39.07986275419109 6873.5953860256195
297 2023-10-23 159.37499999999892 7032.970386025619
298 2023-10-24 125.89084826115247 7158.861234286771
299 2023-10-25 -39.7457464430455 7119.1154878437255
300 2023-10-26 76.84599913941024 7195.961486983136
301 2023-10-27 -40.93958561285608 7155.02190137028
302 2023-10-28 -0.12582720298443117 7154.896074167295
303 2023-10-29 1.7653990903884869 7156.661473257684
304 2023-10-30 14.80106712479257 7171.462540382477
305 2023-10-31 -0.7859893532677678 7170.676551029209
306 2023-11-01 45.63294697011628 7216.309497999325
307 2023-11-02 66.38708795013648 7282.696585949461
308 2023-11-03 7.818102574679843 7290.514688524141
309 2023-11-04 78.74999999999962 7369.264688524141
310 2023-11-05 93.530612820923 7462.795301345064
311 2023-11-06 -8.841261133494985 7453.954040211569
312 2023-11-07 13.587699262662081 7467.541739474231
313 2023-11-08 -27.39298530178767 7440.148754172443
314 2023-11-09 233.65340882150477 7673.802162993948
315 2023-11-10 -43.1250000000002 7630.677162993948
316 2023-11-11 65.04467136509504 7695.721834359043
317 2023-11-12 -24.51389709666313 7671.20793726238
318 2023-11-13 6.381878610566607 7677.589815872947
319 2023-11-14 124.7302217309696 7802.320037603917
320 2023-11-15 -15.4428344502457 7786.877203153671
321 2023-11-16 22.788163027994152 7809.665366181665
322 2023-11-17 -40.58878257444296 7769.076583607222
323 2023-11-18 10.05941544996627 7779.135999057188
324 2023-11-19 77.18072310694912 7856.316722164137
325 2023-11-20 -33.719119790508586 7822.597602373628
326 2023-11-21 -39.39362250328517 7783.203979870344
327 2023-11-22 62.49999999999903 7845.703979870343
328 2023-11-23 -43.750000000000135 7801.953979870343
329 2023-11-24 43.12499999999979 7845.078979870343
330 2023-11-25 2.7372815485791326 7847.816261418921
331 2023-11-26 -19.434424922416646 7828.381836496505
332 2023-11-27 74.22705085378658 7902.608887350291
333 2023-11-28 -21.10900157778203 7881.4998857725095
334 2023-11-29 1.584413397259107 7883.084299169768
335 2023-11-30 -5.586662127245132 7877.497637042523
336 2023-12-01 68.65652016602549 7946.154157208548
337 2023-12-02 116.24999999999937 8062.404157208547
338 2023-12-03 48.229742583832405 8110.633899792379
339 2023-12-04 13.529045410407036 8124.162945202786
340 2023-12-05 93.37552564770242 8217.538470850488
341 2023-12-06 -39.37279199427863 8178.1656788562095
342 2023-12-07 160.37637314576938 8338.54205200198
343 2023-12-08 -32.94183697847613 8305.600215023504
344 2023-12-09 -22.342388616671474 8283.257826406832
345 2023-12-10 -29.00075720912898 8254.257069197703
346 2023-12-11 112.22981231135489 8366.486881509058
347 2023-12-12 -46.76135746492969 8319.725524044128
348 2023-12-13 102.21323939340017 8421.938763437529
349 2023-12-14 33.74999999999983 8455.688763437529
350 2023-12-15 63.75916511664332 8519.447928554173
351 2023-12-16 -32.300370998108704 8487.147557556063
352 2023-12-17 17.066032222984127 8504.213589779047
353 2023-12-18 168.68048765961714 8672.894077438665
354 2023-12-19 25.118359089859197 8698.012436528525
355 2023-12-20 -43.080721035673186 8654.931715492852
356 2023-12-21 -31.737797271451193 8623.193918221401
357 2023-12-22 47.49999999999983 8670.693918221401
358 2023-12-23 20.47855147595199 8691.172469697352
359 2023-12-24 108.453203178383 8799.625672875736
360 2023-12-25 -34.74504529363933 8764.880627582097
361 2023-12-26 104.99999999999984 8869.880627582097
362 2023-12-27 204.66554953165667 9074.546177113754
363 2023-12-28 -5.606769621811815 9068.939407491942
364 2023-12-29 220.95172689013637 9289.891134382078
365 2023-12-30 -15.105228436109885 9274.785905945968
366 2023-12-31 -0.005881281931285898 9274.780024664036

3657
strategy/backtest_2023_trades.csv
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298
strategy/backtest_engine.py
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@@ -1,298 +0,0 @@
"""
回测引擎 - 完整模拟手续费、返佣延迟到账、每日回撤限制、持仓时间约束
支持同时持有多单并发，严格控制每日最大回撤
"""
import datetime
import numpy as np
import pandas as pd
from dataclasses import dataclass
from typing import List, Optional
@dataclass
class Trade:
entry_time: pd.Timestamp
exit_time: Optional[pd.Timestamp] = None
direction: int = 0
entry_price: float = 0.0
exit_price: float = 0.0
pnl: float = 0.0
fee: float = 0.0
rebate: float = 0.0
holding_bars: int = 0
@dataclass
class OpenPosition:
direction: int = 0
entry_price: float = 0.0
entry_time: pd.Timestamp = None
hold_bars: int = 0
class BacktestEngine:
def __init__(
self,
initial_capital: float = 1000.0,
margin_per_trade: float = 25.0,
leverage: int = 50,
fee_rate: float = 0.0005,
rebate_ratio: float = 0.70,
max_daily_drawdown: float = 50.0,
min_hold_bars: int = 1,
stop_loss_pct: float = 0.005,
take_profit_pct: float = 0.01,
max_positions: int = 3,
):
self.initial_capital = initial_capital
self.margin = margin_per_trade
self.leverage = leverage
self.notional = margin_per_trade * leverage
self.fee_rate = fee_rate
self.rebate_ratio = rebate_ratio
self.max_daily_dd = max_daily_drawdown
self.min_hold_bars = min_hold_bars
self.sl_pct = stop_loss_pct
self.tp_pct = take_profit_pct
self.max_positions = max_positions
def _close_position(self, pos, exit_price, t, today, trades, pending_rebates):
"""平仓一个持仓，返回 net_pnl"""
qty = self.notional / pos.entry_price
if pos.direction == 1:
raw_pnl = qty * (exit_price - pos.entry_price)
else:
raw_pnl = qty * (pos.entry_price - exit_price)
close_fee = self.notional * self.fee_rate
net_pnl = raw_pnl - close_fee
total_fee = self.notional * self.fee_rate * 2
rebate = total_fee * self.rebate_ratio
rebate_date = today + datetime.timedelta(days=1)
pending_rebates.append((rebate_date, rebate))
trades.append(Trade(
entry_time=pos.entry_time, exit_time=t,
direction=pos.direction, entry_price=pos.entry_price,
exit_price=exit_price, pnl=net_pnl, fee=total_fee,
rebate=rebate, holding_bars=pos.hold_bars,
))
return net_pnl
def _worst_unrealized(self, positions, h, lo):
"""计算所有持仓在本K线内的最坏浮动亏损（用 high/low）"""
worst = 0.0
for pos in positions:
qty = self.notional / pos.entry_price
if pos.direction == 1:
# 多单最坏情况: 价格跌到 low
worst += qty * (lo - pos.entry_price)
else:
# 空单最坏情况: 价格涨到 high
worst += qty * (pos.entry_price - h)
return worst
def run(self, df: pd.DataFrame, score: pd.Series, open_threshold: float = 0.3) -> dict:
capital = self.initial_capital
trades: List[Trade] = []
daily_pnl = {}
pending_rebates = []
positions: List[OpenPosition] = []
used_margin = 0.0
current_date = None
day_pnl = 0.0
day_stopped = False
close_arr = df['close'].values
high_arr = df['high'].values
low_arr = df['low'].values
times = df.index
scores = score.values
for i in range(len(df)):
t = times[i]
c = close_arr[i]
h = high_arr[i]
lo = low_arr[i]
s = scores[i]
today = t.date()
# --- 日切换 ---
if today != current_date:
if current_date is not None:
daily_pnl[current_date] = day_pnl
current_date = today
day_pnl = 0.0
day_stopped = False
arrived = []
remaining = []
for rd, ra in pending_rebates:
if today >= rd:
arrived.append(ra)
else:
remaining.append((rd, ra))
if arrived:
capital += sum(arrived)
pending_rebates = remaining
if day_stopped:
for pos in positions:
pos.hold_bars += 1
continue
# --- 正常止损止盈逻辑 ---
closed_indices = []
for pi, pos in enumerate(positions):
pos.hold_bars += 1
qty = self.notional / pos.entry_price
if pos.direction == 1:
sl_price = pos.entry_price * (1 - self.sl_pct)
tp_price = pos.entry_price * (1 + self.tp_pct)
hit_sl = lo <= sl_price
hit_tp = h >= tp_price
else:
sl_price = pos.entry_price * (1 + self.sl_pct)
tp_price = pos.entry_price * (1 - self.tp_pct)
hit_sl = h >= sl_price
hit_tp = lo <= tp_price
should_close = False
exit_price = c
# 止损始终生效（不受持仓时间限制）
if hit_sl:
should_close = True
exit_price = sl_price
elif pos.hold_bars >= self.min_hold_bars:
# 止盈和信号反转需要满足最小持仓时间
if hit_tp:
should_close = True
exit_price = tp_price
elif (pos.direction == 1 and s < -open_threshold) or \
(pos.direction == -1 and s > open_threshold):
should_close = True
exit_price = c
if should_close:
net = self._close_position(pos, exit_price, t, today, trades, pending_rebates)
capital += net
used_margin -= self.margin
day_pnl += net
closed_indices.append(pi)
# 每笔平仓后立即检查日回撤
if day_pnl < -self.max_daily_dd:
# 熔断剩余持仓
for pj, pos2 in enumerate(positions):
if pj not in closed_indices:
pos2.hold_bars += 1
net2 = self._close_position(pos2, c, t, today, trades, pending_rebates)
capital += net2
used_margin -= self.margin
day_pnl += net2
closed_indices.append(pj)
day_stopped = True
break
for pi in sorted(set(closed_indices), reverse=True):
positions.pop(pi)
if day_stopped:
continue
# --- 开仓 ---
if len(positions) < self.max_positions:
if np.isnan(s):
continue
# 开仓前检查: 当前所有持仓 + 新仓同时止损的最大亏损
n_after = len(positions) + 1
worst_total_sl = n_after * (self.notional * self.sl_pct + self.notional * self.fee_rate * 2)
if day_pnl - worst_total_sl < -self.max_daily_dd:
continue # 风险敞口太大
open_fee = self.notional * self.fee_rate
if capital - used_margin < self.margin + open_fee:
continue
new_dir = 0
if s > open_threshold:
new_dir = 1
elif s < -open_threshold:
new_dir = -1
if new_dir != 0:
positions.append(OpenPosition(
direction=new_dir, entry_price=c,
entry_time=t, hold_bars=0,
))
capital -= open_fee
used_margin += self.margin
day_pnl -= open_fee
# 最后一天
if current_date is not None:
daily_pnl[current_date] = day_pnl
# 强制平仓
if positions and len(df) > 0:
last_close = close_arr[-1]
for pos in positions:
qty = self.notional / pos.entry_price
if pos.direction == 1:
raw_pnl = qty * (last_close - pos.entry_price)
else:
raw_pnl = qty * (pos.entry_price - last_close)
fee = self.notional * self.fee_rate
net_pnl = raw_pnl - fee
capital += net_pnl
trades.append(Trade(
entry_time=pos.entry_time, exit_time=times[-1],
direction=pos.direction, entry_price=pos.entry_price,
exit_price=last_close, pnl=net_pnl,
fee=self.notional * self.fee_rate * 2,
rebate=0, holding_bars=pos.hold_bars,
))
remaining_rebate = sum(amt for _, amt in pending_rebates)
capital += remaining_rebate
return self._build_result(trades, daily_pnl, capital)
def _build_result(self, trades, daily_pnl, final_capital):
if not trades:
return {
'total_pnl': 0, 'final_capital': final_capital,
'num_trades': 0, 'win_rate': 0, 'avg_pnl': 0,
'max_daily_dd': 0, 'avg_daily_pnl': 0,
'profit_factor': 0, 'trades': [], 'daily_pnl': daily_pnl,
'total_fee': 0, 'total_rebate': 0,
}
pnls = [t.pnl for t in trades]
wins = [p for p in pnls if p > 0]
losses = [p for p in pnls if p <= 0]
daily_vals = list(daily_pnl.values())
total_fee = sum(t.fee for t in trades)
total_rebate = sum(t.rebate for t in trades)
gross_profit = sum(wins) if wins else 0
gross_loss = abs(sum(losses)) if losses else 1e-10
return {
'total_pnl': sum(pnls) + total_rebate,
'final_capital': final_capital,
'num_trades': len(trades),
'win_rate': len(wins) / len(trades) if trades else 0,
'avg_pnl': np.mean(pnls),
'max_daily_dd': min(daily_vals) if daily_vals else 0,
'avg_daily_pnl': np.mean(daily_vals) if daily_vals else 0,
'profit_factor': gross_profit / gross_loss,
'total_fee': total_fee,
'total_rebate': total_rebate,
'trades': trades,
'daily_pnl': daily_pnl,
}

485
strategy/bb_backtest.py
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@@ -1,485 +0,0 @@
"""Bollinger Band mean-reversion strategy backtest.
Logic:
- Price touches upper BB → close any long, open short
- Price touches lower BB → close any short, open long
- Always in position (flip between long and short)
Uses 5-minute OHLC data from the database.
"""
from __future__ import annotations
from dataclasses import dataclass
from datetime import datetime, timezone
from typing import List, Optional
import numpy as np
import pandas as pd
from .indicators import bollinger
# ---------------------------------------------------------------------------
# Config & result types
# ---------------------------------------------------------------------------
@dataclass
class BBConfig:
# Bollinger Band parameters
bb_period: int = 20 # SMA window
bb_std: float = 2.0 # standard deviation multiplier
# Position sizing
margin_per_trade: float = 80.0
leverage: float = 100.0
initial_capital: float = 1000.0
# Risk management
max_daily_loss: float = 150.0 # stop trading after this daily loss
max_daily_loss_pct: float = 0.0 # if >0, daily loss limit = equity * pct (overrides fixed)
stop_loss_pct: float = 0.0 # 0 = disabled; e.g. 0.02 = 2% SL from entry
# Liquidation
liq_enabled: bool = True # enable liquidation simulation
cross_margin: bool = True # 全仓模式: 仅当 equity<=0 爆仓; False=逐仓: 按仓位保证金算强平价
maint_margin_rate: float = 0.005 # 逐仓时用: 0.5% 维持保证金率
# Slippage: applied to each trade execution price
slippage_pct: float = 0.0005 # 0.05% slippage per trade
# 成交价模式: False=理想(在触轨的极价成交), True=真实(在K线收盘价成交)
# 实盘检测到触及布林带后以市价单成交，通常接近收盘价
fill_at_close: bool = False
# Max single order notional (market capacity limit, USDT)
max_notional: float = 0.0 # 0 = unlimited; e.g. 500000 = 50万U max per order
# Dynamic sizing: if > 0, margin = equity * margin_pct (overrides margin_per_trade)
margin_pct: float = 0.0 # e.g. 0.01 = 1% of equity per trade
# Fee structure (taker)
fee_rate: float = 0.0006 # 0.06%
rebate_rate: float = 0.0 # instant maker rebate (if any)
# Delayed rebate: rebate_pct of daily fees returned next day at rebate_hour UTC
rebate_pct: float = 0.0 # e.g. 0.70 = 70% rebate
rebate_hour_utc: int = 0 # hour in UTC when rebate arrives (0 = 8am UTC+8)
# Pyramid (加仓): add to position on repeated same-direction BB touch
pyramid_enabled: bool = False
pyramid_decay: float = 0.99 # each add uses margin * decay^n (n = add count)
pyramid_max: int = 10 # max number of adds (0 = unlimited)
# Increment mode: each add uses equity * (margin_pct + pyramid_step * n)
# e.g. step=0.01 → 1st open=1%, 1st add=2%, 2nd add=3% ...
pyramid_step: float = 0.0 # 0 = use decay mode; >0 = use increment mode
@dataclass
class BBTrade:
side: str # "long" or "short"
entry_price: float
exit_price: float
entry_time: object # pd.Timestamp
exit_time: object
margin: float
leverage: float
qty: float
gross_pnl: float
fee: float
net_pnl: float
@dataclass
class BBResult:
equity_curve: pd.DataFrame # columns: equity, balance, price, position
trades: List[BBTrade]
daily_stats: pd.DataFrame # daily equity + pnl
total_fee: float
total_rebate: float
config: BBConfig
# ---------------------------------------------------------------------------
# Backtest engine
# ---------------------------------------------------------------------------
def run_bb_backtest(df: pd.DataFrame, cfg: BBConfig) -> BBResult:
"""Run Bollinger Band mean-reversion backtest on 5m OHLC data."""
close = df["close"].astype(float)
high = df["high"].astype(float)
low = df["low"].astype(float)
n = len(df)
# Compute Bollinger Bands
bb_mid, bb_upper, bb_lower, bb_width = bollinger(close, cfg.bb_period, cfg.bb_std)
# Convert to numpy for speed
arr_close = close.values
arr_high = high.values
arr_low = low.values
arr_upper = bb_upper.values
arr_lower = bb_lower.values
ts_index = df.index
# State
balance = cfg.initial_capital
position = 0 # +1 = long, -1 = short, 0 = flat
entry_price = 0.0
entry_time = None
entry_margin = 0.0
entry_qty = 0.0
pyramid_count = 0 # number of adds so far
last_add_margin = 0.0 # margin used in last open/add
trades: List[BBTrade] = []
total_fee = 0.0
total_rebate = 0.0
# Daily tracking
day_pnl = 0.0
day_stopped = False
current_day = None
day_start_equity = cfg.initial_capital # equity at start of each day
# Delayed rebate tracking
pending_rebate = 0.0 # fees from previous day to be rebated
today_fees = 0.0 # fees accumulated today
rebate_applied_today = False
# Output arrays
out_equity = np.full(n, np.nan)
out_balance = np.full(n, np.nan)
out_position = np.zeros(n)
def unrealised(price):
if position == 0:
return 0.0
if position == 1:
return entry_qty * (price - entry_price)
else:
return entry_qty * (entry_price - price)
def close_position(exit_price, exit_idx):
nonlocal balance, position, entry_price, entry_time, entry_margin, entry_qty
nonlocal total_fee, total_rebate, day_pnl, today_fees
nonlocal pyramid_count, last_add_margin
if position == 0:
return
# Apply slippage: closing long sells lower, closing short buys higher
if position == 1:
exit_price = exit_price * (1 - cfg.slippage_pct)
else:
exit_price = exit_price * (1 + cfg.slippage_pct)
if position == 1:
gross = entry_qty * (exit_price - entry_price)
else:
gross = entry_qty * (entry_price - exit_price)
exit_notional = entry_qty * exit_price
fee = exit_notional * cfg.fee_rate
rebate = exit_notional * cfg.rebate_rate # instant rebate only
net = gross - fee + rebate
trades.append(BBTrade(
side="long" if position == 1 else "short",
entry_price=entry_price,
exit_price=exit_price,
entry_time=entry_time,
exit_time=ts_index[exit_idx],
margin=entry_margin,
leverage=cfg.leverage,
qty=entry_qty,
gross_pnl=gross,
fee=fee,
net_pnl=net,
))
balance += net
total_fee += fee
total_rebate += rebate
today_fees += fee
day_pnl += net
position = 0
entry_price = 0.0
entry_time = None
entry_margin = 0.0
entry_qty = 0.0
pyramid_count = 0
last_add_margin = 0.0
def open_position(side, price, idx, is_add=False):
nonlocal position, entry_price, entry_time, entry_margin, entry_qty
nonlocal balance, total_fee, day_pnl, today_fees
nonlocal pyramid_count, last_add_margin
# Apply slippage: buy higher, sell lower
if side == "long" or (is_add and position == 1):
price = price * (1 + cfg.slippage_pct)
else:
price = price * (1 - cfg.slippage_pct)
if is_add and cfg.pyramid_step > 0:
# 递增加仓: margin = equity * (margin_pct + step * (count+1))
equity = balance + unrealised(price)
pct = cfg.margin_pct + cfg.pyramid_step * (pyramid_count + 1)
margin = equity * pct
elif is_add:
# 衰减加仓: margin = last_add_margin * decay
margin = last_add_margin * cfg.pyramid_decay
elif cfg.margin_pct > 0:
equity = balance + unrealised(price) if position != 0 else balance
margin = equity * cfg.margin_pct
else:
margin = cfg.margin_per_trade
margin = min(margin, balance * 0.95)
if margin <= 0:
return
notional = margin * cfg.leverage
# Cap notional to market capacity limit
if cfg.max_notional > 0 and notional > cfg.max_notional:
notional = cfg.max_notional
margin = notional / cfg.leverage
qty = notional / price
fee = notional * cfg.fee_rate
balance -= fee
total_fee += fee
today_fees += fee
day_pnl -= fee
if is_add and position != 0:
# 加仓: weighted average entry price
old_notional = entry_qty * entry_price
new_notional = qty * price
entry_qty += qty
entry_price = (old_notional + new_notional) / entry_qty
entry_margin += margin
pyramid_count += 1
last_add_margin = margin
else:
# 新开仓
position = 1 if side == "long" else -1
entry_price = price
entry_time = ts_index[idx]
entry_margin = margin
entry_qty = qty
pyramid_count = 0
last_add_margin = margin
# Main loop
for i in range(n):
# Daily reset + delayed rebate
bar_day = ts_index[i].date() if hasattr(ts_index[i], 'date') else None
bar_hour = ts_index[i].hour if hasattr(ts_index[i], 'hour') else 0
if bar_day is not None and bar_day != current_day:
# New day: move today's fees to pending, reset
if cfg.rebate_pct > 0:
pending_rebate += today_fees * cfg.rebate_pct
today_fees = 0.0
rebate_applied_today = False
day_start_equity = balance + unrealised(arr_close[i])
day_pnl = 0.0
day_stopped = False
current_day = bar_day
# Apply delayed rebate at specified hour
if cfg.rebate_pct > 0 and not rebate_applied_today and bar_hour >= cfg.rebate_hour_utc and pending_rebate > 0:
balance += pending_rebate
total_rebate += pending_rebate
pending_rebate = 0.0
rebate_applied_today = True
# Skip if BB not ready
if np.isnan(arr_upper[i]) or np.isnan(arr_lower[i]):
out_equity[i] = balance + unrealised(arr_close[i])
out_balance[i] = balance
out_position[i] = position
continue
# Daily loss check (percentage-based if configured, else fixed)
if day_stopped:
out_equity[i] = balance + unrealised(arr_close[i])
out_balance[i] = balance
out_position[i] = position
continue
cur_equity = balance + unrealised(arr_close[i])
if cfg.max_daily_loss_pct > 0:
# percentage-based: use start-of-day equity
daily_loss_limit = day_start_equity * cfg.max_daily_loss_pct
else:
daily_loss_limit = cfg.max_daily_loss
if day_pnl + unrealised(arr_close[i]) <= -daily_loss_limit:
close_position(arr_close[i], i)
day_stopped = True
out_equity[i] = balance
out_balance[i] = balance
out_position[i] = 0
continue
# Stop loss check
if position != 0 and cfg.stop_loss_pct > 0:
if position == 1 and arr_low[i] <= entry_price * (1 - cfg.stop_loss_pct):
sl_price = entry_price * (1 - cfg.stop_loss_pct)
close_position(sl_price, i)
elif position == -1 and arr_high[i] >= entry_price * (1 + cfg.stop_loss_pct):
sl_price = entry_price * (1 + cfg.stop_loss_pct)
close_position(sl_price, i)
# Liquidation check
# 全仓(cross_margin): 仅当 账户权益<=0 时爆仓，整仓担保
# 逐仓: 按仓位保证金算强平价
if position != 0 and cfg.liq_enabled and entry_margin > 0:
cur_equity = balance + unrealised(arr_close[i])
upnl = unrealised(arr_close[i])
if cfg.cross_margin:
# 全仓: 只有权益归零才爆仓
if cur_equity <= 0:
balance += upnl # 实现亏损
balance = max(0.0, balance)
day_pnl += upnl
trades.append(BBTrade(
side="long" if position == 1 else "short",
entry_price=entry_price, exit_price=arr_close[i],
entry_time=entry_time, exit_time=ts_index[i],
margin=entry_margin, leverage=cfg.leverage, qty=entry_qty,
gross_pnl=upnl, fee=0.0, net_pnl=upnl,
))
position = 0
entry_price = 0.0
entry_time = None
entry_margin = 0.0
entry_qty = 0.0
pyramid_count = 0
last_add_margin = 0.0
out_equity[i] = balance
out_balance[i] = balance
out_position[i] = 0
if balance <= 0:
out_equity[i:] = 0.0
out_balance[i:] = 0.0
break
else:
# 逐仓: 按强平价
liq_threshold = 1.0 / cfg.leverage * (1 - cfg.maint_margin_rate)
if position == 1:
liq_price = entry_price * (1 - liq_threshold)
if arr_low[i] <= liq_price:
balance -= entry_margin
day_pnl -= entry_margin
trades.append(BBTrade(
side="long", entry_price=entry_price, exit_price=liq_price,
entry_time=entry_time, exit_time=ts_index[i],
margin=entry_margin, leverage=cfg.leverage, qty=entry_qty,
gross_pnl=-entry_margin, fee=0.0, net_pnl=-entry_margin,
))
position = 0
entry_price = 0.0
entry_time = None
entry_margin = 0.0
entry_qty = 0.0
pyramid_count = 0
last_add_margin = 0.0
if balance <= 0:
balance = 0.0
out_equity[i] = 0.0
out_balance[i] = 0.0
out_position[i] = 0
out_equity[i:] = 0.0
out_balance[i:] = 0.0
break
elif position == -1:
liq_price = entry_price * (1 + liq_threshold)
if arr_high[i] >= liq_price:
balance -= entry_margin
day_pnl -= entry_margin
trades.append(BBTrade(
side="short", entry_price=entry_price, exit_price=liq_price,
entry_time=entry_time, exit_time=ts_index[i],
margin=entry_margin, leverage=cfg.leverage, qty=entry_qty,
gross_pnl=-entry_margin, fee=0.0, net_pnl=-entry_margin,
))
position = 0
entry_price = 0.0
entry_time = None
entry_margin = 0.0
entry_qty = 0.0
pyramid_count = 0
last_add_margin = 0.0
if balance <= 0:
balance = 0.0
out_equity[i:] = 0.0
out_balance[i:] = 0.0
break
# Signal detection: use high/low to check if price touched BB
touched_upper = arr_high[i] >= arr_upper[i]
touched_lower = arr_low[i] <= arr_lower[i]
# 成交价: fill_at_close=True 时用收盘价(模拟实盘市价单)，否则用触轨价(理想化)
fill_price = arr_close[i] if cfg.fill_at_close else None
if touched_upper and touched_lower:
# Both touched in same bar (wide bar) — skip, too volatile
pass
elif touched_upper:
# Price touched upper BB → go short
exec_price = fill_price if fill_price is not None else arr_upper[i]
if position == 1:
close_position(exec_price, i)
if position == 0:
open_position("short", exec_price, i)
elif position == -1 and cfg.pyramid_enabled:
can_add = cfg.pyramid_max <= 0 or pyramid_count < cfg.pyramid_max
if can_add:
open_position("short", exec_price, i, is_add=True)
elif touched_lower:
# Price touched lower BB → go long
exec_price = fill_price if fill_price is not None else arr_lower[i]
if position == -1:
close_position(exec_price, i)
if position == 0:
open_position("long", exec_price, i)
elif position == 1 and cfg.pyramid_enabled:
can_add = cfg.pyramid_max <= 0 or pyramid_count < cfg.pyramid_max
if can_add:
open_position("long", exec_price, i, is_add=True)
# Record equity
out_equity[i] = balance + unrealised(arr_close[i])
out_balance[i] = balance
out_position[i] = position
# Force close at end
if position != 0:
close_position(arr_close[n - 1], n - 1)
out_equity[n - 1] = balance
out_balance[n - 1] = balance
out_position[n - 1] = 0
# Build equity DataFrame
eq_df = pd.DataFrame({
"equity": out_equity,
"balance": out_balance,
"price": arr_close,
"position": out_position,
}, index=ts_index)
# Daily stats
daily_eq = eq_df["equity"].resample("1D").last().dropna().to_frame("equity")
daily_eq["pnl"] = daily_eq["equity"].diff().fillna(0.0)
return BBResult(
equity_curve=eq_df,
trades=trades,
daily_stats=daily_eq,
total_fee=total_fee,
total_rebate=total_rebate,
config=cfg,
)

294
strategy/bb_midline_backtest.py
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@@ -1,294 +0,0 @@
"""布林带均线策略回测（优化版）
策略逻辑:
- 阳线 + 碰到布林带均线 → 开多（可选 1m 线过滤：先涨碰到才开）
- 持多: 碰到上轨 → 止盈（无下轨止损）
- 阴线 + 碰到布林带均线 → 平多开空（可选 1m：先跌碰到才开）
- 持空: 碰到下轨 → 止盈（无上轨止损）
全仓模式 | 200U | 1% 权益/单 | 万五手续费 | 90%返佣次日8点到账 | 100x杠杆
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import List, Optional
import numpy as np
import pandas as pd
from .indicators import bollinger
@dataclass
class BBMidlineConfig:
bb_period: int = 20
bb_std: float = 2.0
initial_capital: float = 200.0
margin_pct: float = 0.01
leverage: float = 100.0
cross_margin: bool = True
fee_rate: float = 0.0005
rebate_pct: float = 0.90
rebate_hour_utc: int = 0
slippage_pct: float = 0.0
fill_at_close: bool = True
# 是否用 1m 线判断「先涨碰到」/「先跌碰到」均线
use_1m_touch_filter: bool = True
# 主K线周期(分钟)，用于 1m 触及方向时的桶对齐，5/15/30
kline_step_min: int = 5
@dataclass
class BBTrade:
side: str
entry_price: float
exit_price: float
entry_time: object
exit_time: object
margin: float
leverage: float
qty: float
gross_pnl: float
fee: float
net_pnl: float
exit_reason: str
@dataclass
class BBMidlineResult:
equity_curve: pd.DataFrame
trades: List[BBTrade]
daily_stats: pd.DataFrame
total_fee: float
total_rebate: float
config: BBMidlineConfig
def run_bb_midline_backtest(
df: pd.DataFrame,
cfg: BBMidlineConfig,
df_1m: Optional[pd.DataFrame] = None,
arr_touch_dir_override: Optional[np.ndarray] = None,
) -> BBMidlineResult:
close = df["close"].astype(float)
high = df["high"].astype(float)
low = df["low"].astype(float)
open_ = df["open"].astype(float)
n = len(df)
bb_mid, bb_upper, bb_lower, _ = bollinger(close, cfg.bb_period, cfg.bb_std)
arr_mid = bb_mid.values
# 1m 触及方向：1=先涨碰到, -1=先跌碰到, 0=未碰到
arr_touch_dir = None
if arr_touch_dir_override is not None:
arr_touch_dir = np.asarray(arr_touch_dir_override, dtype=np.int32)
if len(arr_touch_dir) != n:
raise ValueError(f"arr_touch_dir_override 长度不匹配: {len(arr_touch_dir)} != {n}")
elif cfg.use_1m_touch_filter and df_1m is not None and len(df_1m) > 0:
from .data_loader import get_1m_touch_direction
arr_touch_dir = get_1m_touch_direction(df, df_1m, arr_mid, kline_step_min=cfg.kline_step_min)
arr_close = close.values
arr_high = high.values
arr_low = low.values
arr_open = open_.values
arr_upper = bb_upper.values
arr_lower = bb_lower.values
ts_index = df.index
balance = cfg.initial_capital
position = 0
entry_price = 0.0
entry_time = None
entry_margin = 0.0
entry_qty = 0.0
trades: List[BBTrade] = []
total_fee = 0.0
total_rebate = 0.0
day_pnl = 0.0
current_day = None
today_fees = 0.0
pending_rebate = 0.0
rebate_applied_today = False
out_equity = np.full(n, np.nan)
out_balance = np.full(n, np.nan)
out_position = np.zeros(n)
def unrealised(price):
if position == 0:
return 0.0
if position == 1:
return entry_qty * (price - entry_price)
return entry_qty * (entry_price - price)
def close_position(exit_price, exit_idx, reason: str):
nonlocal balance, position, entry_price, entry_time, entry_margin, entry_qty
nonlocal total_fee, total_rebate, day_pnl, today_fees
if position == 0:
return
if position == 1:
exit_price = exit_price * (1 - cfg.slippage_pct)
else:
exit_price = exit_price * (1 + cfg.slippage_pct)
if position == 1:
gross = entry_qty * (exit_price - entry_price)
else:
gross = entry_qty * (entry_price - exit_price)
exit_notional = entry_qty * exit_price
fee = exit_notional * cfg.fee_rate
net = gross - fee
trades.append(BBTrade(
side="long" if position == 1 else "short",
entry_price=entry_price,
exit_price=exit_price,
entry_time=entry_time,
exit_time=ts_index[exit_idx],
margin=entry_margin,
leverage=cfg.leverage,
qty=entry_qty,
gross_pnl=gross,
fee=fee,
net_pnl=net,
exit_reason=reason,
))
balance += net
total_fee += fee
today_fees += fee
day_pnl += net
position = 0
entry_price = 0.0
entry_time = None
entry_margin = 0.0
entry_qty = 0.0
def open_position(side, price, idx):
nonlocal position, entry_price, entry_time, entry_margin, entry_qty
nonlocal balance, total_fee, day_pnl, today_fees
if side == "long":
price = price * (1 + cfg.slippage_pct)
else:
price = price * (1 - cfg.slippage_pct)
equity = balance + unrealised(price) if position != 0 else balance
margin = equity * cfg.margin_pct
margin = min(margin, balance * 0.95)
if margin <= 0:
return False
notional = margin * cfg.leverage
qty = notional / price
fee = notional * cfg.fee_rate
balance -= fee
total_fee += fee
today_fees += fee
day_pnl -= fee
position = 1 if side == "long" else -1
entry_price = price
entry_time = ts_index[idx]
entry_margin = margin
entry_qty = qty
return True
for i in range(n):
bar_day = ts_index[i].date() if hasattr(ts_index[i], 'date') else None
bar_hour = ts_index[i].hour if hasattr(ts_index[i], 'hour') else 0
if bar_day is not None and bar_day != current_day:
pending_rebate += today_fees * cfg.rebate_pct
today_fees = 0.0
rebate_applied_today = False
day_pnl = 0.0
current_day = bar_day
if cfg.rebate_pct > 0 and not rebate_applied_today and bar_hour >= cfg.rebate_hour_utc and pending_rebate > 0:
balance += pending_rebate
total_rebate += pending_rebate
pending_rebate = 0.0
rebate_applied_today = True
if np.isnan(arr_upper[i]) or np.isnan(arr_lower[i]) or np.isnan(arr_mid[i]):
out_equity[i] = balance + unrealised(arr_close[i])
out_balance[i] = balance
out_position[i] = position
continue
fill_price = arr_close[i] if cfg.fill_at_close else None
bullish = arr_close[i] > arr_open[i]
bearish = arr_close[i] < arr_open[i]
# 碰到均线：K 线贯穿或触及 mid
touched_mid = arr_low[i] <= arr_mid[i] <= arr_high[i]
touched_upper = arr_high[i] >= arr_upper[i]
touched_lower = arr_low[i] <= arr_lower[i]
exec_upper = fill_price if fill_price is not None else arr_upper[i]
exec_lower = fill_price if fill_price is not None else arr_lower[i]
# 1m 过滤：开多需先涨碰到，开空需先跌碰到
touch_up_ok = True if arr_touch_dir is None else (arr_touch_dir[i] == 1)
touch_down_ok = True if arr_touch_dir is None else (arr_touch_dir[i] == -1)
# 单根 K 线只允许一次操作
if position == 1 and touched_upper:
# 持多止盈
close_position(exec_upper, i, "tp_upper")
elif position == -1 and touched_lower:
# 持空止盈
close_position(exec_lower, i, "tp_lower")
elif position == 1 and bearish and touched_mid and touch_down_ok:
# 阴线触中轨: 平多并反手开空
close_position(arr_close[i], i, "flip_to_short")
if balance > 0:
open_position("short", arr_close[i], i)
elif position == -1 and bullish and touched_mid and touch_up_ok:
# 阳线触中轨: 平空并反手开多
close_position(arr_close[i], i, "flip_to_long")
if balance > 0:
open_position("long", arr_close[i], i)
elif position == 0 and bullish and touched_mid and touch_up_ok:
# 空仓开多
open_position("long", arr_close[i], i)
elif position == 0 and bearish and touched_mid and touch_down_ok:
# 空仓开空
open_position("short", arr_close[i], i)
out_equity[i] = balance + unrealised(arr_close[i])
out_balance[i] = balance
out_position[i] = position
if position != 0:
close_position(arr_close[n - 1], n - 1, "end")
out_equity[n - 1] = balance
out_balance[n - 1] = balance
out_position[n - 1] = 0
eq_df = pd.DataFrame({
"equity": out_equity,
"balance": out_balance,
"price": arr_close,
"position": out_position,
}, index=ts_index)
daily_eq = eq_df["equity"].resample("1D").last().dropna().to_frame("equity")
daily_eq["pnl"] = daily_eq["equity"].diff().fillna(0.0)
return BBMidlineResult(
equity_curve=eq_df,
trades=trades,
daily_stats=daily_eq,
total_fee=total_fee,
total_rebate=total_rebate,
config=cfg,
)

53
strategy/best_params_2020_2022.json
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@@ -1,53 +0,0 @@
{
"bb_period": 36,
"bb_std": 3.3000000000000003,
"kc_period": 24,
"kc_mult": 1.3,
"dc_period": 41,
"ema_fast": 3,
"ema_slow": 15,
"macd_fast": 9,
"macd_slow": 34,
"macd_signal": 15,
"adx_period": 16,
"st_period": 5,
"st_mult": 1.4,
"rsi_period": 7,
"stoch_k": 18,
"stoch_d": 6,
"stoch_smooth": 3,
"cci_period": 12,
"wr_period": 9,
"wma_period": 47,
"bb_oversold": -0.19999999999999998,
"bb_overbought": 1.3,
"kc_oversold": 0.2,
"kc_overbought": 0.75,
"dc_oversold": 0.05,
"dc_overbought": 0.75,
"adx_threshold": 15.0,
"rsi_overbought": 70.0,
"rsi_oversold": 18.0,
"stoch_overbought": 89.0,
"stoch_oversold": 10.0,
"cci_overbought": 80.0,
"cci_oversold": -140.0,
"wr_overbought": -28.0,
"wr_oversold": -90.0,
"w_bb": 0.15000000000000002,
"w_kc": 0.4,
"w_dc": 0.0,
"w_ema": 0.8500000000000001,
"w_macd": 0.35000000000000003,
"w_adx": 0.0,
"w_st": 0.15000000000000002,
"w_rsi": 0.4,
"w_stoch": 0.15000000000000002,
"w_cci": 0.1,
"w_wr": 0.0,
"w_wma": 0.4,
"open_threshold": 0.22,
"max_positions": 3,
"take_profit_pct": 0.024999999999999998,
"stop_loss_pct": 0.008
}

125
strategy/data_loader.py
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@@ -1,125 +0,0 @@
"""
数据加载模块 - 从 SQLite 加载多周期K线数据为 DataFrame
"""
import numpy as np
import pandas as pd
from peewee import SqliteDatabase
from pathlib import Path
DB_PATH = Path(__file__).parent.parent / 'models' / 'database.db'
# 周期 -> 表名 (bitmart / binance)
PERIOD_MAP = {
'1s': 'bitmart_eth_1s',
'1m': 'bitmart_eth_1m',
'3m': 'bitmart_eth_3m',
'5m': 'bitmart_eth_5m',
'15m': 'bitmart_eth_15m',
'30m': 'bitmart_eth_30m',
'1h': 'bitmart_eth_1h',
}
BINANCE_PERIOD_MAP = {
'1s': 'binance_eth_1s',
'1m': 'binance_eth_1m',
'3m': 'binance_eth_3m',
'5m': 'binance_eth_5m',
'15m': 'binance_eth_15m',
'30m': 'binance_eth_30m',
'1h': 'binance_eth_1h',
}
def load_klines(period: str, start_date: str, end_date: str, tz: str | None = None,
source: str = "bitmart") -> pd.DataFrame:
"""
加载指定周期、指定日期范围的K线数据
:param period: '1s','1m','3m','5m','15m','30m','1h'
:param start_date: 'YYYY-MM-DD'
:param end_date: 'YYYY-MM-DD' (不包含该日)
:param tz: 日期解释的时区，如 'Asia/Shanghai' 表示按北京时间；None 则用本地时区
:return: DataFrame with columns: datetime, open, high, low, close
"""
period_map = BINANCE_PERIOD_MAP if source == "binance" else PERIOD_MAP
table = period_map.get(period)
if not table:
raise ValueError(f"不支持的周期: {period}, 可选: {list(PERIOD_MAP.keys())}")
if tz:
start_ts = int(pd.Timestamp(start_date, tz=tz).timestamp() * 1000)
end_ts = int(pd.Timestamp(end_date, tz=tz).timestamp() * 1000)
else:
start_ts = int(pd.Timestamp(start_date).timestamp() * 1000)
end_ts = int(pd.Timestamp(end_date).timestamp() * 1000)
db = SqliteDatabase(str(DB_PATH))
db.connect()
cursor = db.execute_sql(
f'SELECT id, open, high, low, close FROM [{table}] '
f'WHERE id >= ? AND id < ? ORDER BY id',
(start_ts, end_ts)
)
rows = cursor.fetchall()
db.close()
df = pd.DataFrame(rows, columns=['timestamp_ms', 'open', 'high', 'low', 'close'])
df['datetime'] = pd.to_datetime(df['timestamp_ms'], unit='ms')
df.set_index('datetime', inplace=True)
df.drop(columns=['timestamp_ms'], inplace=True)
df = df.astype(float)
return df
def load_multi_period(periods: list, start_date: str, end_date: str, source: str = "bitmart") -> dict:
"""
加载多个周期的数据
:return: {period: DataFrame}
"""
result = {}
for p in periods:
result[p] = load_klines(p, start_date, end_date, source=source)
print(f" 加载 {p}: {len(result[p])} 条 ({start_date} ~ {end_date})")
return result
def get_1m_touch_direction(df_5m: pd.DataFrame, df_1m: pd.DataFrame,
arr_mid: np.ndarray, kline_step_min: int = 5) -> np.ndarray:
"""
根据 1 分钟线判断每根 5m K 线「先涨碰到均线」还是「先跌碰到均线」。
返回: 1=先涨碰到(可开多), -1=先跌碰到(可开空), 0=未碰到或无法判断
"""
df_1m = df_1m.copy()
df_1m["_bucket"] = df_1m.index.floor(f"{kline_step_min}min")
# 5m 索引与 mid 对齐
mid_sr = pd.Series(arr_mid, index=df_5m.index)
touch_map: dict[pd.Timestamp, int] = {}
for bucket, grp in df_1m.groupby("_bucket", sort=True):
mid = mid_sr.get(bucket, np.nan)
if pd.isna(mid):
touch_map[bucket] = 0
continue
o = grp["open"].to_numpy(dtype=float)
h = grp["high"].to_numpy(dtype=float)
l_ = grp["low"].to_numpy(dtype=float)
touch = 0
for j in range(len(grp)):
if l_[j] <= mid <= h[j]:
touch = 1 if o[j] < mid else -1
break
touch_map[bucket] = touch
# 对齐到主周期 index
out = np.zeros(len(df_5m), dtype=np.int32)
for i, t in enumerate(df_5m.index):
out[i] = touch_map.get(t, 0)
return out
if __name__ == '__main__':
data = load_multi_period(['5m', '15m', '1h'], '2020-01-01', '2024-01-01')
for k, v in data.items():
print(f"{k}: {v.shape}, {v.index[0]} ~ {v.index[-1]}")

104
strategy/indicators.py
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@@ -1,104 +0,0 @@
from __future__ import annotations
import numpy as np
import pandas as pd
def ema(s: pd.Series, span: int) -> pd.Series:
return s.ewm(span=span, adjust=False).mean()
def rsi(close: pd.Series, period: int) -> pd.Series:
delta = close.diff()
up = delta.clip(lower=0.0)
down = (-delta).clip(lower=0.0)
roll_up = up.ewm(alpha=1 / period, adjust=False).mean()
roll_down = down.ewm(alpha=1 / period, adjust=False).mean()
rs = roll_up / roll_down.replace(0.0, np.nan)
return 100.0 - (100.0 / (1.0 + rs))
def atr(high: pd.Series, low: pd.Series, close: pd.Series, period: int) -> pd.Series:
prev_close = close.shift(1)
tr = pd.concat(
[
(high - low).abs(),
(high - prev_close).abs(),
(low - prev_close).abs(),
],
axis=1,
).max(axis=1)
return tr.ewm(alpha=1 / period, adjust=False).mean()
def bollinger(close: pd.Series, window: int, n_std: float):
mid = close.rolling(window=window, min_periods=window).mean()
std = close.rolling(window=window, min_periods=window).std(ddof=0)
upper = mid + n_std * std
lower = mid - n_std * std
width = (upper - lower) / mid
return mid, upper, lower, width
def macd(close: pd.Series, fast: int, slow: int, signal: int):
fast_ema = ema(close, fast)
slow_ema = ema(close, slow)
line = fast_ema - slow_ema
sig = ema(line, signal)
hist = line - sig
return line, sig, hist
def stochastic(high: pd.Series, low: pd.Series, close: pd.Series,
k_period: int = 14, d_period: int = 3):
"""Stochastic Oscillator (%K and %D)."""
lowest = low.rolling(window=k_period, min_periods=k_period).min()
highest = high.rolling(window=k_period, min_periods=k_period).max()
denom = highest - lowest
k = 100.0 * (close - lowest) / denom.replace(0.0, np.nan)
d = k.rolling(window=d_period, min_periods=d_period).mean()
return k, d
def cci(high: pd.Series, low: pd.Series, close: pd.Series,
period: int = 20) -> pd.Series:
"""Commodity Channel Index."""
tp = (high + low + close) / 3.0
sma = tp.rolling(window=period, min_periods=period).mean()
mad = tp.rolling(window=period, min_periods=period).apply(
lambda x: np.mean(np.abs(x - np.mean(x))), raw=True
)
return (tp - sma) / (0.015 * mad.replace(0.0, np.nan))
def adx(high: pd.Series, low: pd.Series, close: pd.Series,
period: int = 14) -> pd.Series:
"""Average Directional Index (returns ADX line only)."""
up_move = high.diff()
down_move = -low.diff()
plus_dm = pd.Series(np.where((up_move > down_move) & (up_move > 0), up_move, 0.0),
index=high.index)
minus_dm = pd.Series(np.where((down_move > up_move) & (down_move > 0), down_move, 0.0),
index=high.index)
atr_val = atr(high, low, close, period)
plus_di = 100.0 * plus_dm.ewm(alpha=1 / period, adjust=False).mean() / atr_val.replace(0.0, np.nan)
minus_di = 100.0 * minus_dm.ewm(alpha=1 / period, adjust=False).mean() / atr_val.replace(0.0, np.nan)
dx = 100.0 * (plus_di - minus_di).abs() / (plus_di + minus_di).replace(0.0, np.nan)
adx_line = dx.ewm(alpha=1 / period, adjust=False).mean()
return adx_line
def keltner_channel(high: pd.Series, low: pd.Series, close: pd.Series,
ema_period: int = 20, atr_period: int = 14, atr_mult: float = 1.5):
"""Keltner Channel (mid, upper, lower)."""
mid = ema(close, ema_period)
atr_val = atr(high, low, close, atr_period)
upper = mid + atr_mult * atr_val
lower = mid - atr_mult * atr_val
return mid, upper, lower

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datetime,equity,balance,price,position
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2025-02-27 06:35:00,234.50431781576518,239.76764820152863,2359.22,-1.0
2025-02-27 06:40:00,236.04706332876694,239.76764820152863,2354.18,-1.0
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2025-02-27 06:55:00,238.009166253041,239.76764820152863,2347.77,-1.0
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2025-02-27 07:10:00,237.96019020500924,239.76764820152863,2347.93,-1.0
2025-02-27 07:15:00,234.7063440138963,239.76764820152863,2358.56,-1.0
2025-02-27 07:20:00,235.93686722069535,239.76764820152863,2354.54,-1.0
2025-02-27 07:25:00,234.58084289081486,239.76764820152863,2358.97,-1.0
2025-02-27 07:30:00,235.03999334111302,239.76764820152863,2357.47,-1.0
2025-02-27 07:35:00,232.74599014704518,232.48240265203538,2363.15,-1.0
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2025-02-27 08:50:00,235.35378638119636,232.99871577515356,2350.28,1.0
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2025-02-27 09:00:00,238.08038860330615,232.99871577515356,2354.86,1.0
2025-02-27 09:05:00,239.43217796555433,239.41559917993223,2358.32,-1.0
2025-02-27 09:10:00,239.67288301310697,239.41559917993223,2355.95,-1.0
2025-02-27 09:15:00,240.1644494393325,239.41559917993223,2351.11,-1.0
2025-02-27 09:20:00,240.4274984153499,239.41559917993223,2348.52,-1.0
2025-02-27 09:25:00,239.5682728025672,239.41559917993223,2356.98,-1.0
2025-02-27 09:30:00,239.06701039503062,239.17663149417038,2361.78,-1.0
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2025-02-27 09:50:00,240.1456080104631,239.17663149417038,2358.23,-1.0
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2025-02-27 11:40:00,252.698448623662,238.34501168025773,2351.32,-1.0
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2025-02-27 11:50:00,255.17418057252553,238.34501168025773,2348.86,-1.0
2025-02-27 11:55:00,251.2492396779857,238.34501168025773,2352.76,-1.0
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2025-02-27 12:05:00,258.917970348856,238.34501168025773,2345.14,-1.0
2025-02-27 12:10:00,262.10824281954604,238.34501168025773,2341.97,-1.0
2025-02-27 12:15:00,257.6801043744242,238.34501168025773,2346.37,-1.0
2025-02-27 12:20:00,254.52002375676884,238.34501168025773,2349.51,-1.0
2025-02-27 12:25:00,252.9903032029995,238.34501168025773,2351.03,-1.0
2025-02-27 12:30:00,249.22638552464625,238.34501168025773,2354.77,-1.0
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2025-02-27 12:40:00,244.8485668345828,238.34501168025773,2359.12,-1.0
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2025-02-27 13:00:00,258.03234265983156,238.34501168025773,2346.02,-1.0
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2025-02-27 13:50:00,270.07747381436445,270.02467260606704,2334.61,1.0
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2025-02-27 14:15:00,269.24413103481135,270.02467260606704,2327.41,1.0
2025-02-27 14:20:00,269.98372275166474,270.02467260606704,2333.8,1.0
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2025-02-27 14:45:00,282.36352964098313,281.98891805994117,2320.39,1.0
2025-02-27 14:50:00,280.88549974995675,281.98891805994117,2308.25,1.0
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2025-02-27 15:55:00,279.1676182294265,279.1676182294265,2322.37,0.0
1 datetime equity balance price position
2 2025-02-26 16:00:00 200.0 200.0 2422.73 0.0
3 2025-02-26 16:05:00 200.0 200.0 2412.09 0.0
4 2025-02-26 16:10:00 200.0 200.0 2409.01 0.0
5 2025-02-26 16:15:00 200.0 200.0 2395.62 0.0
6 2025-02-26 16:20:00 200.0 200.0 2393.83 0.0
7 2025-02-26 16:25:00 200.0 200.0 2403.36 0.0
8 2025-02-26 16:30:00 200.0 200.0 2405.05 0.0
9 2025-02-26 16:35:00 200.0 200.0 2402.14 0.0
10 2025-02-26 16:40:00 200.0 200.0 2404.26 0.0
11 2025-02-26 16:45:00 200.0 200.0 2398.43 0.0
12 2025-02-26 16:50:00 200.0 200.0 2397.62 0.0
13 2025-02-26 16:55:00 200.0 200.0 2397.36 0.0
14 2025-02-26 17:00:00 200.0 200.0 2399.93 0.0
15 2025-02-26 17:05:00 200.0 200.0 2400.31 0.0
16 2025-02-26 17:10:00 200.0 200.0 2402.77 0.0
17 2025-02-26 17:15:00 199.88508727496452 199.9 2391.47 1.0
18 2025-02-26 17:20:00 200.65485040922104 199.7001483159936 2395.07 1.0
19 2025-02-26 17:25:00 201.151413340079 199.7001483159936 2397.05 1.0
20 2025-02-26 17:30:00 196.72646880791046 199.40349503913342 2382.07 1.0
21 2025-02-26 17:35:00 193.97309285581176 199.40349503913342 2376.56 1.0
22 2025-02-26 17:40:00 197.5759677404998 199.40349503913342 2383.77 1.0
23 2025-02-26 17:45:00 195.9269404007673 199.40349503913342 2380.47 1.0
24 2025-02-26 17:50:00 187.90964596335377 187.37965912389114 2369.69 1.0
25 2025-02-26 17:55:00 188.81182939334798 187.19274659378016 2365.89 1.0
26 2025-02-26 18:00:00 190.53682597248493 187.19274659378016 2373.14 1.0
27 2025-02-26 18:05:00 190.52730874997937 187.19274659378016 2373.1 1.0
28 2025-02-26 18:10:00 189.9967235952931 187.19274659378016 2370.87 1.0
29 2025-02-26 18:15:00 186.5538683538984 187.19274659378016 2356.4 1.0
30 2025-02-26 18:20:00 186.30404126312678 187.19274659378016 2355.35 1.0
31 2025-02-26 18:25:00 181.12463646239493 181.48897215232557 2312.77 1.0
32 2025-02-26 18:30:00 181.26724125056887 181.48897215232557 2314.59 1.0
33 2025-02-26 18:35:00 180.20272022123012 179.58333794472614 2275.37 1.0
34 2025-02-26 18:40:00 183.64158817039387 179.58333794472614 2318.77 1.0
35 2025-02-26 18:45:00 182.68520254490062 179.58333794472614 2306.7 1.0
36 2025-02-26 18:50:00 181.186838652765 179.58333794472614 2287.79 1.0
37 2025-02-26 18:55:00 181.52359415469925 179.58333794472614 2292.04 1.0
38 2025-02-26 19:00:00 181.98712819853813 179.58333794472614 2297.89 1.0
39 2025-02-26 19:05:00 181.6535421601515 179.58333794472614 2293.68 1.0
40 2025-02-26 19:10:00 183.04097482812057 179.58333794472614 2311.19 1.0
41 2025-02-26 19:15:00 182.62419037160902 179.58333794472614 2305.93 1.0
42 2025-02-26 19:20:00 181.37145990441365 179.58333794472614 2290.12 1.0
43 2025-02-26 19:25:00 180.45469257326565 179.58333794472614 2278.55 1.0
44 2025-02-26 19:30:00 181.13295777245554 179.58333794472614 2287.11 1.0
45 2025-02-26 19:35:00 181.36115914788388 179.58333794472614 2289.99 1.0
46 2025-02-26 19:40:00 182.0172381022405 179.58333794472614 2298.27 1.0
47 2025-02-26 19:45:00 181.59490708452063 179.58333794472614 2292.94 1.0
48 2025-02-26 19:50:00 182.40074319150207 179.58333794472614 2303.11 1.0
49 2025-02-26 19:55:00 181.77635887262164 179.58333794472614 2295.23 1.0
50 2025-02-26 20:00:00 182.93559016516235 179.58333794472614 2309.86 1.0
51 2025-02-26 20:05:00 184.0665561275799 183.3336108552589 2307.94 -1.0
52 2025-02-26 20:10:00 185.60064206152194 183.3336108552589 2288.56 -1.0
53 2025-02-26 20:15:00 187.50707671961734 187.57352406664992 2260.52 1.0
54 2025-02-26 20:20:00 192.3379875808024 187.38611572438992 2279.89 1.0
55 2025-02-26 20:25:00 198.0698646021626 187.38611572438992 2302.92 1.0
56 2025-02-26 20:30:00 198.7368828573621 187.38611572438992 2305.6 1.0
57 2025-02-26 20:35:00 202.43783862408483 187.38611572438992 2320.47 1.0
58 2025-02-26 20:40:00 203.37365528063347 187.38611572438992 2324.23 1.0
59 2025-02-26 20:45:00 204.07800664713147 187.38611572438992 2327.06 1.0
60 2025-02-26 20:50:00 204.73506940598472 187.38611572438992 2329.7 1.0
61 2025-02-26 20:55:00 207.09203316596222 187.38611572438992 2339.17 1.0
62 2025-02-26 21:00:00 204.78733576180258 187.38611572438992 2329.91 1.0
63 2025-02-26 21:05:00 205.57133109907073 187.38611572438992 2333.06 1.0
64 2025-02-26 21:10:00 204.81222450266833 187.38611572438992 2330.01 1.0
65 2025-02-26 21:15:00 202.65934841778923 187.38611572438992 2321.36 1.0
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67 2025-02-26 21:25:00 209.94247398979596 208.4148919992489 2328.89 -1.0
68 2025-02-26 21:30:00 211.58497501213768 208.4148919992489 2310.41 -1.0
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71 2025-02-26 21:45:00 211.6969637182064 208.4148919992489 2309.15 -1.0
72 2025-02-26 21:50:00 210.49264072834006 208.4148919992489 2322.7 -1.0
73 2025-02-26 21:55:00 208.82969732314456 208.4148919992489 2341.41 -1.0
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77 2025-02-26 22:15:00 207.56433391103513 206.22653563325682 2362.55 -1.0
78 2025-02-26 22:20:00 207.56346623880725 206.22653563325682 2362.56 -1.0
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82 2025-02-26 22:40:00 208.6975138406903 206.22653563325682 2349.49 -1.0
83 2025-02-26 22:45:00 207.5678045999468 206.22653563325682 2362.51 -1.0
84 2025-02-26 22:50:00 207.78559032915314 206.22653563325682 2360.0 -1.0
85 2025-02-26 22:55:00 208.83720906938436 206.22653563325682 2347.88 -1.0
86 2025-02-26 23:00:00 207.90966745774475 206.22653563325682 2358.57 -1.0
87 2025-02-26 23:05:00 208.80684054140738 206.22653563325682 2348.23 -1.0
88 2025-02-26 23:10:00 209.55911236300847 206.22653563325682 2339.56 -1.0
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97 2025-02-26 23:55:00 209.94522650443 206.22653563325682 2335.11 -1.0
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211 2025-02-27 09:25:00 239.5682728025672 239.41559917993223 2356.98 -1.0
212 2025-02-27 09:30:00 239.06701039503062 239.17663149417038 2361.78 -1.0
213 2025-02-27 09:35:00 239.4103386500838 239.17663149417038 2360.65 -1.0
214 2025-02-27 09:40:00 239.6898625391536 239.17663149417038 2359.73 -1.0
215 2025-02-27 09:45:00 239.27057670554888 239.17663149417038 2361.11 -1.0
216 2025-02-27 09:50:00 240.1456080104631 239.17663149417038 2358.23 -1.0
217 2025-02-27 09:55:00 240.19118255759406 239.17663149417038 2358.08 -1.0
218 2025-02-27 10:00:00 239.38907052808943 239.17663149417038 2360.72 -1.0
219 2025-02-27 10:05:00 238.05511374706333 238.82126228887873 2366.39 -1.0
220 2025-02-27 10:10:00 240.01787034899527 238.34501168025773 2363.92 -1.0
221 2025-02-27 10:15:00 235.78094697309464 238.34501168025773 2368.13 -1.0
222 2025-02-27 10:20:00 236.13318525850198 238.34501168025773 2367.78 -1.0
223 2025-02-27 10:25:00 235.90171438523424 238.34501168025773 2368.01 -1.0
224 2025-02-27 10:30:00 235.8111388261298 238.34501168025773 2368.1 -1.0
225 2025-02-27 10:35:00 231.5641514992173 238.34501168025773 2372.32 -1.0
226 2025-02-27 10:40:00 234.43237753753516 238.34501168025773 2369.47 -1.0
227 2025-02-27 10:45:00 239.38384143526181 238.34501168025773 2364.55 -1.0
228 2025-02-27 10:50:00 233.3655987303009 238.34501168025773 2370.53 -1.0
229 2025-02-27 10:55:00 227.0152456419559 238.34501168025773 2376.84 -1.0
230 2025-02-27 11:00:00 233.12406390602175 238.34501168025773 2370.77 -1.0
231 2025-02-27 11:05:00 234.43237753753516 238.34501168025773 2369.47 -1.0
232 2025-02-27 11:10:00 231.77549447046178 238.34501168025773 2372.11 -1.0
233 2025-02-27 11:15:00 225.69686805943107 238.34501168025773 2378.15 -1.0
234 2025-02-27 11:20:00 231.11127370369368 238.34501168025773 2372.77 -1.0
235 2025-02-27 11:25:00 240.10844590810018 238.34501168025773 2363.83 -1.0
236 2025-02-27 11:30:00 245.59329920944398 238.34501168025773 2358.38 -1.0
237 2025-02-27 11:35:00 246.3782873883521 238.34501168025773 2357.6 -1.0
238 2025-02-27 11:40:00 252.698448623662 238.34501168025773 2351.32 -1.0
239 2025-02-27 11:45:00 255.20437242556068 238.34501168025773 2348.83 -1.0
240 2025-02-27 11:50:00 255.17418057252553 238.34501168025773 2348.86 -1.0
241 2025-02-27 11:55:00 251.2492396779857 238.34501168025773 2352.76 -1.0
242 2025-02-27 12:00:00 252.30595453420813 238.34501168025773 2351.71 -1.0
243 2025-02-27 12:05:00 258.917970348856 238.34501168025773 2345.14 -1.0
244 2025-02-27 12:10:00 262.10824281954604 238.34501168025773 2341.97 -1.0
245 2025-02-27 12:15:00 257.6801043744242 238.34501168025773 2346.37 -1.0
246 2025-02-27 12:20:00 254.52002375676884 238.34501168025773 2349.51 -1.0
247 2025-02-27 12:25:00 252.9903032029995 238.34501168025773 2351.03 -1.0
248 2025-02-27 12:30:00 249.22638552464625 238.34501168025773 2354.77 -1.0
249 2025-02-27 12:35:00 249.33708898577441 238.34501168025773 2354.66 -1.0
250 2025-02-27 12:40:00 244.8485668345828 238.34501168025773 2359.12 -1.0
251 2025-02-27 12:45:00 248.48165314978462 238.34501168025773 2355.51 -1.0
252 2025-02-27 12:50:00 256.8951161955161 238.34501168025773 2347.15 -1.0
253 2025-02-27 12:55:00 258.03234265983156 238.34501168025773 2346.02 -1.0
254 2025-02-27 13:00:00 258.03234265983156 238.34501168025773 2346.02 -1.0
255 2025-02-27 13:05:00 252.81921603580201 238.34501168025773 2351.2 -1.0
256 2025-02-27 13:10:00 249.8604144383797 238.34501168025773 2354.14 -1.0
257 2025-02-27 13:15:00 251.40019894316043 238.34501168025773 2352.61 -1.0
258 2025-02-27 13:20:00 254.9427096992578 238.34501168025773 2349.09 -1.0
259 2025-02-27 13:25:00 254.49989585474557 238.34501168025773 2349.53 -1.0
260 2025-02-27 13:30:00 262.26070406853376 262.1012105348726 2342.1 1.0
261 2025-02-27 13:35:00 266.5600632295155 261.83908872922206 2354.85 1.0
262 2025-02-27 13:40:00 265.98216710470956 261.83908872922206 2353.13 1.0
263 2025-02-27 13:45:00 269.27509413972587 267.4613178857076 2343.12 -1.0
264 2025-02-27 13:50:00 270.07747381436445 270.02467260606704 2334.61 1.0
265 2025-02-27 13:55:00 270.1885861849715 270.02467260606704 2335.57 1.0
266 2025-02-27 14:00:00 270.67701764743174 270.02467260606704 2339.79 1.0
267 2025-02-27 14:05:00 270.4582651677991 270.02467260606704 2337.9 1.0
268 2025-02-27 14:10:00 270.35641216140925 270.02467260606704 2337.02 1.0
269 2025-02-27 14:15:00 269.24413103481135 270.02467260606704 2327.41 1.0
270 2025-02-27 14:20:00 269.98372275166474 270.02467260606704 2333.8 1.0
271 2025-02-27 14:25:00 271.18049557674505 270.02467260606704 2344.14 1.0
272 2025-02-27 14:30:00 272.0414098259353 271.6227855132458 2346.69 -1.0
273 2025-02-27 14:35:00 275.2635881993619 271.3510283556246 2338.26 -1.0
274 2025-02-27 14:40:00 278.8619081286832 271.3510283556246 2327.89 -1.0
275 2025-02-27 14:45:00 282.36352964098313 281.98891805994117 2320.39 1.0
276 2025-02-27 14:50:00 280.88549974995675 281.98891805994117 2308.25 1.0
277 2025-02-27 14:55:00 280.84167019799884 281.98891805994117 2307.89 1.0
278 2025-02-27 15:00:00 279.1676182294265 279.1676182294265 2301.55 0.0
279 2025-02-27 15:05:00 279.1676182294265 279.1676182294265 2310.24 0.0
280 2025-02-27 15:10:00 279.1676182294265 279.1676182294265 2317.79 0.0
281 2025-02-27 15:15:00 279.1676182294265 279.1676182294265 2319.9 0.0
282 2025-02-27 15:20:00 279.1676182294265 279.1676182294265 2327.46 0.0
283 2025-02-27 15:25:00 279.1676182294265 279.1676182294265 2326.61 0.0
284 2025-02-27 15:30:00 279.1676182294265 279.1676182294265 2321.63 0.0
285 2025-02-27 15:35:00 279.1676182294265 279.1676182294265 2331.37 0.0
286 2025-02-27 15:40:00 279.1676182294265 279.1676182294265 2322.72 0.0
287 2025-02-27 15:45:00 279.1676182294265 279.1676182294265 2326.39 0.0
288 2025-02-27 15:50:00 279.1676182294265 279.1676182294265 2326.28 0.0
289 2025-02-27 15:55:00 279.1676182294265 279.1676182294265 2322.37 0.0

21
strategy/results/bb_20250227_trades_20260228_123524.csv
View File

@@ -1,21 +0,0 @@
entry_time,exit_time,side,entry_price,exit_price,qty,margin,gross_pnl,fee,net_pnl
2025-02-26 17:15:00,2025-02-26 17:50:00,long,2387.43,2363.67,0.4997,11.93,-11.93,0.00,-11.93
2025-02-26 17:50:00,2025-02-26 18:25:00,long,2359.09,2335.61,0.2379,5.61,-5.61,0.00,-5.61
2025-02-26 18:25:00,2025-02-26 18:35:00,long,2317.42,2294.36,0.0784,1.82,-1.82,0.00,-1.82
2025-02-26 18:35:00,2025-02-26 20:05:00,long,2267.55,2317.20,0.0792,1.80,3.93,0.09,3.84
2025-02-26 20:05:00,2025-02-26 20:15:00,short,2317.20,2261.32,0.0792,1.83,4.42,0.09,4.33
2025-02-26 20:15:00,2025-02-26 21:20:00,long,2259.99,2346.08,0.2489,5.62,21.42,0.29,21.13
2025-02-26 21:20:00,2025-02-26 22:00:00,short,2346.08,2369.42,0.0889,2.09,-2.09,0.00,-2.09
2025-02-26 22:00:00,2025-02-27 01:05:00,short,2354.74,2317.15,0.2669,6.29,10.03,0.31,9.72
2025-02-27 01:05:00,2025-02-27 01:20:00,long,2317.15,2354.05,0.0939,2.18,3.47,0.11,3.36
2025-02-27 01:20:00,2025-02-27 03:25:00,short,2360.61,2329.18,0.9295,21.94,29.22,1.08,28.13
2025-02-27 03:25:00,2025-02-27 04:35:00,long,2325.72,2302.58,0.3192,7.42,-7.42,0.00,-7.42
2025-02-27 05:15:00,2025-02-27 07:35:00,short,2342.03,2365.33,0.3061,7.17,-7.17,0.00,-7.17
2025-02-27 07:35:00,2025-02-27 08:00:00,short,2365.83,2352.30,0.0983,2.33,1.33,0.12,1.21
2025-02-27 08:00:00,2025-02-27 09:05:00,long,2346.32,2358.48,0.5953,13.97,7.24,0.70,6.54
2025-02-27 09:05:00,2025-02-27 13:30:00,short,2365.58,2340.68,1.0064,23.81,25.07,1.18,23.89
2025-02-27 13:30:00,2025-02-27 13:45:00,long,2340.80,2359.11,0.3360,7.86,6.15,0.40,5.76
2025-02-27 13:45:00,2025-02-27 13:50:00,short,2359.11,2334.15,0.1134,2.68,2.83,0.13,2.70
2025-02-27 13:50:00,2025-02-27 14:30:00,long,2334.15,2350.31,0.1157,2.70,1.87,0.14,1.73
2025-02-27 14:30:00,2025-02-27 14:45:00,short,2349.54,2317.31,0.3470,8.15,11.18,0.40,10.78
2025-02-27 14:45:00,2025-02-27 15:00:00,long,2317.31,2294.26,0.1217,2.82,-2.82,0.00,-2.82
1 entry_time exit_time side entry_price exit_price qty margin gross_pnl fee net_pnl
2 2025-02-26 17:15:00 2025-02-26 17:50:00 long 2387.43 2363.67 0.4997 11.93 -11.93 0.00 -11.93
3 2025-02-26 17:50:00 2025-02-26 18:25:00 long 2359.09 2335.61 0.2379 5.61 -5.61 0.00 -5.61
4 2025-02-26 18:25:00 2025-02-26 18:35:00 long 2317.42 2294.36 0.0784 1.82 -1.82 0.00 -1.82
5 2025-02-26 18:35:00 2025-02-26 20:05:00 long 2267.55 2317.20 0.0792 1.80 3.93 0.09 3.84
6 2025-02-26 20:05:00 2025-02-26 20:15:00 short 2317.20 2261.32 0.0792 1.83 4.42 0.09 4.33
7 2025-02-26 20:15:00 2025-02-26 21:20:00 long 2259.99 2346.08 0.2489 5.62 21.42 0.29 21.13
8 2025-02-26 21:20:00 2025-02-26 22:00:00 short 2346.08 2369.42 0.0889 2.09 -2.09 0.00 -2.09
9 2025-02-26 22:00:00 2025-02-27 01:05:00 short 2354.74 2317.15 0.2669 6.29 10.03 0.31 9.72
10 2025-02-27 01:05:00 2025-02-27 01:20:00 long 2317.15 2354.05 0.0939 2.18 3.47 0.11 3.36
11 2025-02-27 01:20:00 2025-02-27 03:25:00 short 2360.61 2329.18 0.9295 21.94 29.22 1.08 28.13
12 2025-02-27 03:25:00 2025-02-27 04:35:00 long 2325.72 2302.58 0.3192 7.42 -7.42 0.00 -7.42
13 2025-02-27 05:15:00 2025-02-27 07:35:00 short 2342.03 2365.33 0.3061 7.17 -7.17 0.00 -7.17
14 2025-02-27 07:35:00 2025-02-27 08:00:00 short 2365.83 2352.30 0.0983 2.33 1.33 0.12 1.21
15 2025-02-27 08:00:00 2025-02-27 09:05:00 long 2346.32 2358.48 0.5953 13.97 7.24 0.70 6.54
16 2025-02-27 09:05:00 2025-02-27 13:30:00 short 2365.58 2340.68 1.0064 23.81 25.07 1.18 23.89
17 2025-02-27 13:30:00 2025-02-27 13:45:00 long 2340.80 2359.11 0.3360 7.86 6.15 0.40 5.76
18 2025-02-27 13:45:00 2025-02-27 13:50:00 short 2359.11 2334.15 0.1134 2.68 2.83 0.13 2.70
19 2025-02-27 13:50:00 2025-02-27 14:30:00 long 2334.15 2350.31 0.1157 2.70 1.87 0.14 1.73
20 2025-02-27 14:30:00 2025-02-27 14:45:00 short 2349.54 2317.31 0.3470 8.15 11.18 0.40 10.78
21 2025-02-27 14:45:00 2025-02-27 15:00:00 long 2317.31 2294.26 0.1217 2.82 -2.82 0.00 -2.82

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序号,方向,开仓时间,平仓时间,开仓价,平仓价,保证金,杠杆,数量,毛盈亏,手续费,净盈亏
1,做多,2026-02-01 00:45:00,2026-02-01 04:20:00,2503.98,2377.2,2.0,50,0.0399,-5.06,0.05,-5.11
2,做空,2026-02-01 04:20:00,2026-02-01 04:35:00,2377.2,2360.24,1.95,50,0.041,0.7,0.05,0.65
3,做多,2026-02-01 04:35:00,2026-02-01 05:00:00,2360.24,2373.42,1.95,50,0.0414,0.55,0.05,0.5
4,做空,2026-02-01 05:00:00,2026-02-01 09:05:00,2373.42,2433.28,1.96,50,0.0413,-2.47,0.05,-2.52
5,做多,2026-02-01 09:05:00,2026-02-01 12:10:00,2433.28,2451.12,1.94,50,0.0398,0.71,0.05,0.66
6,做空,2026-02-01 12:10:00,2026-02-01 13:00:00,2451.12,2439.68,1.94,50,0.0396,0.45,0.05,0.4
7,做多,2026-02-01 13:00:00,2026-02-01 15:40:00,2439.68,2430.01,1.95,50,0.0399,-0.39,0.05,-0.43
8,做空,2026-02-01 15:40:00,2026-02-01 18:30:00,2430.01,2406.59,1.94,50,0.0399,0.94,0.05,0.89
9,做多,2026-02-01 18:30:00,2026-02-01 19:35:00,2406.59,2404.44,1.95,50,0.0405,-0.09,0.05,-0.14
10,做空,2026-02-01 19:35:00,2026-02-01 20:20:00,2404.44,2387.44,1.95,50,0.0405,0.69,0.05,0.64
11,做多,2026-02-01 20:20:00,2026-02-01 21:00:00,2387.44,2402.27,1.95,50,0.0409,0.61,0.05,0.56
12,做空,2026-02-01 21:00:00,2026-02-01 21:40:00,2402.27,2392.0,1.96,50,0.0408,0.42,0.05,0.37
13,做多,2026-02-01 21:40:00,2026-02-02 00:25:00,2392.0,2318.96,1.96,50,0.041,-3.0,0.05,-3.04
14,做空,2026-02-02 00:25:00,2026-02-02 03:45:00,2318.96,2313.79,1.93,50,0.0416,0.22,0.05,0.17
15,做多,2026-02-02 03:45:00,2026-02-02 04:35:00,2313.79,2342.69,1.93,50,0.0418,1.21,0.05,1.16
16,做空,2026-02-02 04:35:00,2026-02-02 05:55:00,2342.69,2289.17,1.94,50,0.0415,2.22,0.05,2.17
17,做多,2026-02-02 05:55:00,2026-02-02 06:10:00,2289.17,2323.39,1.96,50,0.0429,1.47,0.05,1.42
18,做空,2026-02-02 06:10:00,2026-02-02 07:05:00,2323.39,2235.28,1.98,50,0.0426,3.75,0.05,3.7
19,做多,2026-02-02 07:05:00,2026-02-02 08:15:00,2235.28,2307.06,2.01,50,0.0451,3.23,0.05,3.18
20,做空,2026-02-02 08:15:00,2026-02-02 09:00:00,2307.06,2302.75,2.06,50,0.0446,0.19,0.05,0.14
21,做多,2026-02-02 09:00:00,2026-02-02 14:15:00,2302.75,2211.45,2.06,50,0.0447,-4.08,0.05,-4.13
22,做空,2026-02-02 14:15:00,2026-02-02 14:35:00,2211.45,2166.31,2.02,50,0.0456,2.06,0.05,2.01
23,做多,2026-02-02 14:35:00,2026-02-02 14:40:00,2166.31,2219.94,2.04,50,0.047,2.52,0.05,2.47
24,做空,2026-02-02 14:40:00,2026-02-02 17:15:00,2219.94,2250.82,2.06,50,0.0464,-1.43,0.05,-1.49
25,做多,2026-02-02 17:15:00,2026-02-02 17:25:00,2250.82,2277.79,2.05,50,0.0455,1.23,0.05,1.17
26,做空,2026-02-02 17:25:00,2026-02-02 22:10:00,2277.79,2305.44,2.06,50,0.0452,-1.25,0.05,-1.3
27,做多,2026-02-02 22:10:00,2026-02-02 22:35:00,2305.44,2350.92,2.04,50,0.0443,2.02,0.05,1.96
28,做空,2026-02-02 22:35:00,2026-02-03 00:50:00,2350.92,2357.15,2.06,50,0.0439,-0.27,0.05,-0.33
29,做多,2026-02-03 00:50:00,2026-02-03 09:00:00,2357.15,2349.37,2.06,50,0.0437,-0.34,0.05,-0.39
30,做空,2026-02-03 09:00:00,2026-02-03 10:10:00,2349.37,2327.51,2.06,50,0.0439,0.96,0.05,0.91
31,做多,2026-02-03 10:10:00,2026-02-03 15:10:00,2327.51,2322.01,2.07,50,0.0445,-0.24,0.05,-0.3
32,做空,2026-02-03 15:10:00,2026-02-03 16:10:00,2322.01,2311.05,2.07,50,0.0446,0.49,0.05,0.44
33,做多,2026-02-03 16:10:00,2026-02-03 20:10:00,2311.05,2290.92,2.07,50,0.0449,-0.9,0.05,-0.95
34,做空,2026-02-03 20:10:00,2026-02-03 22:15:00,2290.92,2299.99,2.06,50,0.045,-0.41,0.05,-0.46
35,做多,2026-02-03 22:15:00,2026-02-03 23:25:00,2299.99,2292.96,2.06,50,0.0447,-0.31,0.05,-0.37
36,做空,2026-02-03 23:25:00,2026-02-04 00:15:00,2292.96,2265.27,2.05,50,0.0448,1.24,0.05,1.19
37,做多,2026-02-04 00:15:00,2026-02-04 03:10:00,2265.27,2178.1,2.07,50,0.0456,-3.97,0.05,-4.02
38,做空,2026-02-04 03:10:00,2026-02-04 06:05:00,2178.1,2259.5,2.02,50,0.0465,-3.78,0.05,-3.84
39,做多,2026-02-04 06:05:00,2026-02-04 09:00:00,2259.5,2264.51,1.99,50,0.0439,0.22,0.05,0.17
40,做空,2026-02-04 09:00:00,2026-02-04 10:30:00,2264.51,2254.5,2.0,50,0.0441,0.44,0.05,0.39
41,做多,2026-02-04 10:30:00,2026-02-04 11:10:00,2254.5,2275.54,2.0,50,0.0443,0.93,0.05,0.88
42,做空,2026-02-04 11:10:00,2026-02-04 12:25:00,2275.54,2274.79,2.01,50,0.0441,0.03,0.05,-0.02
43,做多,2026-02-04 12:25:00,2026-02-04 20:20:00,2274.79,2253.17,2.01,50,0.0441,-0.95,0.05,-1.0
44,做空,2026-02-04 20:20:00,2026-02-04 20:25:00,2253.17,2234.15,2.0,50,0.0443,0.84,0.05,0.79
45,做多,2026-02-04 20:25:00,2026-02-05 01:00:00,2234.15,2141.81,2.0,50,0.0449,-4.14,0.05,-4.19
46,做空,2026-02-05 01:00:00,2026-02-05 01:35:00,2141.81,2100.23,1.96,50,0.0458,1.9,0.05,1.86
47,做多,2026-02-05 01:35:00,2026-02-05 02:25:00,2100.23,2122.35,1.98,50,0.0471,1.04,0.05,0.99
48,做空,2026-02-05 02:25:00,2026-02-05 03:20:00,2122.35,2138.64,1.99,50,0.0469,-0.76,0.05,-0.81
49,做多,2026-02-05 03:20:00,2026-02-05 03:45:00,2138.64,2159.36,1.98,50,0.0463,0.96,0.05,0.91
50,做空,2026-02-05 03:45:00,2026-02-05 05:05:00,2159.36,2158.46,1.99,50,0.0461,0.04,0.05,-0.01
51,做多,2026-02-05 05:05:00,2026-02-05 10:35:00,2158.46,2153.58,1.99,50,0.0461,-0.22,0.05,-0.27
52,做空,2026-02-05 10:35:00,2026-02-05 11:15:00,2153.58,2113.19,2.0,50,0.0463,1.87,0.05,1.82
53,做多,2026-02-05 11:15:00,2026-02-05 12:40:00,2113.19,2121.89,2.01,50,0.0477,0.41,0.05,0.36
54,做空,2026-02-05 12:40:00,2026-02-05 13:20:00,2121.89,2085.5,2.02,50,0.0475,1.73,0.05,1.68
55,做多,2026-02-05 13:20:00,2026-02-05 15:00:00,2085.5,2115.39,2.03,50,0.0488,1.46,0.05,1.41
56,做空,2026-02-05 15:00:00,2026-02-05 15:55:00,2115.39,2089.53,2.05,50,0.0484,1.25,0.05,1.2
57,做多,2026-02-05 15:55:00,2026-02-05 16:25:00,2089.53,2110.34,2.06,50,0.0493,1.03,0.05,0.97
58,做空,2026-02-05 16:25:00,2026-02-05 18:55:00,2110.34,2096.48,2.07,50,0.049,0.68,0.05,0.63
59,做多,2026-02-05 18:55:00,2026-02-05 21:05:00,2096.48,2071.86,2.07,50,0.0495,-1.22,0.05,-1.27
60,做空,2026-02-05 21:05:00,2026-02-06 01:20:00,2071.86,1987.12,2.06,50,0.0497,4.21,0.05,4.16
61,做多,2026-02-06 01:20:00,2026-02-06 06:25:00,1987.12,1871.41,2.1,50,0.0529,-6.12,0.05,-6.17
62,做空,2026-02-06 06:25:00,2026-02-06 08:10:00,1871.41,1766.83,2.04,50,0.0545,5.7,0.05,5.65
63,做多,2026-02-06 08:10:00,2026-02-06 09:35:00,1766.83,1909.28,2.11,50,0.0596,8.49,0.06,8.43
64,做空,2026-02-06 09:35:00,2026-02-06 11:50:00,1909.28,1885.62,2.19,50,0.0573,1.36,0.05,1.3
65,做多,2026-02-06 11:50:00,2026-02-06 13:05:00,1885.62,1911.81,2.2,50,0.0584,1.53,0.06,1.47
66,做空,2026-02-06 13:05:00,2026-02-06 16:15:00,1911.81,1879.3,2.22,50,0.0579,1.88,0.05,1.83
67,做多,2026-02-06 16:15:00,2026-02-06 17:10:00,1879.3,1885.97,2.23,50,0.0594,0.4,0.06,0.34
68,做空,2026-02-06 17:10:00,2026-02-06 19:00:00,1885.97,1924.17,2.24,50,0.0593,-2.26,0.06,-2.32
69,做多,2026-02-06 19:00:00,2026-02-06 19:15:00,1924.17,1924.74,2.21,50,0.0575,0.03,0.06,-0.02
70,做空,2026-02-06 19:15:00,2026-02-06 20:05:00,1924.74,1922.87,2.21,50,0.0575,0.11,0.06,0.05
71,做多,2026-02-06 20:05:00,2026-02-06 21:00:00,1922.87,1932.91,2.21,50,0.0575,0.58,0.06,0.52
72,做空,2026-02-06 21:00:00,2026-02-06 23:15:00,1932.91,1971.8,2.22,50,0.0573,-2.23,0.06,-2.29
73,做多,2026-02-06 23:15:00,2026-02-06 23:45:00,1971.8,1990.88,2.19,50,0.0556,1.06,0.06,1.01
74,做空,2026-02-06 23:45:00,2026-02-07 02:05:00,1990.88,2032.15,2.2,50,0.0553,-2.28,0.06,-2.34
75,做多,2026-02-07 02:05:00,2026-02-07 04:35:00,2032.15,2073.09,2.18,50,0.0536,2.19,0.06,2.14
76,做空,2026-02-07 04:35:00,2026-02-07 07:15:00,2073.09,2053.48,2.2,50,0.053,1.04,0.05,0.99
77,做多,2026-02-07 07:15:00,2026-02-07 11:00:00,2053.48,2064.11,2.21,50,0.0538,0.57,0.06,0.52
78,做空,2026-02-07 11:00:00,2026-02-07 14:15:00,2064.11,2073.13,2.23,50,0.054,-0.49,0.06,-0.54
79,做多,2026-02-07 14:15:00,2026-02-07 19:55:00,2073.13,2017.76,2.22,50,0.0536,-2.97,0.05,-3.02
80,做空,2026-02-07 19:55:00,2026-02-07 21:35:00,2017.76,2027.6,2.19,50,0.0543,-0.53,0.06,-0.59
81,做多,2026-02-07 21:35:00,2026-02-08 02:15:00,2027.6,2076.87,2.18,50,0.0539,2.65,0.06,2.6
82,做空,2026-02-08 02:15:00,2026-02-08 06:10:00,2076.87,2099.58,2.21,50,0.0532,-1.21,0.06,-1.26
83,做多,2026-02-08 06:10:00,2026-02-08 10:20:00,2099.58,2102.05,2.2,50,0.0523,0.13,0.05,0.07
84,做空,2026-02-08 10:20:00,2026-02-08 12:15:00,2102.05,2074.69,2.2,50,0.0524,1.43,0.05,1.38
85,做多,2026-02-08 12:15:00,2026-02-08 12:55:00,2074.69,2086.46,2.22,50,0.0534,0.63,0.06,0.57
86,做空,2026-02-08 12:55:00,2026-02-08 14:05:00,2086.46,2079.62,2.22,50,0.0532,0.36,0.06,0.31
87,做多,2026-02-08 14:05:00,2026-02-08 16:20:00,2079.62,2096.67,2.22,50,0.0535,0.91,0.06,0.86
88,做空,2026-02-08 16:20:00,2026-02-08 17:35:00,2096.67,2103.15,2.23,50,0.0532,-0.34,0.06,-0.4
89,做多,2026-02-08 17:35:00,2026-02-08 18:05:00,2103.15,2107.65,2.23,50,0.053,0.24,0.06,0.18
90,做空,2026-02-08 18:05:00,2026-02-08 18:20:00,2107.65,2087.85,2.23,50,0.0529,1.05,0.06,0.99
91,做多,2026-02-08 18:20:00,2026-02-08 19:15:00,2087.85,2110.36,2.24,50,0.0536,1.21,0.06,1.15
92,做空,2026-02-08 19:15:00,2026-02-08 21:05:00,2110.36,2123.28,2.25,50,0.0533,-0.69,0.06,-0.75
93,做多,2026-02-08 21:05:00,2026-02-09 02:15:00,2123.28,2094.55,2.24,50,0.0528,-1.52,0.06,-1.57
94,做空,2026-02-09 02:15:00,2026-02-09 04:45:00,2094.55,2104.6,2.22,50,0.0531,-0.53,0.06,-0.59
95,做多,2026-02-09 04:45:00,2026-02-09 06:55:00,2104.6,2107.44,2.22,50,0.0527,0.15,0.06,0.09
96,做空,2026-02-09 06:55:00,2026-02-09 08:25:00,2107.44,2081.4,2.22,50,0.0526,1.37,0.05,1.32
97,做多,2026-02-09 08:25:00,2026-02-09 09:20:00,2081.4,2098.76,2.24,50,0.0539,0.94,0.06,0.88
98,做空,2026-02-09 09:20:00,2026-02-09 09:40:00,2098.76,2063.62,2.25,50,0.0537,1.89,0.06,1.83
99,做多,2026-02-09 09:40:00,2026-02-09 10:35:00,2063.62,2079.96,2.27,50,0.055,0.9,0.06,0.84
100,做空,2026-02-09 10:35:00,2026-02-09 14:15:00,2079.96,2075.79,2.28,50,0.0548,0.23,0.06,0.17
101,做多,2026-02-09 14:15:00,2026-02-09 15:10:00,2075.79,2084.33,2.28,50,0.0549,0.47,0.06,0.41
102,做空,2026-02-09 15:10:00,2026-02-09 15:35:00,2084.33,2067.23,2.28,50,0.0548,0.94,0.06,0.88
103,做多,2026-02-09 15:35:00,2026-02-09 20:00:00,2067.23,2031.57,2.29,50,0.0554,-1.98,0.06,-2.03
104,做空,2026-02-09 20:00:00,2026-02-10 02:20:00,2031.57,2115.84,2.27,50,0.0559,-4.71,0.06,-4.77
105,做多,2026-02-10 02:20:00,2026-02-10 03:30:00,2115.84,2131.85,2.22,50,0.0525,0.84,0.06,0.78
106,做空,2026-02-10 03:30:00,2026-02-10 04:00:00,2131.85,2130.8,2.23,50,0.0523,0.05,0.06,-0.0
107,做多,2026-02-10 04:00:00,2026-02-10 04:05:00,2130.8,2135.34,2.23,50,0.0523,0.24,0.06,0.18
108,做空,2026-02-10 04:05:00,2026-02-10 04:15:00,2135.34,2122.59,2.23,50,0.0522,0.67,0.06,0.61
109,做多,2026-02-10 04:15:00,2026-02-10 16:20:00,2122.59,2011.91,2.24,50,0.0527,-5.83,0.05,-5.88
110,做空,2026-02-10 16:20:00,2026-02-10 17:50:00,2011.91,2015.22,2.19,50,0.0544,-0.18,0.05,-0.23
111,做多,2026-02-10 17:50:00,2026-02-10 19:30:00,2015.22,2019.66,2.19,50,0.0543,0.24,0.05,0.19
112,做空,2026-02-10 19:30:00,2026-02-10 19:40:00,2019.66,2007.94,2.19,50,0.0542,0.63,0.05,0.58
113,做多,2026-02-10 19:40:00,2026-02-10 21:15:00,2007.94,2012.02,2.19,50,0.0546,0.22,0.05,0.17
114,做空,2026-02-10 21:15:00,2026-02-10 22:35:00,2012.02,2012.47,2.19,50,0.0545,-0.02,0.05,-0.08
115,做多,2026-02-10 22:35:00,2026-02-11 00:10:00,2012.47,2032.87,2.19,50,0.0545,1.11,0.06,1.06
116,做空,2026-02-11 00:10:00,2026-02-11 02:20:00,2032.87,2013.69,2.2,50,0.0542,1.04,0.05,0.98
117,做多,2026-02-11 02:20:00,2026-02-11 03:30:00,2013.69,2013.39,2.21,50,0.0549,-0.02,0.06,-0.07
118,做空,2026-02-11 03:30:00,2026-02-11 05:30:00,2013.39,2001.89,2.21,50,0.0549,0.63,0.05,0.58
119,做多,2026-02-11 05:30:00,2026-02-11 07:35:00,2001.89,2018.01,2.22,50,0.0554,0.89,0.06,0.84
120,做空,2026-02-11 07:35:00,2026-02-11 09:20:00,2018.01,2018.06,2.22,50,0.0551,-0.0,0.06,-0.06
121,做多,2026-02-11 09:20:00,2026-02-11 10:20:00,2018.06,2027.05,2.23,50,0.0553,0.5,0.06,0.44
122,做空,2026-02-11 10:20:00,2026-02-11 10:45:00,2027.05,2014.32,2.24,50,0.0552,0.7,0.06,0.65
123,做多,2026-02-11 10:45:00,2026-02-11 15:35:00,2014.32,1951.85,2.24,50,0.0557,-3.48,0.05,-3.53
124,做空,2026-02-11 15:35:00,2026-02-11 16:05:00,1951.85,1939.79,2.21,50,0.0566,0.68,0.05,0.63
125,做多,2026-02-11 16:05:00,2026-02-11 17:00:00,1939.79,1949.26,2.21,50,0.057,0.54,0.06,0.48
126,做空,2026-02-11 17:00:00,2026-02-11 17:45:00,1949.26,1944.0,2.22,50,0.0569,0.3,0.06,0.24
127,做多,2026-02-11 17:45:00,2026-02-11 20:05:00,1944.0,1961.67,2.22,50,0.0571,1.01,0.06,0.95
128,做空,2026-02-11 20:05:00,2026-02-11 21:25:00,1961.67,1948.28,2.23,50,0.0568,0.76,0.06,0.71
129,做多,2026-02-11 21:25:00,2026-02-11 21:30:00,1948.28,1963.0,2.23,50,0.0574,0.84,0.06,0.79
130,做空,2026-02-11 21:30:00,2026-02-12 03:30:00,1963.0,1943.01,2.24,50,0.0571,1.14,0.06,1.09
131,做多,2026-02-12 03:30:00,2026-02-12 03:45:00,1943.01,1952.12,2.25,50,0.058,0.53,0.06,0.47
132,做空,2026-02-12 03:45:00,2026-02-12 04:25:00,1952.12,1941.02,2.26,50,0.0578,0.64,0.06,0.59
133,做多,2026-02-12 04:25:00,2026-02-12 05:20:00,1941.02,1968.22,2.26,50,0.0583,1.58,0.06,1.53
134,做空,2026-02-12 05:20:00,2026-02-12 06:10:00,1968.22,1960.92,2.28,50,0.0578,0.42,0.06,0.37
135,做多,2026-02-12 06:10:00,2026-02-12 08:05:00,1960.92,1951.09,2.28,50,0.0581,-0.57,0.06,-0.63
136,做空,2026-02-12 08:05:00,2026-02-12 12:30:00,1951.09,1967.55,2.29,50,0.0586,-0.97,0.06,-1.02
137,做多,2026-02-12 12:30:00,2026-02-12 14:10:00,1967.55,1970.2,2.28,50,0.0579,0.15,0.06,0.1
138,做空,2026-02-12 14:10:00,2026-02-12 16:25:00,1970.2,1958.73,2.28,50,0.0578,0.66,0.06,0.61
139,做多,2026-02-12 16:25:00,2026-02-12 17:10:00,1958.73,1968.27,2.28,50,0.0583,0.56,0.06,0.5
140,做空,2026-02-12 17:10:00,2026-02-12 20:50:00,1968.27,1976.76,2.29,50,0.0581,-0.49,0.06,-0.55
141,做多,2026-02-12 20:50:00,2026-02-12 21:30:00,1976.76,1985.8,2.28,50,0.0577,0.52,0.06,0.46
142,做空,2026-02-12 21:30:00,2026-02-12 22:45:00,1985.8,1978.53,2.29,50,0.0575,0.42,0.06,0.36
143,做多,2026-02-12 22:45:00,2026-02-13 01:35:00,1978.53,1920.11,2.29,50,0.0578,-3.38,0.06,-3.43
144,做空,2026-02-13 01:35:00,2026-02-13 04:10:00,1920.11,1920.22,2.25,50,0.0587,-0.01,0.06,-0.06
145,做多,2026-02-13 04:10:00,2026-02-13 06:30:00,1920.22,1927.46,2.25,50,0.0587,0.42,0.06,0.37
146,做空,2026-02-13 06:30:00,2026-02-13 08:15:00,1927.46,1940.1,2.26,50,0.0585,-0.74,0.06,-0.8
147,做多,2026-02-13 08:15:00,2026-02-13 09:05:00,1940.1,1941.5,2.26,50,0.0582,0.08,0.06,0.02
148,做空,2026-02-13 09:05:00,2026-02-13 10:20:00,1941.5,1946.1,2.26,50,0.0582,-0.27,0.06,-0.32
149,做多,2026-02-13 10:20:00,2026-02-13 10:50:00,1946.1,1947.9,2.25,50,0.0579,0.1,0.06,0.05
150,做空,2026-02-13 10:50:00,2026-02-13 12:10:00,1947.9,1950.84,2.25,50,0.0579,-0.17,0.06,-0.23
151,做多,2026-02-13 12:10:00,2026-02-13 12:25:00,1950.84,1949.6,2.25,50,0.0577,-0.07,0.06,-0.13
152,做空,2026-02-13 12:25:00,2026-02-13 12:30:00,1949.6,1950.08,2.25,50,0.0577,-0.03,0.06,-0.08
153,做多,2026-02-13 12:30:00,2026-02-13 16:20:00,1950.08,1939.99,2.25,50,0.0576,-0.58,0.06,-0.64
154,做空,2026-02-13 16:20:00,2026-02-13 19:05:00,1939.99,1955.6,2.24,50,0.0578,-0.9,0.06,-0.96
155,做多,2026-02-13 19:05:00,2026-02-13 20:25:00,1955.6,1961.63,2.23,50,0.057,0.34,0.06,0.29
156,做空,2026-02-13 20:25:00,2026-02-13 21:05:00,1961.63,1957.39,2.23,50,0.0569,0.24,0.06,0.19
157,做多,2026-02-13 21:05:00,2026-02-13 21:20:00,1957.39,1967.19,2.23,50,0.0571,0.56,0.06,0.5
158,做空,2026-02-13 21:20:00,2026-02-14 02:00:00,1967.19,2056.73,2.24,50,0.0569,-5.1,0.06,-5.15
159,做多,2026-02-14 02:00:00,2026-02-14 02:35:00,2056.73,2061.34,2.19,50,0.0532,0.25,0.05,0.19
160,做空,2026-02-14 02:35:00,2026-02-14 04:15:00,2061.34,2049.81,2.19,50,0.0531,0.61,0.05,0.56
161,做多,2026-02-14 04:15:00,2026-02-14 05:00:00,2049.81,2054.62,2.19,50,0.0535,0.26,0.05,0.2
162,做空,2026-02-14 05:00:00,2026-02-14 07:35:00,2054.62,2047.52,2.19,50,0.0534,0.38,0.05,0.32
163,做多,2026-02-14 07:35:00,2026-02-14 09:50:00,2047.52,2053.35,2.2,50,0.0537,0.31,0.06,0.26
164,做空,2026-02-14 09:50:00,2026-02-14 09:55:00,2053.35,2053.37,2.22,50,0.054,-0.0,0.06,-0.06
165,做多,2026-02-14 09:55:00,2026-02-14 11:25:00,2053.37,2050.56,2.22,50,0.0539,-0.15,0.06,-0.21
166,做空,2026-02-14 11:25:00,2026-02-14 13:00:00,2050.56,2052.1,2.21,50,0.054,-0.08,0.06,-0.14
167,做多,2026-02-14 13:00:00,2026-02-14 15:30:00,2052.1,2052.31,2.21,50,0.0539,0.01,0.06,-0.04
168,做空,2026-02-14 15:30:00,2026-02-14 15:35:00,2052.31,2050.7,2.21,50,0.0538,0.09,0.06,0.03
169,做多,2026-02-14 15:35:00,2026-02-14 15:50:00,2050.7,2057.65,2.21,50,0.0539,0.37,0.06,0.32
170,做空,2026-02-14 15:50:00,2026-02-14 20:15:00,2057.65,2094.01,2.21,50,0.0538,-1.95,0.06,-2.01
171,做多,2026-02-14 20:15:00,2026-02-14 22:20:00,2094.01,2069.5,2.19,50,0.0523,-1.28,0.05,-1.34
172,做空,2026-02-14 22:20:00,2026-02-15 00:00:00,2069.5,2086.28,2.18,50,0.0526,-0.88,0.05,-0.94
173,做多,2026-02-15 00:00:00,2026-02-15 02:20:00,2086.28,2082.08,2.17,50,0.052,-0.22,0.05,-0.27
174,做空,2026-02-15 02:20:00,2026-02-15 03:15:00,2082.08,2084.26,2.16,50,0.052,-0.11,0.05,-0.17
175,做多,2026-02-15 03:15:00,2026-02-15 03:30:00,2084.26,2085.58,2.16,50,0.0519,0.07,0.05,0.01
176,做空,2026-02-15 03:30:00,2026-02-15 07:15:00,2085.58,2087.12,2.16,50,0.0518,-0.08,0.05,-0.13
177,做多,2026-02-15 07:15:00,2026-02-15 10:05:00,2087.12,2058.92,2.16,50,0.0517,-1.46,0.05,-1.51
178,做空,2026-02-15 10:05:00,2026-02-15 10:20:00,2058.92,2054.96,2.16,50,0.0524,0.21,0.05,0.15
179,做多,2026-02-15 10:20:00,2026-02-15 12:05:00,2054.96,2072.91,2.16,50,0.0525,0.94,0.05,0.89
180,做空,2026-02-15 12:05:00,2026-02-15 14:00:00,2072.91,2086.85,2.17,50,0.0523,-0.73,0.05,-0.78
181,做多,2026-02-15 14:00:00,2026-02-15 14:35:00,2086.85,2090.16,2.16,50,0.0517,0.17,0.05,0.12
182,做空,2026-02-15 14:35:00,2026-02-15 16:25:00,2090.16,2075.54,2.16,50,0.0517,0.76,0.05,0.7
183,做多,2026-02-15 16:25:00,2026-02-15 20:20:00,2075.54,2062.81,2.17,50,0.0522,-0.66,0.05,-0.72
184,做空,2026-02-15 20:20:00,2026-02-15 20:45:00,2062.81,2046.45,2.16,50,0.0523,0.86,0.05,0.8
185,做多,2026-02-15 20:45:00,2026-02-15 22:30:00,2046.45,1999.25,2.17,50,0.0529,-2.5,0.05,-2.55
186,做空,2026-02-15 22:30:00,2026-02-16 00:05:00,1999.25,1999.62,2.14,50,0.0535,-0.02,0.05,-0.07
187,做多,2026-02-16 00:05:00,2026-02-16 05:30:00,1999.62,1959.09,2.14,50,0.0535,-2.17,0.05,-2.22
188,做空,2026-02-16 05:30:00,2026-02-16 07:00:00,1959.09,1956.8,2.12,50,0.054,0.12,0.05,0.07
189,做多,2026-02-16 07:00:00,2026-02-16 08:05:00,1956.8,1973.71,2.12,50,0.0541,0.91,0.05,0.86
190,做空,2026-02-16 08:05:00,2026-02-16 10:20:00,1973.71,1958.9,2.14,50,0.0541,0.8,0.05,0.75
191,做多,2026-02-16 10:20:00,2026-02-16 12:00:00,1958.9,1963.67,2.14,50,0.0547,0.26,0.05,0.21
192,做空,2026-02-16 12:00:00,2026-02-16 19:05:00,1963.67,1973.75,2.14,50,0.0546,-0.55,0.05,-0.6
193,做多,2026-02-16 19:05:00,2026-02-16 20:25:00,1973.75,1991.31,2.14,50,0.0542,0.95,0.05,0.9
194,做空,2026-02-16 20:25:00,2026-02-16 21:25:00,1991.31,1976.1,2.15,50,0.0539,0.82,0.05,0.77
195,做多,2026-02-16 21:25:00,2026-02-16 22:50:00,1976.1,1969.72,2.15,50,0.0545,-0.35,0.05,-0.4
196,做空,2026-02-16 22:50:00,2026-02-16 23:30:00,1969.72,1940.59,2.15,50,0.0545,1.59,0.05,1.54
197,做多,2026-02-16 23:30:00,2026-02-17 01:30:00,1940.59,1983.81,2.16,50,0.0557,2.41,0.06,2.35
198,做空,2026-02-17 01:30:00,2026-02-17 02:35:00,1983.81,1974.99,2.19,50,0.0551,0.49,0.05,0.43
199,做多,2026-02-17 02:35:00,2026-02-17 03:35:00,1974.99,1977.15,2.19,50,0.0555,0.12,0.05,0.06
200,做空,2026-02-17 03:35:00,2026-02-17 06:45:00,1977.15,1991.2,2.19,50,0.0554,-0.78,0.06,-0.83
201,做多,2026-02-17 06:45:00,2026-02-17 07:05:00,1991.2,1993.01,2.18,50,0.0548,0.1,0.05,0.04
202,做空,2026-02-17 07:05:00,2026-02-17 10:00:00,1993.01,1986.61,2.18,50,0.0547,0.35,0.05,0.3
203,做多,2026-02-17 10:00:00,2026-02-17 12:20:00,1986.61,1996.22,2.2,50,0.0553,0.53,0.06,0.48
204,做空,2026-02-17 12:20:00,2026-02-17 13:15:00,1996.22,1975.63,2.2,50,0.0551,1.14,0.05,1.08
205,做多,2026-02-17 13:15:00,2026-02-17 14:25:00,1975.63,1981.79,2.21,50,0.056,0.34,0.06,0.29
206,做空,2026-02-17 14:25:00,2026-02-17 15:05:00,1981.79,1975.75,2.21,50,0.0558,0.34,0.06,0.28
207,做多,2026-02-17 15:05:00,2026-02-17 18:00:00,1975.75,1974.07,2.22,50,0.0561,-0.09,0.06,-0.15
208,做空,2026-02-17 18:00:00,2026-02-17 18:15:00,1974.07,1963.99,2.21,50,0.0561,0.57,0.06,0.51
209,做多,2026-02-17 18:15:00,2026-02-17 19:00:00,1963.99,1966.66,2.22,50,0.0565,0.15,0.06,0.1
210,做空,2026-02-17 19:00:00,2026-02-17 19:45:00,1966.66,1966.75,2.22,50,0.0564,-0.01,0.06,-0.06
211,做多,2026-02-17 19:45:00,2026-02-17 20:05:00,1966.75,1973.21,2.22,50,0.0564,0.36,0.06,0.31
212,做空,2026-02-17 20:05:00,2026-02-17 20:50:00,1973.21,1970.9,2.22,50,0.0563,0.13,0.06,0.07
213,做多,2026-02-17 20:50:00,2026-02-17 21:10:00,1970.9,1979.05,2.22,50,0.0563,0.46,0.06,0.4
214,做空,2026-02-17 21:10:00,2026-02-17 22:35:00,1979.05,1978.92,2.22,50,0.0562,0.01,0.06,-0.05
215,做多,2026-02-17 22:35:00,2026-02-18 00:00:00,1978.92,1986.74,2.22,50,0.0562,0.44,0.06,0.38
216,做空,2026-02-18 00:00:00,2026-02-18 01:10:00,1986.74,1964.67,2.23,50,0.056,1.24,0.06,1.18
217,做多,2026-02-18 01:10:00,2026-02-18 02:20:00,1964.67,1986.33,2.24,50,0.0569,1.23,0.06,1.18
218,做空,2026-02-18 02:20:00,2026-02-18 04:30:00,1986.33,1993.32,2.25,50,0.0566,-0.4,0.06,-0.45
219,做多,2026-02-18 04:30:00,2026-02-18 04:40:00,1993.32,1997.94,2.24,50,0.0563,0.26,0.06,0.2
220,做空,2026-02-18 04:40:00,2026-02-18 06:50:00,1997.94,1993.94,2.24,50,0.0562,0.22,0.06,0.17
221,做多,2026-02-18 06:50:00,2026-02-18 11:25:00,1993.94,1993.01,2.25,50,0.0563,-0.05,0.06,-0.11
222,做空,2026-02-18 11:25:00,2026-02-18 12:35:00,1993.01,1994.19,2.26,50,0.0568,-0.07,0.06,-0.12
223,做多,2026-02-18 12:35:00,2026-02-18 14:20:00,1994.19,2001.76,2.26,50,0.0567,0.43,0.06,0.37
224,做空,2026-02-18 14:20:00,2026-02-18 17:50:00,2001.76,2017.24,2.26,50,0.0566,-0.88,0.06,-0.93
225,做多,2026-02-18 17:50:00,2026-02-18 22:30:00,2017.24,1967.63,2.25,50,0.0559,-2.77,0.05,-2.83
226,做空,2026-02-18 22:30:00,2026-02-18 22:45:00,1967.63,1955.53,2.23,50,0.0566,0.68,0.06,0.63
227,做多,2026-02-18 22:45:00,2026-02-18 23:05:00,1955.53,1983.75,2.23,50,0.0571,1.61,0.06,1.55
228,做空,2026-02-18 23:05:00,2026-02-19 02:15:00,1983.75,1963.63,2.25,50,0.0566,1.14,0.06,1.08
229,做多,2026-02-19 02:15:00,2026-02-19 04:30:00,1963.63,1947.9,2.26,50,0.0575,-0.9,0.06,-0.96
230,做空,2026-02-19 04:30:00,2026-02-19 07:15:00,1947.9,1946.27,2.25,50,0.0577,0.09,0.06,0.04
231,做多,2026-02-19 07:15:00,2026-02-19 07:25:00,1946.27,1947.6,2.25,50,0.0577,0.08,0.06,0.02
232,做空,2026-02-19 07:25:00,2026-02-19 08:25:00,1947.6,1952.44,2.25,50,0.0577,-0.28,0.06,-0.34
233,做多,2026-02-19 08:25:00,2026-02-19 09:10:00,1952.44,1969.01,2.25,50,0.0577,0.96,0.06,0.9
234,做空,2026-02-19 09:10:00,2026-02-19 12:45:00,1969.01,1971.59,2.26,50,0.0574,-0.15,0.06,-0.2
235,做多,2026-02-19 12:45:00,2026-02-19 14:05:00,1971.59,1971.53,2.26,50,0.0573,-0.0,0.06,-0.06
236,做空,2026-02-19 14:05:00,2026-02-19 15:40:00,1971.53,1981.57,2.26,50,0.0573,-0.57,0.06,-0.63
237,做多,2026-02-19 15:40:00,2026-02-19 17:20:00,1981.57,1977.7,2.25,50,0.0568,-0.22,0.06,-0.28
238,做空,2026-02-19 17:20:00,2026-02-19 17:45:00,1977.7,1961.93,2.25,50,0.0568,0.9,0.06,0.84
239,做多,2026-02-19 17:45:00,2026-02-19 22:50:00,1961.93,1939.99,2.26,50,0.0575,-1.26,0.06,-1.32
240,做空,2026-02-19 22:50:00,2026-02-20 00:05:00,1939.99,1915.04,2.24,50,0.0578,1.44,0.06,1.39
241,做多,2026-02-20 00:05:00,2026-02-20 01:15:00,1915.04,1941.03,2.26,50,0.0589,1.53,0.06,1.47
242,做空,2026-02-20 01:15:00,2026-02-20 03:25:00,1941.03,1935.82,2.27,50,0.0585,0.3,0.06,0.25
243,做多,2026-02-20 03:25:00,2026-02-20 04:45:00,1935.82,1947.16,2.27,50,0.0587,0.67,0.06,0.61
244,做空,2026-02-20 04:45:00,2026-02-20 05:55:00,1947.16,1947.67,2.28,50,0.0585,-0.03,0.06,-0.09
245,做多,2026-02-20 05:55:00,2026-02-20 07:00:00,1947.67,1943.15,2.28,50,0.0584,-0.26,0.06,-0.32
246,做空,2026-02-20 07:00:00,2026-02-20 12:00:00,1943.15,1948.02,2.27,50,0.0585,-0.28,0.06,-0.34
247,做多,2026-02-20 12:00:00,2026-02-20 13:30:00,1948.02,1960.28,2.28,50,0.0586,0.72,0.06,0.66
248,做空,2026-02-20 13:30:00,2026-02-20 14:30:00,1960.28,1954.43,2.29,50,0.0584,0.34,0.06,0.28
249,做多,2026-02-20 14:30:00,2026-02-20 15:15:00,1954.43,1959.51,2.29,50,0.0586,0.3,0.06,0.24
250,做空,2026-02-20 15:15:00,2026-02-20 18:40:00,1959.51,1961.18,2.29,50,0.0585,-0.1,0.06,-0.16
251,做多,2026-02-20 18:40:00,2026-02-20 20:50:00,1961.18,1946.59,2.29,50,0.0584,-0.85,0.06,-0.91
252,做空,2026-02-20 20:50:00,2026-02-20 21:00:00,1946.59,1943.99,2.28,50,0.0586,0.15,0.06,0.1
253,做多,2026-02-20 21:00:00,2026-02-20 22:30:00,1943.99,1944.3,2.28,50,0.0587,0.02,0.06,-0.04
254,做空,2026-02-20 22:30:00,2026-02-21 06:10:00,1944.3,1964.49,2.28,50,0.0586,-1.18,0.06,-1.24
255,做多,2026-02-21 06:10:00,2026-02-21 07:05:00,1964.49,1968.92,2.27,50,0.0577,0.26,0.06,0.2
256,做空,2026-02-21 07:05:00,2026-02-21 08:45:00,1968.92,1963.92,2.27,50,0.0576,0.29,0.06,0.23
257,做多,2026-02-21 08:45:00,2026-02-21 12:25:00,1963.92,1959.01,2.28,50,0.058,-0.29,0.06,-0.34
258,做空,2026-02-21 12:25:00,2026-02-21 20:25:00,1959.01,1969.99,2.28,50,0.0581,-0.64,0.06,-0.7
259,做多,2026-02-21 20:25:00,2026-02-21 22:05:00,1969.99,1981.03,2.27,50,0.0576,0.64,0.06,0.58
260,做空,2026-02-21 22:05:00,2026-02-21 23:30:00,1981.03,1988.19,2.27,50,0.0574,-0.41,0.06,-0.47
261,做多,2026-02-21 23:30:00,2026-02-21 23:40:00,1988.19,1989.55,2.27,50,0.057,0.08,0.06,0.02
262,做空,2026-02-21 23:40:00,2026-02-22 00:00:00,1989.55,1979.46,2.27,50,0.057,0.58,0.06,0.52
263,做多,2026-02-22 00:00:00,2026-02-22 01:40:00,1979.46,1988.74,2.27,50,0.0574,0.53,0.06,0.48
264,做空,2026-02-22 01:40:00,2026-02-22 03:50:00,1988.74,1987.85,2.28,50,0.0572,0.05,0.06,-0.01
265,做多,2026-02-22 03:50:00,2026-02-22 09:00:00,1987.85,1973.58,2.28,50,0.0573,-0.82,0.06,-0.87
266,做空,2026-02-22 09:00:00,2026-02-22 11:15:00,1973.58,1968.26,2.28,50,0.0577,0.31,0.06,0.25
267,做多,2026-02-22 11:15:00,2026-02-22 15:25:00,1968.26,1975.34,2.28,50,0.0579,0.41,0.06,0.35
268,做空,2026-02-22 15:25:00,2026-02-22 16:20:00,1975.34,1973.39,2.28,50,0.0577,0.11,0.06,0.06
269,做多,2026-02-22 16:20:00,2026-02-22 17:15:00,1973.39,1973.0,2.28,50,0.0578,-0.02,0.06,-0.08
270,做空,2026-02-22 17:15:00,2026-02-22 20:00:00,1973.0,1976.79,2.28,50,0.0578,-0.22,0.06,-0.28
271,做多,2026-02-22 20:00:00,2026-02-22 22:30:00,1976.79,1951.75,2.28,50,0.0576,-1.44,0.06,-1.5
272,做空,2026-02-22 22:30:00,2026-02-23 01:30:00,1951.75,1935.57,2.26,50,0.0579,0.94,0.06,0.88
273,做多,2026-02-23 01:30:00,2026-02-23 02:15:00,1935.57,1941.0,2.27,50,0.0586,0.32,0.06,0.26
274,做空,2026-02-23 02:15:00,2026-02-23 02:35:00,1941.0,1940.45,2.27,50,0.0585,0.03,0.06,-0.02
275,做多,2026-02-23 02:35:00,2026-02-23 02:50:00,1940.45,1942.01,2.27,50,0.0585,0.09,0.06,0.03
276,做空,2026-02-23 02:50:00,2026-02-23 04:50:00,1942.01,1939.47,2.27,50,0.0584,0.15,0.06,0.09
277,做多,2026-02-23 04:50:00,2026-02-23 05:35:00,1939.47,1949.72,2.27,50,0.0585,0.6,0.06,0.54
278,做空,2026-02-23 05:35:00,2026-02-23 07:20:00,1949.72,1951.6,2.28,50,0.0584,-0.11,0.06,-0.17
279,做多,2026-02-23 07:20:00,2026-02-23 07:55:00,1951.6,1957.01,2.27,50,0.0582,0.32,0.06,0.26
280,做空,2026-02-23 07:55:00,2026-02-23 08:25:00,1957.01,1947.41,2.28,50,0.0581,0.56,0.06,0.5
281,做多,2026-02-23 08:25:00,2026-02-23 11:05:00,1947.41,1866.1,2.29,50,0.0589,-4.79,0.05,-4.85
282,做空,2026-02-23 11:05:00,2026-02-23 11:15:00,1866.1,1858.39,2.25,50,0.0602,0.46,0.06,0.41
283,做多,2026-02-23 11:15:00,2026-02-23 13:15:00,1858.39,1866.46,2.25,50,0.0605,0.49,0.06,0.43
284,做空,2026-02-23 13:15:00,2026-02-23 16:35:00,1866.46,1877.82,2.25,50,0.0604,-0.69,0.06,-0.74
285,做多,2026-02-23 16:35:00,2026-02-23 17:10:00,1877.82,1884.54,2.25,50,0.0598,0.4,0.06,0.35
286,做空,2026-02-23 17:10:00,2026-02-23 20:30:00,1884.54,1907.37,2.25,50,0.0596,-1.36,0.06,-1.42
287,做多,2026-02-23 20:30:00,2026-02-23 20:35:00,1907.37,1919.57,2.23,50,0.0585,0.71,0.06,0.66
288,做空,2026-02-23 20:35:00,2026-02-23 22:35:00,1919.57,1906.62,2.24,50,0.0583,0.76,0.06,0.7
289,做多,2026-02-23 22:35:00,2026-02-24 10:30:00,1906.62,1849.89,2.25,50,0.0589,-3.34,0.05,-3.4
290,做空,2026-02-24 10:30:00,2026-02-24 11:00:00,1849.89,1836.02,2.22,50,0.06,0.83,0.06,0.78
291,做多,2026-02-24 11:00:00,2026-02-24 13:05:00,1836.02,1836.44,2.23,50,0.0607,0.03,0.06,-0.03
292,做空,2026-02-24 13:05:00,2026-02-24 13:20:00,1836.44,1815.44,2.23,50,0.0606,1.27,0.06,1.22
293,做多,2026-02-24 13:20:00,2026-02-24 16:30:00,1815.44,1832.52,2.24,50,0.0616,1.05,0.06,1.0
294,做空,2026-02-24 16:30:00,2026-02-24 17:05:00,1832.52,1824.3,2.25,50,0.0613,0.5,0.06,0.45
295,做多,2026-02-24 17:05:00,2026-02-24 20:10:00,1824.3,1826.47,2.25,50,0.0617,0.13,0.06,0.08
296,做空,2026-02-24 20:10:00,2026-02-24 21:20:00,1826.47,1819.72,2.25,50,0.0616,0.42,0.06,0.36
297,做多,2026-02-24 21:20:00,2026-02-24 22:35:00,1819.72,1821.38,2.25,50,0.062,0.1,0.06,0.05
298,做空,2026-02-24 22:35:00,2026-02-25 02:10:00,1821.38,1846.04,2.25,50,0.0619,-1.53,0.06,-1.58
299,做多,2026-02-25 02:10:00,2026-02-25 03:05:00,1846.04,1853.79,2.24,50,0.0606,0.47,0.06,0.41
300,做空,2026-02-25 03:05:00,2026-02-25 05:00:00,1853.79,1850.16,2.24,50,0.0605,0.22,0.06,0.16
301,做多,2026-02-25 05:00:00,2026-02-25 09:05:00,1850.16,1864.09,2.24,50,0.0606,0.84,0.06,0.79
302,做空,2026-02-25 09:05:00,2026-02-25 12:00:00,1864.09,1900.0,2.26,50,0.0607,-2.18,0.06,-2.24
303,做多,2026-02-25 12:00:00,2026-02-25 14:20:00,1900.0,1892.77,2.24,50,0.0589,-0.43,0.06,-0.48
304,做空,2026-02-25 14:20:00,2026-02-25 14:50:00,1892.77,1885.31,2.23,50,0.059,0.44,0.06,0.38
305,做多,2026-02-25 14:50:00,2026-02-25 16:20:00,1885.31,1896.51,2.24,50,0.0593,0.66,0.06,0.61
306,做空,2026-02-25 16:20:00,2026-02-25 19:05:00,1896.51,1914.28,2.24,50,0.0591,-1.05,0.06,-1.11
307,做多,2026-02-25 19:05:00,2026-02-25 19:10:00,1914.28,1914.26,2.23,50,0.0583,-0.0,0.06,-0.06
308,做空,2026-02-25 19:10:00,2026-02-26 03:10:00,1914.26,2065.7,2.23,50,0.0583,-8.82,0.06,-8.88
309,做多,2026-02-26 03:10:00,2026-02-26 04:50:00,2065.7,2077.16,2.14,50,0.0518,0.59,0.05,0.54
310,做空,2026-02-26 04:50:00,2026-02-26 06:40:00,2077.16,2088.92,2.15,50,0.0516,-0.61,0.05,-0.66
311,做多,2026-02-26 06:40:00,2026-02-26 10:45:00,2088.92,2048.37,2.14,50,0.0512,-2.08,0.05,-2.13
312,做空,2026-02-26 10:45:00,2026-02-26 13:15:00,2048.37,2059.76,2.13,50,0.0519,-0.59,0.05,-0.64
313,做多,2026-02-26 13:15:00,2026-02-26 14:05:00,2059.76,2070.3,2.12,50,0.0515,0.54,0.05,0.49
314,做空,2026-02-26 14:05:00,2026-02-26 14:45:00,2070.3,2061.26,2.12,50,0.0513,0.46,0.05,0.41
1 序号 方向 开仓时间 平仓时间 开仓价 平仓价 保证金 杠杆 数量 毛盈亏 手续费 净盈亏
2 1 做多 2026-02-01 00:45:00 2026-02-01 04:20:00 2503.98 2377.2 2.0 50 0.0399 -5.06 0.05 -5.11
3 2 做空 2026-02-01 04:20:00 2026-02-01 04:35:00 2377.2 2360.24 1.95 50 0.041 0.7 0.05 0.65
4 3 做多 2026-02-01 04:35:00 2026-02-01 05:00:00 2360.24 2373.42 1.95 50 0.0414 0.55 0.05 0.5
5 4 做空 2026-02-01 05:00:00 2026-02-01 09:05:00 2373.42 2433.28 1.96 50 0.0413 -2.47 0.05 -2.52
6 5 做多 2026-02-01 09:05:00 2026-02-01 12:10:00 2433.28 2451.12 1.94 50 0.0398 0.71 0.05 0.66
7 6 做空 2026-02-01 12:10:00 2026-02-01 13:00:00 2451.12 2439.68 1.94 50 0.0396 0.45 0.05 0.4
8 7 做多 2026-02-01 13:00:00 2026-02-01 15:40:00 2439.68 2430.01 1.95 50 0.0399 -0.39 0.05 -0.43
9 8 做空 2026-02-01 15:40:00 2026-02-01 18:30:00 2430.01 2406.59 1.94 50 0.0399 0.94 0.05 0.89
10 9 做多 2026-02-01 18:30:00 2026-02-01 19:35:00 2406.59 2404.44 1.95 50 0.0405 -0.09 0.05 -0.14
11 10 做空 2026-02-01 19:35:00 2026-02-01 20:20:00 2404.44 2387.44 1.95 50 0.0405 0.69 0.05 0.64
12 11 做多 2026-02-01 20:20:00 2026-02-01 21:00:00 2387.44 2402.27 1.95 50 0.0409 0.61 0.05 0.56
13 12 做空 2026-02-01 21:00:00 2026-02-01 21:40:00 2402.27 2392.0 1.96 50 0.0408 0.42 0.05 0.37
14 13 做多 2026-02-01 21:40:00 2026-02-02 00:25:00 2392.0 2318.96 1.96 50 0.041 -3.0 0.05 -3.04
15 14 做空 2026-02-02 00:25:00 2026-02-02 03:45:00 2318.96 2313.79 1.93 50 0.0416 0.22 0.05 0.17
16 15 做多 2026-02-02 03:45:00 2026-02-02 04:35:00 2313.79 2342.69 1.93 50 0.0418 1.21 0.05 1.16
17 16 做空 2026-02-02 04:35:00 2026-02-02 05:55:00 2342.69 2289.17 1.94 50 0.0415 2.22 0.05 2.17
18 17 做多 2026-02-02 05:55:00 2026-02-02 06:10:00 2289.17 2323.39 1.96 50 0.0429 1.47 0.05 1.42
19 18 做空 2026-02-02 06:10:00 2026-02-02 07:05:00 2323.39 2235.28 1.98 50 0.0426 3.75 0.05 3.7
20 19 做多 2026-02-02 07:05:00 2026-02-02 08:15:00 2235.28 2307.06 2.01 50 0.0451 3.23 0.05 3.18
21 20 做空 2026-02-02 08:15:00 2026-02-02 09:00:00 2307.06 2302.75 2.06 50 0.0446 0.19 0.05 0.14
22 21 做多 2026-02-02 09:00:00 2026-02-02 14:15:00 2302.75 2211.45 2.06 50 0.0447 -4.08 0.05 -4.13
23 22 做空 2026-02-02 14:15:00 2026-02-02 14:35:00 2211.45 2166.31 2.02 50 0.0456 2.06 0.05 2.01
24 23 做多 2026-02-02 14:35:00 2026-02-02 14:40:00 2166.31 2219.94 2.04 50 0.047 2.52 0.05 2.47
25 24 做空 2026-02-02 14:40:00 2026-02-02 17:15:00 2219.94 2250.82 2.06 50 0.0464 -1.43 0.05 -1.49
26 25 做多 2026-02-02 17:15:00 2026-02-02 17:25:00 2250.82 2277.79 2.05 50 0.0455 1.23 0.05 1.17
27 26 做空 2026-02-02 17:25:00 2026-02-02 22:10:00 2277.79 2305.44 2.06 50 0.0452 -1.25 0.05 -1.3
28 27 做多 2026-02-02 22:10:00 2026-02-02 22:35:00 2305.44 2350.92 2.04 50 0.0443 2.02 0.05 1.96
29 28 做空 2026-02-02 22:35:00 2026-02-03 00:50:00 2350.92 2357.15 2.06 50 0.0439 -0.27 0.05 -0.33
30 29 做多 2026-02-03 00:50:00 2026-02-03 09:00:00 2357.15 2349.37 2.06 50 0.0437 -0.34 0.05 -0.39
31 30 做空 2026-02-03 09:00:00 2026-02-03 10:10:00 2349.37 2327.51 2.06 50 0.0439 0.96 0.05 0.91
32 31 做多 2026-02-03 10:10:00 2026-02-03 15:10:00 2327.51 2322.01 2.07 50 0.0445 -0.24 0.05 -0.3
33 32 做空 2026-02-03 15:10:00 2026-02-03 16:10:00 2322.01 2311.05 2.07 50 0.0446 0.49 0.05 0.44
34 33 做多 2026-02-03 16:10:00 2026-02-03 20:10:00 2311.05 2290.92 2.07 50 0.0449 -0.9 0.05 -0.95
35 34 做空 2026-02-03 20:10:00 2026-02-03 22:15:00 2290.92 2299.99 2.06 50 0.045 -0.41 0.05 -0.46
36 35 做多 2026-02-03 22:15:00 2026-02-03 23:25:00 2299.99 2292.96 2.06 50 0.0447 -0.31 0.05 -0.37
37 36 做空 2026-02-03 23:25:00 2026-02-04 00:15:00 2292.96 2265.27 2.05 50 0.0448 1.24 0.05 1.19
38 37 做多 2026-02-04 00:15:00 2026-02-04 03:10:00 2265.27 2178.1 2.07 50 0.0456 -3.97 0.05 -4.02
39 38 做空 2026-02-04 03:10:00 2026-02-04 06:05:00 2178.1 2259.5 2.02 50 0.0465 -3.78 0.05 -3.84
40 39 做多 2026-02-04 06:05:00 2026-02-04 09:00:00 2259.5 2264.51 1.99 50 0.0439 0.22 0.05 0.17
41 40 做空 2026-02-04 09:00:00 2026-02-04 10:30:00 2264.51 2254.5 2.0 50 0.0441 0.44 0.05 0.39
42 41 做多 2026-02-04 10:30:00 2026-02-04 11:10:00 2254.5 2275.54 2.0 50 0.0443 0.93 0.05 0.88
43 42 做空 2026-02-04 11:10:00 2026-02-04 12:25:00 2275.54 2274.79 2.01 50 0.0441 0.03 0.05 -0.02
44 43 做多 2026-02-04 12:25:00 2026-02-04 20:20:00 2274.79 2253.17 2.01 50 0.0441 -0.95 0.05 -1.0
45 44 做空 2026-02-04 20:20:00 2026-02-04 20:25:00 2253.17 2234.15 2.0 50 0.0443 0.84 0.05 0.79
46 45 做多 2026-02-04 20:25:00 2026-02-05 01:00:00 2234.15 2141.81 2.0 50 0.0449 -4.14 0.05 -4.19
47 46 做空 2026-02-05 01:00:00 2026-02-05 01:35:00 2141.81 2100.23 1.96 50 0.0458 1.9 0.05 1.86
48 47 做多 2026-02-05 01:35:00 2026-02-05 02:25:00 2100.23 2122.35 1.98 50 0.0471 1.04 0.05 0.99
49 48 做空 2026-02-05 02:25:00 2026-02-05 03:20:00 2122.35 2138.64 1.99 50 0.0469 -0.76 0.05 -0.81
50 49 做多 2026-02-05 03:20:00 2026-02-05 03:45:00 2138.64 2159.36 1.98 50 0.0463 0.96 0.05 0.91
51 50 做空 2026-02-05 03:45:00 2026-02-05 05:05:00 2159.36 2158.46 1.99 50 0.0461 0.04 0.05 -0.01
52 51 做多 2026-02-05 05:05:00 2026-02-05 10:35:00 2158.46 2153.58 1.99 50 0.0461 -0.22 0.05 -0.27
53 52 做空 2026-02-05 10:35:00 2026-02-05 11:15:00 2153.58 2113.19 2.0 50 0.0463 1.87 0.05 1.82
54 53 做多 2026-02-05 11:15:00 2026-02-05 12:40:00 2113.19 2121.89 2.01 50 0.0477 0.41 0.05 0.36
55 54 做空 2026-02-05 12:40:00 2026-02-05 13:20:00 2121.89 2085.5 2.02 50 0.0475 1.73 0.05 1.68
56 55 做多 2026-02-05 13:20:00 2026-02-05 15:00:00 2085.5 2115.39 2.03 50 0.0488 1.46 0.05 1.41
57 56 做空 2026-02-05 15:00:00 2026-02-05 15:55:00 2115.39 2089.53 2.05 50 0.0484 1.25 0.05 1.2
58 57 做多 2026-02-05 15:55:00 2026-02-05 16:25:00 2089.53 2110.34 2.06 50 0.0493 1.03 0.05 0.97
59 58 做空 2026-02-05 16:25:00 2026-02-05 18:55:00 2110.34 2096.48 2.07 50 0.049 0.68 0.05 0.63
60 59 做多 2026-02-05 18:55:00 2026-02-05 21:05:00 2096.48 2071.86 2.07 50 0.0495 -1.22 0.05 -1.27
61 60 做空 2026-02-05 21:05:00 2026-02-06 01:20:00 2071.86 1987.12 2.06 50 0.0497 4.21 0.05 4.16
62 61 做多 2026-02-06 01:20:00 2026-02-06 06:25:00 1987.12 1871.41 2.1 50 0.0529 -6.12 0.05 -6.17
63 62 做空 2026-02-06 06:25:00 2026-02-06 08:10:00 1871.41 1766.83 2.04 50 0.0545 5.7 0.05 5.65
64 63 做多 2026-02-06 08:10:00 2026-02-06 09:35:00 1766.83 1909.28 2.11 50 0.0596 8.49 0.06 8.43
65 64 做空 2026-02-06 09:35:00 2026-02-06 11:50:00 1909.28 1885.62 2.19 50 0.0573 1.36 0.05 1.3
66 65 做多 2026-02-06 11:50:00 2026-02-06 13:05:00 1885.62 1911.81 2.2 50 0.0584 1.53 0.06 1.47
67 66 做空 2026-02-06 13:05:00 2026-02-06 16:15:00 1911.81 1879.3 2.22 50 0.0579 1.88 0.05 1.83
68 67 做多 2026-02-06 16:15:00 2026-02-06 17:10:00 1879.3 1885.97 2.23 50 0.0594 0.4 0.06 0.34
69 68 做空 2026-02-06 17:10:00 2026-02-06 19:00:00 1885.97 1924.17 2.24 50 0.0593 -2.26 0.06 -2.32
70 69 做多 2026-02-06 19:00:00 2026-02-06 19:15:00 1924.17 1924.74 2.21 50 0.0575 0.03 0.06 -0.02
71 70 做空 2026-02-06 19:15:00 2026-02-06 20:05:00 1924.74 1922.87 2.21 50 0.0575 0.11 0.06 0.05
72 71 做多 2026-02-06 20:05:00 2026-02-06 21:00:00 1922.87 1932.91 2.21 50 0.0575 0.58 0.06 0.52
73 72 做空 2026-02-06 21:00:00 2026-02-06 23:15:00 1932.91 1971.8 2.22 50 0.0573 -2.23 0.06 -2.29
74 73 做多 2026-02-06 23:15:00 2026-02-06 23:45:00 1971.8 1990.88 2.19 50 0.0556 1.06 0.06 1.01
75 74 做空 2026-02-06 23:45:00 2026-02-07 02:05:00 1990.88 2032.15 2.2 50 0.0553 -2.28 0.06 -2.34
76 75 做多 2026-02-07 02:05:00 2026-02-07 04:35:00 2032.15 2073.09 2.18 50 0.0536 2.19 0.06 2.14
77 76 做空 2026-02-07 04:35:00 2026-02-07 07:15:00 2073.09 2053.48 2.2 50 0.053 1.04 0.05 0.99
78 77 做多 2026-02-07 07:15:00 2026-02-07 11:00:00 2053.48 2064.11 2.21 50 0.0538 0.57 0.06 0.52
79 78 做空 2026-02-07 11:00:00 2026-02-07 14:15:00 2064.11 2073.13 2.23 50 0.054 -0.49 0.06 -0.54
80 79 做多 2026-02-07 14:15:00 2026-02-07 19:55:00 2073.13 2017.76 2.22 50 0.0536 -2.97 0.05 -3.02
81 80 做空 2026-02-07 19:55:00 2026-02-07 21:35:00 2017.76 2027.6 2.19 50 0.0543 -0.53 0.06 -0.59
82 81 做多 2026-02-07 21:35:00 2026-02-08 02:15:00 2027.6 2076.87 2.18 50 0.0539 2.65 0.06 2.6
83 82 做空 2026-02-08 02:15:00 2026-02-08 06:10:00 2076.87 2099.58 2.21 50 0.0532 -1.21 0.06 -1.26
84 83 做多 2026-02-08 06:10:00 2026-02-08 10:20:00 2099.58 2102.05 2.2 50 0.0523 0.13 0.05 0.07
85 84 做空 2026-02-08 10:20:00 2026-02-08 12:15:00 2102.05 2074.69 2.2 50 0.0524 1.43 0.05 1.38
86 85 做多 2026-02-08 12:15:00 2026-02-08 12:55:00 2074.69 2086.46 2.22 50 0.0534 0.63 0.06 0.57
87 86 做空 2026-02-08 12:55:00 2026-02-08 14:05:00 2086.46 2079.62 2.22 50 0.0532 0.36 0.06 0.31
88 87 做多 2026-02-08 14:05:00 2026-02-08 16:20:00 2079.62 2096.67 2.22 50 0.0535 0.91 0.06 0.86
89 88 做空 2026-02-08 16:20:00 2026-02-08 17:35:00 2096.67 2103.15 2.23 50 0.0532 -0.34 0.06 -0.4
90 89 做多 2026-02-08 17:35:00 2026-02-08 18:05:00 2103.15 2107.65 2.23 50 0.053 0.24 0.06 0.18
91 90 做空 2026-02-08 18:05:00 2026-02-08 18:20:00 2107.65 2087.85 2.23 50 0.0529 1.05 0.06 0.99
92 91 做多 2026-02-08 18:20:00 2026-02-08 19:15:00 2087.85 2110.36 2.24 50 0.0536 1.21 0.06 1.15
93 92 做空 2026-02-08 19:15:00 2026-02-08 21:05:00 2110.36 2123.28 2.25 50 0.0533 -0.69 0.06 -0.75
94 93 做多 2026-02-08 21:05:00 2026-02-09 02:15:00 2123.28 2094.55 2.24 50 0.0528 -1.52 0.06 -1.57
95 94 做空 2026-02-09 02:15:00 2026-02-09 04:45:00 2094.55 2104.6 2.22 50 0.0531 -0.53 0.06 -0.59
96 95 做多 2026-02-09 04:45:00 2026-02-09 06:55:00 2104.6 2107.44 2.22 50 0.0527 0.15 0.06 0.09
97 96 做空 2026-02-09 06:55:00 2026-02-09 08:25:00 2107.44 2081.4 2.22 50 0.0526 1.37 0.05 1.32
98 97 做多 2026-02-09 08:25:00 2026-02-09 09:20:00 2081.4 2098.76 2.24 50 0.0539 0.94 0.06 0.88
99 98 做空 2026-02-09 09:20:00 2026-02-09 09:40:00 2098.76 2063.62 2.25 50 0.0537 1.89 0.06 1.83
100 99 做多 2026-02-09 09:40:00 2026-02-09 10:35:00 2063.62 2079.96 2.27 50 0.055 0.9 0.06 0.84
101 100 做空 2026-02-09 10:35:00 2026-02-09 14:15:00 2079.96 2075.79 2.28 50 0.0548 0.23 0.06 0.17
102 101 做多 2026-02-09 14:15:00 2026-02-09 15:10:00 2075.79 2084.33 2.28 50 0.0549 0.47 0.06 0.41
103 102 做空 2026-02-09 15:10:00 2026-02-09 15:35:00 2084.33 2067.23 2.28 50 0.0548 0.94 0.06 0.88
104 103 做多 2026-02-09 15:35:00 2026-02-09 20:00:00 2067.23 2031.57 2.29 50 0.0554 -1.98 0.06 -2.03
105 104 做空 2026-02-09 20:00:00 2026-02-10 02:20:00 2031.57 2115.84 2.27 50 0.0559 -4.71 0.06 -4.77
106 105 做多 2026-02-10 02:20:00 2026-02-10 03:30:00 2115.84 2131.85 2.22 50 0.0525 0.84 0.06 0.78
107 106 做空 2026-02-10 03:30:00 2026-02-10 04:00:00 2131.85 2130.8 2.23 50 0.0523 0.05 0.06 -0.0
108 107 做多 2026-02-10 04:00:00 2026-02-10 04:05:00 2130.8 2135.34 2.23 50 0.0523 0.24 0.06 0.18
109 108 做空 2026-02-10 04:05:00 2026-02-10 04:15:00 2135.34 2122.59 2.23 50 0.0522 0.67 0.06 0.61
110 109 做多 2026-02-10 04:15:00 2026-02-10 16:20:00 2122.59 2011.91 2.24 50 0.0527 -5.83 0.05 -5.88
111 110 做空 2026-02-10 16:20:00 2026-02-10 17:50:00 2011.91 2015.22 2.19 50 0.0544 -0.18 0.05 -0.23
112 111 做多 2026-02-10 17:50:00 2026-02-10 19:30:00 2015.22 2019.66 2.19 50 0.0543 0.24 0.05 0.19
113 112 做空 2026-02-10 19:30:00 2026-02-10 19:40:00 2019.66 2007.94 2.19 50 0.0542 0.63 0.05 0.58
114 113 做多 2026-02-10 19:40:00 2026-02-10 21:15:00 2007.94 2012.02 2.19 50 0.0546 0.22 0.05 0.17
115 114 做空 2026-02-10 21:15:00 2026-02-10 22:35:00 2012.02 2012.47 2.19 50 0.0545 -0.02 0.05 -0.08
116 115 做多 2026-02-10 22:35:00 2026-02-11 00:10:00 2012.47 2032.87 2.19 50 0.0545 1.11 0.06 1.06
117 116 做空 2026-02-11 00:10:00 2026-02-11 02:20:00 2032.87 2013.69 2.2 50 0.0542 1.04 0.05 0.98
118 117 做多 2026-02-11 02:20:00 2026-02-11 03:30:00 2013.69 2013.39 2.21 50 0.0549 -0.02 0.06 -0.07
119 118 做空 2026-02-11 03:30:00 2026-02-11 05:30:00 2013.39 2001.89 2.21 50 0.0549 0.63 0.05 0.58
120 119 做多 2026-02-11 05:30:00 2026-02-11 07:35:00 2001.89 2018.01 2.22 50 0.0554 0.89 0.06 0.84
121 120 做空 2026-02-11 07:35:00 2026-02-11 09:20:00 2018.01 2018.06 2.22 50 0.0551 -0.0 0.06 -0.06
122 121 做多 2026-02-11 09:20:00 2026-02-11 10:20:00 2018.06 2027.05 2.23 50 0.0553 0.5 0.06 0.44
123 122 做空 2026-02-11 10:20:00 2026-02-11 10:45:00 2027.05 2014.32 2.24 50 0.0552 0.7 0.06 0.65
124 123 做多 2026-02-11 10:45:00 2026-02-11 15:35:00 2014.32 1951.85 2.24 50 0.0557 -3.48 0.05 -3.53
125 124 做空 2026-02-11 15:35:00 2026-02-11 16:05:00 1951.85 1939.79 2.21 50 0.0566 0.68 0.05 0.63
126 125 做多 2026-02-11 16:05:00 2026-02-11 17:00:00 1939.79 1949.26 2.21 50 0.057 0.54 0.06 0.48
127 126 做空 2026-02-11 17:00:00 2026-02-11 17:45:00 1949.26 1944.0 2.22 50 0.0569 0.3 0.06 0.24
128 127 做多 2026-02-11 17:45:00 2026-02-11 20:05:00 1944.0 1961.67 2.22 50 0.0571 1.01 0.06 0.95
129 128 做空 2026-02-11 20:05:00 2026-02-11 21:25:00 1961.67 1948.28 2.23 50 0.0568 0.76 0.06 0.71
130 129 做多 2026-02-11 21:25:00 2026-02-11 21:30:00 1948.28 1963.0 2.23 50 0.0574 0.84 0.06 0.79
131 130 做空 2026-02-11 21:30:00 2026-02-12 03:30:00 1963.0 1943.01 2.24 50 0.0571 1.14 0.06 1.09
132 131 做多 2026-02-12 03:30:00 2026-02-12 03:45:00 1943.01 1952.12 2.25 50 0.058 0.53 0.06 0.47
133 132 做空 2026-02-12 03:45:00 2026-02-12 04:25:00 1952.12 1941.02 2.26 50 0.0578 0.64 0.06 0.59
134 133 做多 2026-02-12 04:25:00 2026-02-12 05:20:00 1941.02 1968.22 2.26 50 0.0583 1.58 0.06 1.53
135 134 做空 2026-02-12 05:20:00 2026-02-12 06:10:00 1968.22 1960.92 2.28 50 0.0578 0.42 0.06 0.37
136 135 做多 2026-02-12 06:10:00 2026-02-12 08:05:00 1960.92 1951.09 2.28 50 0.0581 -0.57 0.06 -0.63
137 136 做空 2026-02-12 08:05:00 2026-02-12 12:30:00 1951.09 1967.55 2.29 50 0.0586 -0.97 0.06 -1.02
138 137 做多 2026-02-12 12:30:00 2026-02-12 14:10:00 1967.55 1970.2 2.28 50 0.0579 0.15 0.06 0.1
139 138 做空 2026-02-12 14:10:00 2026-02-12 16:25:00 1970.2 1958.73 2.28 50 0.0578 0.66 0.06 0.61
140 139 做多 2026-02-12 16:25:00 2026-02-12 17:10:00 1958.73 1968.27 2.28 50 0.0583 0.56 0.06 0.5
141 140 做空 2026-02-12 17:10:00 2026-02-12 20:50:00 1968.27 1976.76 2.29 50 0.0581 -0.49 0.06 -0.55
142 141 做多 2026-02-12 20:50:00 2026-02-12 21:30:00 1976.76 1985.8 2.28 50 0.0577 0.52 0.06 0.46
143 142 做空 2026-02-12 21:30:00 2026-02-12 22:45:00 1985.8 1978.53 2.29 50 0.0575 0.42 0.06 0.36
144 143 做多 2026-02-12 22:45:00 2026-02-13 01:35:00 1978.53 1920.11 2.29 50 0.0578 -3.38 0.06 -3.43
145 144 做空 2026-02-13 01:35:00 2026-02-13 04:10:00 1920.11 1920.22 2.25 50 0.0587 -0.01 0.06 -0.06
146 145 做多 2026-02-13 04:10:00 2026-02-13 06:30:00 1920.22 1927.46 2.25 50 0.0587 0.42 0.06 0.37
147 146 做空 2026-02-13 06:30:00 2026-02-13 08:15:00 1927.46 1940.1 2.26 50 0.0585 -0.74 0.06 -0.8
148 147 做多 2026-02-13 08:15:00 2026-02-13 09:05:00 1940.1 1941.5 2.26 50 0.0582 0.08 0.06 0.02
149 148 做空 2026-02-13 09:05:00 2026-02-13 10:20:00 1941.5 1946.1 2.26 50 0.0582 -0.27 0.06 -0.32
150 149 做多 2026-02-13 10:20:00 2026-02-13 10:50:00 1946.1 1947.9 2.25 50 0.0579 0.1 0.06 0.05
151 150 做空 2026-02-13 10:50:00 2026-02-13 12:10:00 1947.9 1950.84 2.25 50 0.0579 -0.17 0.06 -0.23
152 151 做多 2026-02-13 12:10:00 2026-02-13 12:25:00 1950.84 1949.6 2.25 50 0.0577 -0.07 0.06 -0.13
153 152 做空 2026-02-13 12:25:00 2026-02-13 12:30:00 1949.6 1950.08 2.25 50 0.0577 -0.03 0.06 -0.08
154 153 做多 2026-02-13 12:30:00 2026-02-13 16:20:00 1950.08 1939.99 2.25 50 0.0576 -0.58 0.06 -0.64
155 154 做空 2026-02-13 16:20:00 2026-02-13 19:05:00 1939.99 1955.6 2.24 50 0.0578 -0.9 0.06 -0.96
156 155 做多 2026-02-13 19:05:00 2026-02-13 20:25:00 1955.6 1961.63 2.23 50 0.057 0.34 0.06 0.29
157 156 做空 2026-02-13 20:25:00 2026-02-13 21:05:00 1961.63 1957.39 2.23 50 0.0569 0.24 0.06 0.19
158 157 做多 2026-02-13 21:05:00 2026-02-13 21:20:00 1957.39 1967.19 2.23 50 0.0571 0.56 0.06 0.5
159 158 做空 2026-02-13 21:20:00 2026-02-14 02:00:00 1967.19 2056.73 2.24 50 0.0569 -5.1 0.06 -5.15
160 159 做多 2026-02-14 02:00:00 2026-02-14 02:35:00 2056.73 2061.34 2.19 50 0.0532 0.25 0.05 0.19
161 160 做空 2026-02-14 02:35:00 2026-02-14 04:15:00 2061.34 2049.81 2.19 50 0.0531 0.61 0.05 0.56
162 161 做多 2026-02-14 04:15:00 2026-02-14 05:00:00 2049.81 2054.62 2.19 50 0.0535 0.26 0.05 0.2
163 162 做空 2026-02-14 05:00:00 2026-02-14 07:35:00 2054.62 2047.52 2.19 50 0.0534 0.38 0.05 0.32
164 163 做多 2026-02-14 07:35:00 2026-02-14 09:50:00 2047.52 2053.35 2.2 50 0.0537 0.31 0.06 0.26
165 164 做空 2026-02-14 09:50:00 2026-02-14 09:55:00 2053.35 2053.37 2.22 50 0.054 -0.0 0.06 -0.06
166 165 做多 2026-02-14 09:55:00 2026-02-14 11:25:00 2053.37 2050.56 2.22 50 0.0539 -0.15 0.06 -0.21
167 166 做空 2026-02-14 11:25:00 2026-02-14 13:00:00 2050.56 2052.1 2.21 50 0.054 -0.08 0.06 -0.14
168 167 做多 2026-02-14 13:00:00 2026-02-14 15:30:00 2052.1 2052.31 2.21 50 0.0539 0.01 0.06 -0.04
169 168 做空 2026-02-14 15:30:00 2026-02-14 15:35:00 2052.31 2050.7 2.21 50 0.0538 0.09 0.06 0.03
170 169 做多 2026-02-14 15:35:00 2026-02-14 15:50:00 2050.7 2057.65 2.21 50 0.0539 0.37 0.06 0.32
171 170 做空 2026-02-14 15:50:00 2026-02-14 20:15:00 2057.65 2094.01 2.21 50 0.0538 -1.95 0.06 -2.01
172 171 做多 2026-02-14 20:15:00 2026-02-14 22:20:00 2094.01 2069.5 2.19 50 0.0523 -1.28 0.05 -1.34
173 172 做空 2026-02-14 22:20:00 2026-02-15 00:00:00 2069.5 2086.28 2.18 50 0.0526 -0.88 0.05 -0.94
174 173 做多 2026-02-15 00:00:00 2026-02-15 02:20:00 2086.28 2082.08 2.17 50 0.052 -0.22 0.05 -0.27
175 174 做空 2026-02-15 02:20:00 2026-02-15 03:15:00 2082.08 2084.26 2.16 50 0.052 -0.11 0.05 -0.17
176 175 做多 2026-02-15 03:15:00 2026-02-15 03:30:00 2084.26 2085.58 2.16 50 0.0519 0.07 0.05 0.01
177 176 做空 2026-02-15 03:30:00 2026-02-15 07:15:00 2085.58 2087.12 2.16 50 0.0518 -0.08 0.05 -0.13
178 177 做多 2026-02-15 07:15:00 2026-02-15 10:05:00 2087.12 2058.92 2.16 50 0.0517 -1.46 0.05 -1.51
179 178 做空 2026-02-15 10:05:00 2026-02-15 10:20:00 2058.92 2054.96 2.16 50 0.0524 0.21 0.05 0.15
180 179 做多 2026-02-15 10:20:00 2026-02-15 12:05:00 2054.96 2072.91 2.16 50 0.0525 0.94 0.05 0.89
181 180 做空 2026-02-15 12:05:00 2026-02-15 14:00:00 2072.91 2086.85 2.17 50 0.0523 -0.73 0.05 -0.78
182 181 做多 2026-02-15 14:00:00 2026-02-15 14:35:00 2086.85 2090.16 2.16 50 0.0517 0.17 0.05 0.12
183 182 做空 2026-02-15 14:35:00 2026-02-15 16:25:00 2090.16 2075.54 2.16 50 0.0517 0.76 0.05 0.7
184 183 做多 2026-02-15 16:25:00 2026-02-15 20:20:00 2075.54 2062.81 2.17 50 0.0522 -0.66 0.05 -0.72
185 184 做空 2026-02-15 20:20:00 2026-02-15 20:45:00 2062.81 2046.45 2.16 50 0.0523 0.86 0.05 0.8
186 185 做多 2026-02-15 20:45:00 2026-02-15 22:30:00 2046.45 1999.25 2.17 50 0.0529 -2.5 0.05 -2.55
187 186 做空 2026-02-15 22:30:00 2026-02-16 00:05:00 1999.25 1999.62 2.14 50 0.0535 -0.02 0.05 -0.07
188 187 做多 2026-02-16 00:05:00 2026-02-16 05:30:00 1999.62 1959.09 2.14 50 0.0535 -2.17 0.05 -2.22
189 188 做空 2026-02-16 05:30:00 2026-02-16 07:00:00 1959.09 1956.8 2.12 50 0.054 0.12 0.05 0.07
190 189 做多 2026-02-16 07:00:00 2026-02-16 08:05:00 1956.8 1973.71 2.12 50 0.0541 0.91 0.05 0.86
191 190 做空 2026-02-16 08:05:00 2026-02-16 10:20:00 1973.71 1958.9 2.14 50 0.0541 0.8 0.05 0.75
192 191 做多 2026-02-16 10:20:00 2026-02-16 12:00:00 1958.9 1963.67 2.14 50 0.0547 0.26 0.05 0.21
193 192 做空 2026-02-16 12:00:00 2026-02-16 19:05:00 1963.67 1973.75 2.14 50 0.0546 -0.55 0.05 -0.6
194 193 做多 2026-02-16 19:05:00 2026-02-16 20:25:00 1973.75 1991.31 2.14 50 0.0542 0.95 0.05 0.9
195 194 做空 2026-02-16 20:25:00 2026-02-16 21:25:00 1991.31 1976.1 2.15 50 0.0539 0.82 0.05 0.77
196 195 做多 2026-02-16 21:25:00 2026-02-16 22:50:00 1976.1 1969.72 2.15 50 0.0545 -0.35 0.05 -0.4
197 196 做空 2026-02-16 22:50:00 2026-02-16 23:30:00 1969.72 1940.59 2.15 50 0.0545 1.59 0.05 1.54
198 197 做多 2026-02-16 23:30:00 2026-02-17 01:30:00 1940.59 1983.81 2.16 50 0.0557 2.41 0.06 2.35
199 198 做空 2026-02-17 01:30:00 2026-02-17 02:35:00 1983.81 1974.99 2.19 50 0.0551 0.49 0.05 0.43
200 199 做多 2026-02-17 02:35:00 2026-02-17 03:35:00 1974.99 1977.15 2.19 50 0.0555 0.12 0.05 0.06
201 200 做空 2026-02-17 03:35:00 2026-02-17 06:45:00 1977.15 1991.2 2.19 50 0.0554 -0.78 0.06 -0.83
202 201 做多 2026-02-17 06:45:00 2026-02-17 07:05:00 1991.2 1993.01 2.18 50 0.0548 0.1 0.05 0.04
203 202 做空 2026-02-17 07:05:00 2026-02-17 10:00:00 1993.01 1986.61 2.18 50 0.0547 0.35 0.05 0.3
204 203 做多 2026-02-17 10:00:00 2026-02-17 12:20:00 1986.61 1996.22 2.2 50 0.0553 0.53 0.06 0.48
205 204 做空 2026-02-17 12:20:00 2026-02-17 13:15:00 1996.22 1975.63 2.2 50 0.0551 1.14 0.05 1.08
206 205 做多 2026-02-17 13:15:00 2026-02-17 14:25:00 1975.63 1981.79 2.21 50 0.056 0.34 0.06 0.29
207 206 做空 2026-02-17 14:25:00 2026-02-17 15:05:00 1981.79 1975.75 2.21 50 0.0558 0.34 0.06 0.28
208 207 做多 2026-02-17 15:05:00 2026-02-17 18:00:00 1975.75 1974.07 2.22 50 0.0561 -0.09 0.06 -0.15
209 208 做空 2026-02-17 18:00:00 2026-02-17 18:15:00 1974.07 1963.99 2.21 50 0.0561 0.57 0.06 0.51
210 209 做多 2026-02-17 18:15:00 2026-02-17 19:00:00 1963.99 1966.66 2.22 50 0.0565 0.15 0.06 0.1
211 210 做空 2026-02-17 19:00:00 2026-02-17 19:45:00 1966.66 1966.75 2.22 50 0.0564 -0.01 0.06 -0.06
212 211 做多 2026-02-17 19:45:00 2026-02-17 20:05:00 1966.75 1973.21 2.22 50 0.0564 0.36 0.06 0.31
213 212 做空 2026-02-17 20:05:00 2026-02-17 20:50:00 1973.21 1970.9 2.22 50 0.0563 0.13 0.06 0.07
214 213 做多 2026-02-17 20:50:00 2026-02-17 21:10:00 1970.9 1979.05 2.22 50 0.0563 0.46 0.06 0.4
215 214 做空 2026-02-17 21:10:00 2026-02-17 22:35:00 1979.05 1978.92 2.22 50 0.0562 0.01 0.06 -0.05
216 215 做多 2026-02-17 22:35:00 2026-02-18 00:00:00 1978.92 1986.74 2.22 50 0.0562 0.44 0.06 0.38
217 216 做空 2026-02-18 00:00:00 2026-02-18 01:10:00 1986.74 1964.67 2.23 50 0.056 1.24 0.06 1.18
218 217 做多 2026-02-18 01:10:00 2026-02-18 02:20:00 1964.67 1986.33 2.24 50 0.0569 1.23 0.06 1.18
219 218 做空 2026-02-18 02:20:00 2026-02-18 04:30:00 1986.33 1993.32 2.25 50 0.0566 -0.4 0.06 -0.45
220 219 做多 2026-02-18 04:30:00 2026-02-18 04:40:00 1993.32 1997.94 2.24 50 0.0563 0.26 0.06 0.2
221 220 做空 2026-02-18 04:40:00 2026-02-18 06:50:00 1997.94 1993.94 2.24 50 0.0562 0.22 0.06 0.17
222 221 做多 2026-02-18 06:50:00 2026-02-18 11:25:00 1993.94 1993.01 2.25 50 0.0563 -0.05 0.06 -0.11
223 222 做空 2026-02-18 11:25:00 2026-02-18 12:35:00 1993.01 1994.19 2.26 50 0.0568 -0.07 0.06 -0.12
224 223 做多 2026-02-18 12:35:00 2026-02-18 14:20:00 1994.19 2001.76 2.26 50 0.0567 0.43 0.06 0.37
225 224 做空 2026-02-18 14:20:00 2026-02-18 17:50:00 2001.76 2017.24 2.26 50 0.0566 -0.88 0.06 -0.93
226 225 做多 2026-02-18 17:50:00 2026-02-18 22:30:00 2017.24 1967.63 2.25 50 0.0559 -2.77 0.05 -2.83
227 226 做空 2026-02-18 22:30:00 2026-02-18 22:45:00 1967.63 1955.53 2.23 50 0.0566 0.68 0.06 0.63
228 227 做多 2026-02-18 22:45:00 2026-02-18 23:05:00 1955.53 1983.75 2.23 50 0.0571 1.61 0.06 1.55
229 228 做空 2026-02-18 23:05:00 2026-02-19 02:15:00 1983.75 1963.63 2.25 50 0.0566 1.14 0.06 1.08
230 229 做多 2026-02-19 02:15:00 2026-02-19 04:30:00 1963.63 1947.9 2.26 50 0.0575 -0.9 0.06 -0.96
231 230 做空 2026-02-19 04:30:00 2026-02-19 07:15:00 1947.9 1946.27 2.25 50 0.0577 0.09 0.06 0.04
232 231 做多 2026-02-19 07:15:00 2026-02-19 07:25:00 1946.27 1947.6 2.25 50 0.0577 0.08 0.06 0.02
233 232 做空 2026-02-19 07:25:00 2026-02-19 08:25:00 1947.6 1952.44 2.25 50 0.0577 -0.28 0.06 -0.34
234 233 做多 2026-02-19 08:25:00 2026-02-19 09:10:00 1952.44 1969.01 2.25 50 0.0577 0.96 0.06 0.9
235 234 做空 2026-02-19 09:10:00 2026-02-19 12:45:00 1969.01 1971.59 2.26 50 0.0574 -0.15 0.06 -0.2
236 235 做多 2026-02-19 12:45:00 2026-02-19 14:05:00 1971.59 1971.53 2.26 50 0.0573 -0.0 0.06 -0.06
237 236 做空 2026-02-19 14:05:00 2026-02-19 15:40:00 1971.53 1981.57 2.26 50 0.0573 -0.57 0.06 -0.63
238 237 做多 2026-02-19 15:40:00 2026-02-19 17:20:00 1981.57 1977.7 2.25 50 0.0568 -0.22 0.06 -0.28
239 238 做空 2026-02-19 17:20:00 2026-02-19 17:45:00 1977.7 1961.93 2.25 50 0.0568 0.9 0.06 0.84
240 239 做多 2026-02-19 17:45:00 2026-02-19 22:50:00 1961.93 1939.99 2.26 50 0.0575 -1.26 0.06 -1.32
241 240 做空 2026-02-19 22:50:00 2026-02-20 00:05:00 1939.99 1915.04 2.24 50 0.0578 1.44 0.06 1.39
242 241 做多 2026-02-20 00:05:00 2026-02-20 01:15:00 1915.04 1941.03 2.26 50 0.0589 1.53 0.06 1.47
243 242 做空 2026-02-20 01:15:00 2026-02-20 03:25:00 1941.03 1935.82 2.27 50 0.0585 0.3 0.06 0.25
244 243 做多 2026-02-20 03:25:00 2026-02-20 04:45:00 1935.82 1947.16 2.27 50 0.0587 0.67 0.06 0.61
245 244 做空 2026-02-20 04:45:00 2026-02-20 05:55:00 1947.16 1947.67 2.28 50 0.0585 -0.03 0.06 -0.09
246 245 做多 2026-02-20 05:55:00 2026-02-20 07:00:00 1947.67 1943.15 2.28 50 0.0584 -0.26 0.06 -0.32
247 246 做空 2026-02-20 07:00:00 2026-02-20 12:00:00 1943.15 1948.02 2.27 50 0.0585 -0.28 0.06 -0.34
248 247 做多 2026-02-20 12:00:00 2026-02-20 13:30:00 1948.02 1960.28 2.28 50 0.0586 0.72 0.06 0.66
249 248 做空 2026-02-20 13:30:00 2026-02-20 14:30:00 1960.28 1954.43 2.29 50 0.0584 0.34 0.06 0.28
250 249 做多 2026-02-20 14:30:00 2026-02-20 15:15:00 1954.43 1959.51 2.29 50 0.0586 0.3 0.06 0.24
251 250 做空 2026-02-20 15:15:00 2026-02-20 18:40:00 1959.51 1961.18 2.29 50 0.0585 -0.1 0.06 -0.16
252 251 做多 2026-02-20 18:40:00 2026-02-20 20:50:00 1961.18 1946.59 2.29 50 0.0584 -0.85 0.06 -0.91
253 252 做空 2026-02-20 20:50:00 2026-02-20 21:00:00 1946.59 1943.99 2.28 50 0.0586 0.15 0.06 0.1
254 253 做多 2026-02-20 21:00:00 2026-02-20 22:30:00 1943.99 1944.3 2.28 50 0.0587 0.02 0.06 -0.04
255 254 做空 2026-02-20 22:30:00 2026-02-21 06:10:00 1944.3 1964.49 2.28 50 0.0586 -1.18 0.06 -1.24
256 255 做多 2026-02-21 06:10:00 2026-02-21 07:05:00 1964.49 1968.92 2.27 50 0.0577 0.26 0.06 0.2
257 256 做空 2026-02-21 07:05:00 2026-02-21 08:45:00 1968.92 1963.92 2.27 50 0.0576 0.29 0.06 0.23
258 257 做多 2026-02-21 08:45:00 2026-02-21 12:25:00 1963.92 1959.01 2.28 50 0.058 -0.29 0.06 -0.34
259 258 做空 2026-02-21 12:25:00 2026-02-21 20:25:00 1959.01 1969.99 2.28 50 0.0581 -0.64 0.06 -0.7
260 259 做多 2026-02-21 20:25:00 2026-02-21 22:05:00 1969.99 1981.03 2.27 50 0.0576 0.64 0.06 0.58
261 260 做空 2026-02-21 22:05:00 2026-02-21 23:30:00 1981.03 1988.19 2.27 50 0.0574 -0.41 0.06 -0.47
262 261 做多 2026-02-21 23:30:00 2026-02-21 23:40:00 1988.19 1989.55 2.27 50 0.057 0.08 0.06 0.02
263 262 做空 2026-02-21 23:40:00 2026-02-22 00:00:00 1989.55 1979.46 2.27 50 0.057 0.58 0.06 0.52
264 263 做多 2026-02-22 00:00:00 2026-02-22 01:40:00 1979.46 1988.74 2.27 50 0.0574 0.53 0.06 0.48
265 264 做空 2026-02-22 01:40:00 2026-02-22 03:50:00 1988.74 1987.85 2.28 50 0.0572 0.05 0.06 -0.01
266 265 做多 2026-02-22 03:50:00 2026-02-22 09:00:00 1987.85 1973.58 2.28 50 0.0573 -0.82 0.06 -0.87
267 266 做空 2026-02-22 09:00:00 2026-02-22 11:15:00 1973.58 1968.26 2.28 50 0.0577 0.31 0.06 0.25
268 267 做多 2026-02-22 11:15:00 2026-02-22 15:25:00 1968.26 1975.34 2.28 50 0.0579 0.41 0.06 0.35
269 268 做空 2026-02-22 15:25:00 2026-02-22 16:20:00 1975.34 1973.39 2.28 50 0.0577 0.11 0.06 0.06
270 269 做多 2026-02-22 16:20:00 2026-02-22 17:15:00 1973.39 1973.0 2.28 50 0.0578 -0.02 0.06 -0.08
271 270 做空 2026-02-22 17:15:00 2026-02-22 20:00:00 1973.0 1976.79 2.28 50 0.0578 -0.22 0.06 -0.28
272 271 做多 2026-02-22 20:00:00 2026-02-22 22:30:00 1976.79 1951.75 2.28 50 0.0576 -1.44 0.06 -1.5
273 272 做空 2026-02-22 22:30:00 2026-02-23 01:30:00 1951.75 1935.57 2.26 50 0.0579 0.94 0.06 0.88
274 273 做多 2026-02-23 01:30:00 2026-02-23 02:15:00 1935.57 1941.0 2.27 50 0.0586 0.32 0.06 0.26
275 274 做空 2026-02-23 02:15:00 2026-02-23 02:35:00 1941.0 1940.45 2.27 50 0.0585 0.03 0.06 -0.02
276 275 做多 2026-02-23 02:35:00 2026-02-23 02:50:00 1940.45 1942.01 2.27 50 0.0585 0.09 0.06 0.03
277 276 做空 2026-02-23 02:50:00 2026-02-23 04:50:00 1942.01 1939.47 2.27 50 0.0584 0.15 0.06 0.09
278 277 做多 2026-02-23 04:50:00 2026-02-23 05:35:00 1939.47 1949.72 2.27 50 0.0585 0.6 0.06 0.54
279 278 做空 2026-02-23 05:35:00 2026-02-23 07:20:00 1949.72 1951.6 2.28 50 0.0584 -0.11 0.06 -0.17
280 279 做多 2026-02-23 07:20:00 2026-02-23 07:55:00 1951.6 1957.01 2.27 50 0.0582 0.32 0.06 0.26
281 280 做空 2026-02-23 07:55:00 2026-02-23 08:25:00 1957.01 1947.41 2.28 50 0.0581 0.56 0.06 0.5
282 281 做多 2026-02-23 08:25:00 2026-02-23 11:05:00 1947.41 1866.1 2.29 50 0.0589 -4.79 0.05 -4.85
283 282 做空 2026-02-23 11:05:00 2026-02-23 11:15:00 1866.1 1858.39 2.25 50 0.0602 0.46 0.06 0.41
284 283 做多 2026-02-23 11:15:00 2026-02-23 13:15:00 1858.39 1866.46 2.25 50 0.0605 0.49 0.06 0.43
285 284 做空 2026-02-23 13:15:00 2026-02-23 16:35:00 1866.46 1877.82 2.25 50 0.0604 -0.69 0.06 -0.74
286 285 做多 2026-02-23 16:35:00 2026-02-23 17:10:00 1877.82 1884.54 2.25 50 0.0598 0.4 0.06 0.35
287 286 做空 2026-02-23 17:10:00 2026-02-23 20:30:00 1884.54 1907.37 2.25 50 0.0596 -1.36 0.06 -1.42
288 287 做多 2026-02-23 20:30:00 2026-02-23 20:35:00 1907.37 1919.57 2.23 50 0.0585 0.71 0.06 0.66
289 288 做空 2026-02-23 20:35:00 2026-02-23 22:35:00 1919.57 1906.62 2.24 50 0.0583 0.76 0.06 0.7
290 289 做多 2026-02-23 22:35:00 2026-02-24 10:30:00 1906.62 1849.89 2.25 50 0.0589 -3.34 0.05 -3.4
291 290 做空 2026-02-24 10:30:00 2026-02-24 11:00:00 1849.89 1836.02 2.22 50 0.06 0.83 0.06 0.78
292 291 做多 2026-02-24 11:00:00 2026-02-24 13:05:00 1836.02 1836.44 2.23 50 0.0607 0.03 0.06 -0.03
293 292 做空 2026-02-24 13:05:00 2026-02-24 13:20:00 1836.44 1815.44 2.23 50 0.0606 1.27 0.06 1.22
294 293 做多 2026-02-24 13:20:00 2026-02-24 16:30:00 1815.44 1832.52 2.24 50 0.0616 1.05 0.06 1.0
295 294 做空 2026-02-24 16:30:00 2026-02-24 17:05:00 1832.52 1824.3 2.25 50 0.0613 0.5 0.06 0.45
296 295 做多 2026-02-24 17:05:00 2026-02-24 20:10:00 1824.3 1826.47 2.25 50 0.0617 0.13 0.06 0.08
297 296 做空 2026-02-24 20:10:00 2026-02-24 21:20:00 1826.47 1819.72 2.25 50 0.0616 0.42 0.06 0.36
298 297 做多 2026-02-24 21:20:00 2026-02-24 22:35:00 1819.72 1821.38 2.25 50 0.062 0.1 0.06 0.05
299 298 做空 2026-02-24 22:35:00 2026-02-25 02:10:00 1821.38 1846.04 2.25 50 0.0619 -1.53 0.06 -1.58
300 299 做多 2026-02-25 02:10:00 2026-02-25 03:05:00 1846.04 1853.79 2.24 50 0.0606 0.47 0.06 0.41
301 300 做空 2026-02-25 03:05:00 2026-02-25 05:00:00 1853.79 1850.16 2.24 50 0.0605 0.22 0.06 0.16
302 301 做多 2026-02-25 05:00:00 2026-02-25 09:05:00 1850.16 1864.09 2.24 50 0.0606 0.84 0.06 0.79
303 302 做空 2026-02-25 09:05:00 2026-02-25 12:00:00 1864.09 1900.0 2.26 50 0.0607 -2.18 0.06 -2.24
304 303 做多 2026-02-25 12:00:00 2026-02-25 14:20:00 1900.0 1892.77 2.24 50 0.0589 -0.43 0.06 -0.48
305 304 做空 2026-02-25 14:20:00 2026-02-25 14:50:00 1892.77 1885.31 2.23 50 0.059 0.44 0.06 0.38
306 305 做多 2026-02-25 14:50:00 2026-02-25 16:20:00 1885.31 1896.51 2.24 50 0.0593 0.66 0.06 0.61
307 306 做空 2026-02-25 16:20:00 2026-02-25 19:05:00 1896.51 1914.28 2.24 50 0.0591 -1.05 0.06 -1.11
308 307 做多 2026-02-25 19:05:00 2026-02-25 19:10:00 1914.28 1914.26 2.23 50 0.0583 -0.0 0.06 -0.06
309 308 做空 2026-02-25 19:10:00 2026-02-26 03:10:00 1914.26 2065.7 2.23 50 0.0583 -8.82 0.06 -8.88
310 309 做多 2026-02-26 03:10:00 2026-02-26 04:50:00 2065.7 2077.16 2.14 50 0.0518 0.59 0.05 0.54
311 310 做空 2026-02-26 04:50:00 2026-02-26 06:40:00 2077.16 2088.92 2.15 50 0.0516 -0.61 0.05 -0.66
312 311 做多 2026-02-26 06:40:00 2026-02-26 10:45:00 2088.92 2048.37 2.14 50 0.0512 -2.08 0.05 -2.13
313 312 做空 2026-02-26 10:45:00 2026-02-26 13:15:00 2048.37 2059.76 2.13 50 0.0519 -0.59 0.05 -0.64
314 313 做多 2026-02-26 13:15:00 2026-02-26 14:05:00 2059.76 2070.3 2.12 50 0.0515 0.54 0.05 0.49
315 314 做空 2026-02-26 14:05:00 2026-02-26 14:45:00 2070.3 2061.26 2.12 50 0.0513 0.46 0.05 0.41

37
strategy/results/bb_5m_param_sweep_conservative_2020_2025.csv
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@@ -1,37 +0,0 @@
period,std,train_final,train_ret_pct,train_trades,train_liq,test_final,test_ret_pct,test_trades,test_liq,score
30,3.0,6.461292242669117e-12,-99.99999999999677,6251,2111,1.5236368612973373e-11,-99.99999999999238,5324,1148,1.5236368612973373e-11
20,3.0,1.8100835126072174e-13,-99.99999999999991,8303,2213,2.004484456301478e-13,-99.9999999999999,7119,1125,2.004484456301478e-13
30,2.8,2.4311483078671436e-12,-99.99999999999878,7043,2250,1.7667468163728375e-13,-99.99999999999991,6027,1207,1.7667468163728375e-13
20,2.8,7.846644590273088e-17,-100.0,9405,2411,1.373663083431439e-15,-100.0,8162,1200,1.373663083431439e-15
15,3.0,4.000972689149238e-14,-99.99999999999999,10176,2274,1.334121125924488e-15,-100.0,9020,1127,1.334121125924488e-15
12,3.0,1.3648909498077833e-16,-100.0,12248,2312,3.221031658827695e-16,-100.0,10961,1111,3.221031658827695e-16
30,2.5,3.5612711817081846e-19,-100.0,8364,2548,2.6039428085637916e-16,-100.0,7169,1309,2.6039428085637916e-16
10,3.0,1.0851271143245777e-19,-100.0,14554,2331,1.5973234572926277e-17,-100.0,12954,1056,1.5973234572926277e-17
15,2.8,1.0867209668553655e-17,-100.0,11611,2443,2.8298588113655214e-18,-100.0,10334,1169,2.8298588113655214e-18
8,3.0,5.306932488644652e-21,-100.0,17958,2357,1.2736252312876604e-19,-100.0,16166,1010,1.2736252312876604e-19
12,2.8,1.4286513547325967e-19,-100.0,13966,2448,1.0033977462584242e-19,-100.0,12480,1164,1.0033977462584242e-19
20,2.5,2.2403774532126082e-23,-100.0,11128,2662,7.765130502770661e-20,-100.0,9730,1294,7.765130502770661e-20
30,2.2,7.112452348296382e-24,-100.0,9746,2837,2.178522685356585e-20,-100.0,8376,1404,2.178522685356585e-20
10,2.8,1.14378568397889e-22,-100.0,16467,2419,1.7504767423183517e-20,-100.0,14860,1099,1.7504767423183517e-20
15,2.5,1.2889707168075324e-24,-100.0,13904,2665,7.701898585592486e-22,-100.0,12376,1244,7.701898585592486e-22
30,2.0,3.3954467452863534e-30,-100.0,10737,3038,3.006982280285153e-23,-100.0,9277,1476,3.006982280285153e-23
8,2.8,2.3911268173585217e-25,-100.0,20200,2424,2.6239693580748698e-23,-100.0,18347,1047,2.6239693580748698e-23
12,2.5,1.5959390099076778e-26,-100.0,16840,2666,2.5122374088172532e-24,-100.0,15097,1197,2.5122374088172532e-24
20,2.2,4.075155061653048e-30,-100.0,13056,2898,1.6217540014835198e-24,-100.0,11509,1387,1.6217540014835198e-24
10,2.5,1.5239252240418153e-27,-100.0,19776,2602,3.478054992553642e-26,-100.0,17847,1143,3.478054992553642e-26
30,1.8,2.905397760666862e-35,-100.0,11783,3196,1.4643221654306065e-26,-100.0,10238,1510,1.4643221654306065e-26
20,2.0,4.5258883799074187e-35,-100.0,14558,3084,6.0778437333199835e-28,-100.0,12766,1425,6.0778437333199835e-28
15,2.2,1.0473150457667376e-34,-100.0,16471,2949,4.616747557539696e-28,-100.0,14639,1329,4.616747557539696e-28
8,2.5,3.728422330048161e-32,-100.0,24241,2572,8.443947733942594e-30,-100.0,22035,1088,8.443947733942594e-30
12,2.2,4.023491430830127e-34,-100.0,19939,2849,2.0103723058079613e-31,-100.0,17925,1278,2.0103723058079613e-31
15,2.0,1.5077333275255206e-39,-100.0,18290,3047,4.5852539168192905e-32,-100.0,16384,1379,4.5852539168192905e-32
20,1.8,3.1358668307895184e-40,-100.0,16081,3234,3.528256253001935e-32,-100.0,14238,1477,3.528256253001935e-32
10,2.2,9.818937063199508e-37,-100.0,23289,2808,8.301596164191992e-34,-100.0,21116,1192,8.301596164191992e-34
12,2.0,2.89871510624932e-41,-100.0,22108,2997,5.7249833676044525e-36,-100.0,19972,1312,5.7249833676044525e-36
15,1.8,1.9646377910410098e-43,-100.0,20241,3165,1.887622828901651e-36,-100.0,18203,1403,1.887622828901651e-36
8,2.2,3.137207043844276e-39,-100.0,28429,2715,3.907836948794964e-37,-100.0,26080,1123,3.907836948794964e-37
10,2.0,3.772919321721765e-43,-100.0,25796,2936,3.0387903785706536e-39,-100.0,23573,1242,3.0387903785706536e-39
12,1.8,2.2809761615524126e-47,-100.0,24375,3136,2.3011372195381717e-40,-100.0,22137,1322,2.3011372195381717e-40
8,2.0,1.0509234742751481e-45,-100.0,31437,2841,8.788965059420589e-44,-100.0,28899,1160,8.788965059420589e-44
10,1.8,5.805887390677206e-50,-100.0,28616,3081,2.7972542140757193e-44,-100.0,26107,1254,2.7972542140757193e-44
8,1.8,2.081454986276226e-52,-100.0,34477,2948,9.722792700824057e-50,-100.0,31739,1170,9.722792700824057e-50
1 period std train_final train_ret_pct train_trades train_liq test_final test_ret_pct test_trades test_liq score
2 30 3.0 6.461292242669117e-12 -99.99999999999677 6251 2111 1.5236368612973373e-11 -99.99999999999238 5324 1148 1.5236368612973373e-11
3 20 3.0 1.8100835126072174e-13 -99.99999999999991 8303 2213 2.004484456301478e-13 -99.9999999999999 7119 1125 2.004484456301478e-13
4 30 2.8 2.4311483078671436e-12 -99.99999999999878 7043 2250 1.7667468163728375e-13 -99.99999999999991 6027 1207 1.7667468163728375e-13
5 20 2.8 7.846644590273088e-17 -100.0 9405 2411 1.373663083431439e-15 -100.0 8162 1200 1.373663083431439e-15
6 15 3.0 4.000972689149238e-14 -99.99999999999999 10176 2274 1.334121125924488e-15 -100.0 9020 1127 1.334121125924488e-15
7 12 3.0 1.3648909498077833e-16 -100.0 12248 2312 3.221031658827695e-16 -100.0 10961 1111 3.221031658827695e-16
8 30 2.5 3.5612711817081846e-19 -100.0 8364 2548 2.6039428085637916e-16 -100.0 7169 1309 2.6039428085637916e-16
9 10 3.0 1.0851271143245777e-19 -100.0 14554 2331 1.5973234572926277e-17 -100.0 12954 1056 1.5973234572926277e-17
10 15 2.8 1.0867209668553655e-17 -100.0 11611 2443 2.8298588113655214e-18 -100.0 10334 1169 2.8298588113655214e-18
11 8 3.0 5.306932488644652e-21 -100.0 17958 2357 1.2736252312876604e-19 -100.0 16166 1010 1.2736252312876604e-19
12 12 2.8 1.4286513547325967e-19 -100.0 13966 2448 1.0033977462584242e-19 -100.0 12480 1164 1.0033977462584242e-19
13 20 2.5 2.2403774532126082e-23 -100.0 11128 2662 7.765130502770661e-20 -100.0 9730 1294 7.765130502770661e-20
14 30 2.2 7.112452348296382e-24 -100.0 9746 2837 2.178522685356585e-20 -100.0 8376 1404 2.178522685356585e-20
15 10 2.8 1.14378568397889e-22 -100.0 16467 2419 1.7504767423183517e-20 -100.0 14860 1099 1.7504767423183517e-20
16 15 2.5 1.2889707168075324e-24 -100.0 13904 2665 7.701898585592486e-22 -100.0 12376 1244 7.701898585592486e-22
17 30 2.0 3.3954467452863534e-30 -100.0 10737 3038 3.006982280285153e-23 -100.0 9277 1476 3.006982280285153e-23
18 8 2.8 2.3911268173585217e-25 -100.0 20200 2424 2.6239693580748698e-23 -100.0 18347 1047 2.6239693580748698e-23
19 12 2.5 1.5959390099076778e-26 -100.0 16840 2666 2.5122374088172532e-24 -100.0 15097 1197 2.5122374088172532e-24
20 20 2.2 4.075155061653048e-30 -100.0 13056 2898 1.6217540014835198e-24 -100.0 11509 1387 1.6217540014835198e-24
21 10 2.5 1.5239252240418153e-27 -100.0 19776 2602 3.478054992553642e-26 -100.0 17847 1143 3.478054992553642e-26
22 30 1.8 2.905397760666862e-35 -100.0 11783 3196 1.4643221654306065e-26 -100.0 10238 1510 1.4643221654306065e-26
23 20 2.0 4.5258883799074187e-35 -100.0 14558 3084 6.0778437333199835e-28 -100.0 12766 1425 6.0778437333199835e-28
24 15 2.2 1.0473150457667376e-34 -100.0 16471 2949 4.616747557539696e-28 -100.0 14639 1329 4.616747557539696e-28
25 8 2.5 3.728422330048161e-32 -100.0 24241 2572 8.443947733942594e-30 -100.0 22035 1088 8.443947733942594e-30
26 12 2.2 4.023491430830127e-34 -100.0 19939 2849 2.0103723058079613e-31 -100.0 17925 1278 2.0103723058079613e-31
27 15 2.0 1.5077333275255206e-39 -100.0 18290 3047 4.5852539168192905e-32 -100.0 16384 1379 4.5852539168192905e-32
28 20 1.8 3.1358668307895184e-40 -100.0 16081 3234 3.528256253001935e-32 -100.0 14238 1477 3.528256253001935e-32
29 10 2.2 9.818937063199508e-37 -100.0 23289 2808 8.301596164191992e-34 -100.0 21116 1192 8.301596164191992e-34
30 12 2.0 2.89871510624932e-41 -100.0 22108 2997 5.7249833676044525e-36 -100.0 19972 1312 5.7249833676044525e-36
31 15 1.8 1.9646377910410098e-43 -100.0 20241 3165 1.887622828901651e-36 -100.0 18203 1403 1.887622828901651e-36
32 8 2.2 3.137207043844276e-39 -100.0 28429 2715 3.907836948794964e-37 -100.0 26080 1123 3.907836948794964e-37
33 10 2.0 3.772919321721765e-43 -100.0 25796 2936 3.0387903785706536e-39 -100.0 23573 1242 3.0387903785706536e-39
34 12 1.8 2.2809761615524126e-47 -100.0 24375 3136 2.3011372195381717e-40 -100.0 22137 1322 2.3011372195381717e-40
35 8 2.0 1.0509234742751481e-45 -100.0 31437 2841 8.788965059420589e-44 -100.0 28899 1160 8.788965059420589e-44
36 10 1.8 5.805887390677206e-50 -100.0 28616 3081 2.7972542140757193e-44 -100.0 26107 1254 2.7972542140757193e-44
37 8 1.8 2.081454986276226e-52 -100.0 34477 2948 9.722792700824057e-50 -100.0 31739 1170 9.722792700824057e-50

37
strategy/results/bb_5m_param_sweep_conservative_2020_2025_no_pyramid.csv
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@@ -1,37 +0,0 @@
period,std,train_final,train_ret_pct,train_trades,train_liq,test_final,test_ret_pct,test_trades,test_liq
30,3.0,10.33503463017524,-94.83248268491238,6729,2627,9.105543803335324,-95.44722809833233,5622,1452
30,2.8,5.776936552726516,-97.11153172363674,7595,2844,5.8310318500270775,-97.08448407498646,6321,1513
20,3.0,2.8530376604538237,-98.57348116977309,8932,2894,3.117142538047551,-98.44142873097623,7487,1533
30,2.5,3.3090997870703003,-98.34545010646485,8894,3101,2.9901743436510984,-98.50491282817445,7453,1604
20,2.8,0.859787066661548,-99.57010646666923,10085,3122,1.9227920948602537,-99.03860395256987,8513,1595
30,2.2,0.6281222070732229,-99.68593889646338,10267,3369,1.236067041291377,-99.38196647935432,8634,1668
20,2.5,0.25278361222515633,-99.87360819388742,11847,3394,1.049877999265477,-99.47506100036726,10073,1644
15,3.0,1.466920389212773,-99.26653980539362,10948,3115,0.9504725191291745,-99.52476374043542,9444,1587
15,2.8,0.40768152859129914,-99.79615923570435,12417,3310,0.7301936713745412,-99.63490316431273,10761,1640
30,2.0,0.27389542637526637,-99.86305228681236,11185,3489,0.5860341454411505,-99.70698292727943,9512,1716
12,3.0,0.4596628661296959,-99.77016856693515,13093,3243,0.4599583255360801,-99.77002083723195,11385,1580
15,2.5,0.07101870013743423,-99.96449064993128,14756,3554,0.27989601683829196,-99.86005199158086,12771,1662
20,2.2,0.068694671965183,-99.96565266401741,13743,3599,0.2447384990957471,-99.87763075045213,11824,1712
30,1.8,0.07356202139088691,-99.96321898930455,12192,3609,0.23106787386920916,-99.88446606306539,10474,1746
12,2.8,0.20911756492338415,-99.8954412175383,14849,3399,0.2173258061277338,-99.89133709693613,12877,1605
10,3.0,0.1284276762723427,-99.93578616186383,15455,3345,0.1658833559115693,-99.91705832204421,13421,1582
20,2.0,0.027822496858016875,-99.98608875157099,15185,3723,0.10850768549118871,-99.9457461572544,13061,1728
10,2.8,0.062411539565720396,-99.96879423021714,17473,3515,0.0858355994398584,-99.95708220028007,15299,1600
12,2.5,0.0635800161079057,-99.96820999194605,17762,3624,0.07595434907945135,-99.96202282546028,15517,1641
15,2.2,0.011211866028193341,-99.9943940669859,17256,3759,0.07346375074383373,-99.96326812462809,14996,1687
20,1.8,0.010177972068216252,-99.99491101396589,16657,3813,0.041379392242463335,-99.97931030387876,14523,1765
8,3.0,0.037529843697168386,-99.98123507815141,18948,3467,0.030619960044026544,-99.98469001997799,16661,1580
15,2.0,0.004790098785046004,-99.99760495060748,19085,3851,0.0250058198917987,-99.9874970900541,16699,1703
10,2.5,0.020080566762398937,-99.9899597166188,20810,3675,0.02154161161844521,-99.98922919419078,18304,1622
12,2.2,0.006198215965126392,-99.99690089201744,20863,3802,0.01569234833017946,-99.9921538258349,18304,1675
8,2.8,0.00916331137620061,-99.9954183443119,21278,3593,0.013446224481937236,-99.99327688775904,18831,1590
15,1.8,0.00195636598029706,-99.99902181700985,20986,3912,0.007973883235858103,-99.99601305838208,18506,1711
12,2.0,0.0012611903383017201,-99.99936940483084,22983,3884,0.0049442565518793904,-99.99752787172406,20318,1667
10,2.2,0.0015631498325544685,-99.99921842508373,24294,3843,0.004016982771284942,-99.99799150861436,21533,1619
8,2.5,0.0015805889869186715,-99.99920970550654,25383,3778,0.00222758185497439,-99.99888620907251,22550,1619
12,1.8,0.0003285760597477184,-99.99983571197012,25197,3969,0.0014253657508011418,-99.9992873171246,22474,1670
10,2.0,0.00030181924086439277,-99.99984909037957,26744,3905,0.0007558609511890611,-99.9996220695244,23966,1645
8,2.2,0.00022388168465948394,-99.99988805915767,29572,3888,0.000283647480462369,-99.99985817625976,26546,1608
10,1.8,0.00010484354042229784,-99.9999475782298,29476,3949,0.00015541841539480644,-99.9999222907923,26481,1640
8,2.0,3.0427493228289115e-05,-99.99998478625339,32549,3962,3.787843338427551e-05,-99.99998106078331,29334,1613
8,1.8,5.318861339787151e-06,-99.99999734056934,35502,3981,6.1348022466747185e-06,-99.99999693259888,32157,1599
1 period std train_final train_ret_pct train_trades train_liq test_final test_ret_pct test_trades test_liq
2 30 3.0 10.33503463017524 -94.83248268491238 6729 2627 9.105543803335324 -95.44722809833233 5622 1452
3 30 2.8 5.776936552726516 -97.11153172363674 7595 2844 5.8310318500270775 -97.08448407498646 6321 1513
4 20 3.0 2.8530376604538237 -98.57348116977309 8932 2894 3.117142538047551 -98.44142873097623 7487 1533
5 30 2.5 3.3090997870703003 -98.34545010646485 8894 3101 2.9901743436510984 -98.50491282817445 7453 1604
6 20 2.8 0.859787066661548 -99.57010646666923 10085 3122 1.9227920948602537 -99.03860395256987 8513 1595
7 30 2.2 0.6281222070732229 -99.68593889646338 10267 3369 1.236067041291377 -99.38196647935432 8634 1668
8 20 2.5 0.25278361222515633 -99.87360819388742 11847 3394 1.049877999265477 -99.47506100036726 10073 1644
9 15 3.0 1.466920389212773 -99.26653980539362 10948 3115 0.9504725191291745 -99.52476374043542 9444 1587
10 15 2.8 0.40768152859129914 -99.79615923570435 12417 3310 0.7301936713745412 -99.63490316431273 10761 1640
11 30 2.0 0.27389542637526637 -99.86305228681236 11185 3489 0.5860341454411505 -99.70698292727943 9512 1716
12 12 3.0 0.4596628661296959 -99.77016856693515 13093 3243 0.4599583255360801 -99.77002083723195 11385 1580
13 15 2.5 0.07101870013743423 -99.96449064993128 14756 3554 0.27989601683829196 -99.86005199158086 12771 1662
14 20 2.2 0.068694671965183 -99.96565266401741 13743 3599 0.2447384990957471 -99.87763075045213 11824 1712
15 30 1.8 0.07356202139088691 -99.96321898930455 12192 3609 0.23106787386920916 -99.88446606306539 10474 1746
16 12 2.8 0.20911756492338415 -99.8954412175383 14849 3399 0.2173258061277338 -99.89133709693613 12877 1605
17 10 3.0 0.1284276762723427 -99.93578616186383 15455 3345 0.1658833559115693 -99.91705832204421 13421 1582
18 20 2.0 0.027822496858016875 -99.98608875157099 15185 3723 0.10850768549118871 -99.9457461572544 13061 1728
19 10 2.8 0.062411539565720396 -99.96879423021714 17473 3515 0.0858355994398584 -99.95708220028007 15299 1600
20 12 2.5 0.0635800161079057 -99.96820999194605 17762 3624 0.07595434907945135 -99.96202282546028 15517 1641
21 15 2.2 0.011211866028193341 -99.9943940669859 17256 3759 0.07346375074383373 -99.96326812462809 14996 1687
22 20 1.8 0.010177972068216252 -99.99491101396589 16657 3813 0.041379392242463335 -99.97931030387876 14523 1765
23 8 3.0 0.037529843697168386 -99.98123507815141 18948 3467 0.030619960044026544 -99.98469001997799 16661 1580
24 15 2.0 0.004790098785046004 -99.99760495060748 19085 3851 0.0250058198917987 -99.9874970900541 16699 1703
25 10 2.5 0.020080566762398937 -99.9899597166188 20810 3675 0.02154161161844521 -99.98922919419078 18304 1622
26 12 2.2 0.006198215965126392 -99.99690089201744 20863 3802 0.01569234833017946 -99.9921538258349 18304 1675
27 8 2.8 0.00916331137620061 -99.9954183443119 21278 3593 0.013446224481937236 -99.99327688775904 18831 1590
28 15 1.8 0.00195636598029706 -99.99902181700985 20986 3912 0.007973883235858103 -99.99601305838208 18506 1711
29 12 2.0 0.0012611903383017201 -99.99936940483084 22983 3884 0.0049442565518793904 -99.99752787172406 20318 1667
30 10 2.2 0.0015631498325544685 -99.99921842508373 24294 3843 0.004016982771284942 -99.99799150861436 21533 1619
31 8 2.5 0.0015805889869186715 -99.99920970550654 25383 3778 0.00222758185497439 -99.99888620907251 22550 1619
32 12 1.8 0.0003285760597477184 -99.99983571197012 25197 3969 0.0014253657508011418 -99.9992873171246 22474 1670
33 10 2.0 0.00030181924086439277 -99.99984909037957 26744 3905 0.0007558609511890611 -99.9996220695244 23966 1645
34 8 2.2 0.00022388168465948394 -99.99988805915767 29572 3888 0.000283647480462369 -99.99985817625976 26546 1608
35 10 1.8 0.00010484354042229784 -99.9999475782298 29476 3949 0.00015541841539480644 -99.9999222907923 26481 1640
36 8 2.0 3.0427493228289115e-05 -99.99998478625339 32549 3962 3.787843338427551e-05 -99.99998106078331 29334 1613
37 8 1.8 5.318861339787151e-06 -99.99999734056934 35502 3981 6.1348022466747185e-06 -99.99999693259888 32157 1599

49
strategy/results/bb_5m_practical_upgrade_bandwidth_tune.csv
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@@ -1,49 +0,0 @@
bb_period,bb_std,cooldown,min_bw,final,ret,trades,liq,win,dd,sharpe,fee,rebate
30,3.2,6,0.01,202.98777028457513,1.4938851422875672,2208,0,64.4927536231884,-2.2659000933991535,0.5747142550615898,4.492191078113778,4.042051704177064
30,3.2,6,0.008,202.88007457111019,1.4400372855550927,2497,0,63.99679615538646,-2.3593132155964156,0.5474772340529435,5.077568757707738,4.568895760736814
30,3.2,12,0.01,202.58279532236259,1.2913976611812927,2176,0,63.60294117647059,-2.590578505705281,0.49034397612249453,4.425821502882915,3.9823209224653042
30,3.2,12,0.008,202.43724464440305,1.2186223222015258,2453,0,63.06563391765185,-2.6964621320131528,0.45661035313826664,4.984364752542757,4.4850138457153115
30,3.2,6,0.005,202.2604553875168,1.1302276937583997,2848,0,63.55337078651685,-3.5097750900355322,0.4233881729734044,5.800483411417114,5.2195225693696905
30,3.2,6,0.0,201.9035397473293,0.9517698736646452,2983,0,62.45390546429769,-3.6901419995414244,0.3554736578132862,6.071731305514356,5.463647284287156
30,3.2,12,0.005,201.87948930618595,0.9397446530929727,2788,0,62.51793400286944,-3.7314549188814397,0.3459557408534809,5.673598716356395,5.1053271761221275
30,3.2,24,0.008,201.81718023856013,0.9085901192800634,2321,0,60.83584661783714,-3.1850135601426643,0.3332848294762347,4.711163723354367,4.239135720345717
40,3.2,24,0.008,201.7362421481199,0.8681210740599568,1762,0,61.80476730987514,-1.9330254703131686,0.33009880295284993,3.5617733962125127,3.2028717014335406
30,3.2,24,0.01,201.69506662016363,0.8475333100818148,2071,0,61.41960405601159,-3.0289149936820934,0.31756756837299815,4.202969916048575,3.7817585189246183
30,3.2,12,0.0,201.60362661159775,0.8018133057988733,2922,0,61.327857631759066,-3.88911319759859,0.2941026586132461,5.943783104874334,5.348494371558028
30,3.0,6,0.01,201.5881617500534,0.7940808750266939,2543,0,63.429020841525755,-2.368575342977323,0.2998262616505584,5.151884495340698,4.633970942969105
40,3.2,24,0.01,201.53527397642122,0.7676369882106115,1605,0,62.55451713395639,-1.9504615008019073,0.2907552932707512,3.2424316070919357,2.91546508291806
30,3.0,6,0.008,201.359992187183,0.6799960935914982,2918,0,63.19396847155586,-2.027912276846223,0.2610014112678299,5.899172349883858,5.306536721796989
30,3.0,12,0.01,201.3241380805589,0.662069040279448,2506,0,62.729449321628096,-2.8976334001800694,0.24838320050757742,5.080944389695803,4.570128417005312
30,3.0,12,0.008,201.31165446949683,0.6558272347484149,2866,0,62.35170969993021,-2.494460658608773,0.24878271250544268,5.80043717869557,5.217675411997363
30,3.0,24,0.01,201.26991592259085,0.6349579612954273,2368,0,61.02195945945946,-3.348407035056823,0.23124334375082511,4.807697212750294,4.324206690738638
30,3.0,24,0.008,201.25444575369687,0.6272228768484354,2688,0,60.56547619047619,-3.1621600662850256,0.22981479221382087,5.449274512519768,4.901629784853851
30,3.2,24,0.005,201.17465506334136,0.587327531670681,2627,0,60.22078416444614,-4.2867277356414775,0.21102822502134555,5.338402308340532,4.803653591872367
40,3.2,6,0.008,201.0778775233752,0.5389387616876036,1853,0,63.680518078791145,-1.545875090009332,0.21018483710385255,3.730990845759943,3.3551758773915945
40,3.2,24,0.005,201.05984222600543,0.5299211130027146,1924,0,61.018711018711016,-2.175922708147283,0.19495371215945767,3.885764208269912,3.494473381060668
40,3.2,24,0.0,200.85259022124131,0.4262951106206571,1977,0,60.59686393525544,-2.2131317121589404,0.15572489459076294,3.9910079187965377,3.589195518538243
40,3.2,6,0.01,200.74117229778386,0.3705861488919311,1680,0,63.988095238095234,-1.788489097899884,0.14388428114556331,3.3801026625472996,3.039379763592075
40,3.2,6,0.005,200.6975214875542,0.3487607437771061,2038,0,63.297350343473994,-1.8348344987913379,0.1307282485158805,4.1020755008586685,3.689158017516957
30,3.2,24,0.0,200.6742618681597,0.33713093407985184,2748,0,58.806404657933044,-4.526694095829043,0.12031876886338758,5.578810922296089,5.020023604160509
40,3.2,12,0.008,200.65656048664087,0.32828024332043526,1824,0,62.88377192982456,-1.7544366504391178,0.12833478287726025,3.6720632612140545,3.3021471605391497
30,3.0,6,0.005,200.51551216591736,0.2577560829586787,3377,0,62.51110453064851,-2.9778328420115088,0.09497684815116508,6.823545946251757,6.138485174444638
40,3.2,6,0.0,200.44496962075274,0.2224848103763719,2097,0,62.994754411063425,-2.042148146654995,0.08158240969215196,4.218333320816779,3.79379346557969
40,3.0,24,0.008,200.41889346236763,0.2094467311838173,2057,0,61.93485658726301,-2.473425069100074,0.07868630999290598,4.146171354749355,3.72884910922223
30,3.0,12,0.005,200.35022974683667,0.17511487341833742,3300,0,61.36363636363637,-3.584345639913721,0.06306984952787358,6.672617704418062,6.002651107710117
40,3.2,12,0.01,200.24642098781575,0.12321049390787665,1659,0,63.29113924050633,-2.2088172045713748,0.047913641900456855,3.3367725501172822,3.000389348021997
40,3.2,12,0.005,200.22091496566105,0.11045748283052605,2002,0,62.38761238761239,-2.0783640787319086,0.03817994892546192,4.027940397396305,3.6224432772034887
40,3.0,24,0.01,200.12568455038564,0.06284227519282126,1879,0,62.16072378924961,-2.6329610918170374,0.023564976494820568,3.787542396891309,3.4060828242510723
40,3.0,6,0.008,200.0678037808892,0.03390189044459646,2194,0,63.947128532360985,-2.2038606611252476,0.01294800862092224,4.410398919734593,3.966658239689972
30,3.0,6,0.0,200.00414940285927,0.002074701429634729,3569,0,61.58587839731017,-3.4138707553280483,-0.0006647490253754809,7.204083538326778,6.4809759087145915
40,3.2,12,0.0,199.9609691102816,-0.019515444859194986,2060,0,61.99029126213592,-2.293284464503728,-0.012281258667209633,4.142089182262913,3.725180692979831
30,3.0,12,0.0,199.86477350989745,-0.06761324505127675,3488,0,60.321100917431195,-3.9479783254082292,-0.026217988255613115,7.044972103443261,6.337776620729888
30,3.0,24,0.005,199.8322173667805,-0.08389131660975124,3078,0,59.25925925925925,-4.517336088935309,-0.031095706303734948,6.227919385867038,5.602429614435114
40,3.0,24,0.005,199.5939851399495,-0.20300743002525223,2287,0,60.690861390467866,-3.1641220863467083,-0.07962941557219835,4.605201690819581,4.141987512861499
40,3.0,12,0.008,199.5739264063975,-0.21303679680124785,2148,0,63.17504655493482,-2.615052284973501,-0.08139777980329083,4.316235063292179,3.8819178516682813
40,3.0,6,0.01,199.4783021660671,-0.2608489169664523,1988,0,63.581488933601605,-2.6165649578562693,-0.09950203972642017,3.991524078241081,3.5896750888907443
40,3.0,24,0.0,199.34357556785355,-0.3282122160732257,2360,0,60.21186440677966,-3.3377613611040715,-0.1256995824626251,4.74929125388248,4.271670892552751
30,3.0,24,0.0,199.30001953765344,-0.34999023117327965,3243,0,58.125192722787546,-4.896971344439777,-0.12506755033132605,6.553042403347132,5.89504751509868
40,3.0,12,0.01,199.12086527405495,-0.4395673629725252,1956,0,62.985685071574636,-2.934702029502688,-0.16766517034426934,3.9282781540179963,3.532758588982499
40,3.0,6,0.005,199.01319225044602,-0.49340387477698755,2458,0,62.9780309194467,-3.206770049240731,-0.19188065769564314,4.932566481122759,4.436623248761567
40,3.0,6,0.0,198.65444873410513,-0.6727756329474346,2539,0,62.46553761323356,-3.483785802636902,-0.25943426479073867,5.091061673108702,4.579273159284268
40,3.0,12,0.005,198.53430497909721,-0.7328475104513927,2400,0,62.041666666666664,-3.6063962830445178,-0.2822792363382157,4.814513695479092,4.33038262002996
40,3.0,12,0.0,198.16460271518014,-0.9176986424099312,2480,0,61.41129032258065,-3.8946055152910333,-0.35154409034010436,4.970878228218944,4.47111508616677
1 bb_period bb_std cooldown min_bw final ret trades liq win dd sharpe fee rebate
2 30 3.2 6 0.01 202.98777028457513 1.4938851422875672 2208 0 64.4927536231884 -2.2659000933991535 0.5747142550615898 4.492191078113778 4.042051704177064
3 30 3.2 6 0.008 202.88007457111019 1.4400372855550927 2497 0 63.99679615538646 -2.3593132155964156 0.5474772340529435 5.077568757707738 4.568895760736814
4 30 3.2 12 0.01 202.58279532236259 1.2913976611812927 2176 0 63.60294117647059 -2.590578505705281 0.49034397612249453 4.425821502882915 3.9823209224653042
5 30 3.2 12 0.008 202.43724464440305 1.2186223222015258 2453 0 63.06563391765185 -2.6964621320131528 0.45661035313826664 4.984364752542757 4.4850138457153115
6 30 3.2 6 0.005 202.2604553875168 1.1302276937583997 2848 0 63.55337078651685 -3.5097750900355322 0.4233881729734044 5.800483411417114 5.2195225693696905
7 30 3.2 6 0.0 201.9035397473293 0.9517698736646452 2983 0 62.45390546429769 -3.6901419995414244 0.3554736578132862 6.071731305514356 5.463647284287156
8 30 3.2 12 0.005 201.87948930618595 0.9397446530929727 2788 0 62.51793400286944 -3.7314549188814397 0.3459557408534809 5.673598716356395 5.1053271761221275
9 30 3.2 24 0.008 201.81718023856013 0.9085901192800634 2321 0 60.83584661783714 -3.1850135601426643 0.3332848294762347 4.711163723354367 4.239135720345717
10 40 3.2 24 0.008 201.7362421481199 0.8681210740599568 1762 0 61.80476730987514 -1.9330254703131686 0.33009880295284993 3.5617733962125127 3.2028717014335406
11 30 3.2 24 0.01 201.69506662016363 0.8475333100818148 2071 0 61.41960405601159 -3.0289149936820934 0.31756756837299815 4.202969916048575 3.7817585189246183
12 30 3.2 12 0.0 201.60362661159775 0.8018133057988733 2922 0 61.327857631759066 -3.88911319759859 0.2941026586132461 5.943783104874334 5.348494371558028
13 30 3.0 6 0.01 201.5881617500534 0.7940808750266939 2543 0 63.429020841525755 -2.368575342977323 0.2998262616505584 5.151884495340698 4.633970942969105
14 40 3.2 24 0.01 201.53527397642122 0.7676369882106115 1605 0 62.55451713395639 -1.9504615008019073 0.2907552932707512 3.2424316070919357 2.91546508291806
15 30 3.0 6 0.008 201.359992187183 0.6799960935914982 2918 0 63.19396847155586 -2.027912276846223 0.2610014112678299 5.899172349883858 5.306536721796989
16 30 3.0 12 0.01 201.3241380805589 0.662069040279448 2506 0 62.729449321628096 -2.8976334001800694 0.24838320050757742 5.080944389695803 4.570128417005312
17 30 3.0 12 0.008 201.31165446949683 0.6558272347484149 2866 0 62.35170969993021 -2.494460658608773 0.24878271250544268 5.80043717869557 5.217675411997363
18 30 3.0 24 0.01 201.26991592259085 0.6349579612954273 2368 0 61.02195945945946 -3.348407035056823 0.23124334375082511 4.807697212750294 4.324206690738638
19 30 3.0 24 0.008 201.25444575369687 0.6272228768484354 2688 0 60.56547619047619 -3.1621600662850256 0.22981479221382087 5.449274512519768 4.901629784853851
20 30 3.2 24 0.005 201.17465506334136 0.587327531670681 2627 0 60.22078416444614 -4.2867277356414775 0.21102822502134555 5.338402308340532 4.803653591872367
21 40 3.2 6 0.008 201.0778775233752 0.5389387616876036 1853 0 63.680518078791145 -1.545875090009332 0.21018483710385255 3.730990845759943 3.3551758773915945
22 40 3.2 24 0.005 201.05984222600543 0.5299211130027146 1924 0 61.018711018711016 -2.175922708147283 0.19495371215945767 3.885764208269912 3.494473381060668
23 40 3.2 24 0.0 200.85259022124131 0.4262951106206571 1977 0 60.59686393525544 -2.2131317121589404 0.15572489459076294 3.9910079187965377 3.589195518538243
24 40 3.2 6 0.01 200.74117229778386 0.3705861488919311 1680 0 63.988095238095234 -1.788489097899884 0.14388428114556331 3.3801026625472996 3.039379763592075
25 40 3.2 6 0.005 200.6975214875542 0.3487607437771061 2038 0 63.297350343473994 -1.8348344987913379 0.1307282485158805 4.1020755008586685 3.689158017516957
26 30 3.2 24 0.0 200.6742618681597 0.33713093407985184 2748 0 58.806404657933044 -4.526694095829043 0.12031876886338758 5.578810922296089 5.020023604160509
27 40 3.2 12 0.008 200.65656048664087 0.32828024332043526 1824 0 62.88377192982456 -1.7544366504391178 0.12833478287726025 3.6720632612140545 3.3021471605391497
28 30 3.0 6 0.005 200.51551216591736 0.2577560829586787 3377 0 62.51110453064851 -2.9778328420115088 0.09497684815116508 6.823545946251757 6.138485174444638
29 40 3.2 6 0.0 200.44496962075274 0.2224848103763719 2097 0 62.994754411063425 -2.042148146654995 0.08158240969215196 4.218333320816779 3.79379346557969
30 40 3.0 24 0.008 200.41889346236763 0.2094467311838173 2057 0 61.93485658726301 -2.473425069100074 0.07868630999290598 4.146171354749355 3.72884910922223
31 30 3.0 12 0.005 200.35022974683667 0.17511487341833742 3300 0 61.36363636363637 -3.584345639913721 0.06306984952787358 6.672617704418062 6.002651107710117
32 40 3.2 12 0.01 200.24642098781575 0.12321049390787665 1659 0 63.29113924050633 -2.2088172045713748 0.047913641900456855 3.3367725501172822 3.000389348021997
33 40 3.2 12 0.005 200.22091496566105 0.11045748283052605 2002 0 62.38761238761239 -2.0783640787319086 0.03817994892546192 4.027940397396305 3.6224432772034887
34 40 3.0 24 0.01 200.12568455038564 0.06284227519282126 1879 0 62.16072378924961 -2.6329610918170374 0.023564976494820568 3.787542396891309 3.4060828242510723
35 40 3.0 6 0.008 200.0678037808892 0.03390189044459646 2194 0 63.947128532360985 -2.2038606611252476 0.01294800862092224 4.410398919734593 3.966658239689972
36 30 3.0 6 0.0 200.00414940285927 0.002074701429634729 3569 0 61.58587839731017 -3.4138707553280483 -0.0006647490253754809 7.204083538326778 6.4809759087145915
37 40 3.2 12 0.0 199.9609691102816 -0.019515444859194986 2060 0 61.99029126213592 -2.293284464503728 -0.012281258667209633 4.142089182262913 3.725180692979831
38 30 3.0 12 0.0 199.86477350989745 -0.06761324505127675 3488 0 60.321100917431195 -3.9479783254082292 -0.026217988255613115 7.044972103443261 6.337776620729888
39 30 3.0 24 0.005 199.8322173667805 -0.08389131660975124 3078 0 59.25925925925925 -4.517336088935309 -0.031095706303734948 6.227919385867038 5.602429614435114
40 40 3.0 24 0.005 199.5939851399495 -0.20300743002525223 2287 0 60.690861390467866 -3.1641220863467083 -0.07962941557219835 4.605201690819581 4.141987512861499
41 40 3.0 12 0.008 199.5739264063975 -0.21303679680124785 2148 0 63.17504655493482 -2.615052284973501 -0.08139777980329083 4.316235063292179 3.8819178516682813
42 40 3.0 6 0.01 199.4783021660671 -0.2608489169664523 1988 0 63.581488933601605 -2.6165649578562693 -0.09950203972642017 3.991524078241081 3.5896750888907443
43 40 3.0 24 0.0 199.34357556785355 -0.3282122160732257 2360 0 60.21186440677966 -3.3377613611040715 -0.1256995824626251 4.74929125388248 4.271670892552751
44 30 3.0 24 0.0 199.30001953765344 -0.34999023117327965 3243 0 58.125192722787546 -4.896971344439777 -0.12506755033132605 6.553042403347132 5.89504751509868
45 40 3.0 12 0.01 199.12086527405495 -0.4395673629725252 1956 0 62.985685071574636 -2.934702029502688 -0.16766517034426934 3.9282781540179963 3.532758588982499
46 40 3.0 6 0.005 199.01319225044602 -0.49340387477698755 2458 0 62.9780309194467 -3.206770049240731 -0.19188065769564314 4.932566481122759 4.436623248761567
47 40 3.0 6 0.0 198.65444873410513 -0.6727756329474346 2539 0 62.46553761323356 -3.483785802636902 -0.25943426479073867 5.091061673108702 4.579273159284268
48 40 3.0 12 0.005 198.53430497909721 -0.7328475104513927 2400 0 62.041666666666664 -3.6063962830445178 -0.2822792363382157 4.814513695479092 4.33038262002996
49 40 3.0 12 0.0 198.16460271518014 -0.9176986424099312 2480 0 61.41129032258065 -3.8946055152910333 -0.35154409034010436 4.970878228218944 4.47111508616677

9
strategy/results/bb_5m_practical_upgrade_candidates.csv
View File

@@ -1,9 +0,0 @@
name,bb_period,bb_std,lev,trend_ema,cooldown_bars,stop_loss,take_profit,final,ret,trades,liq,win,dd,sharpe,fee,rebate
D_trend288_cd12,30,3.0,1,288.0,12,0.0,0.0,199.86477350989745,-0.06761324505127675,3488,0,60.321100917431195,-3.9479783254082292,-0.026217988255613115,7.044972103443261,6.337776620729888
B_trend288,30,3.0,1,288.0,0,0.0,0.0,199.7540264365565,-0.1229867817217496,3668,0,62.05016357688113,-3.996173585769327,-0.046036770219455056,7.41289979748533,6.668913917641575
C_trend576,30,3.0,1,576.0,0,0.0,0.0,199.20628338025136,-0.39685830987431814,4143,0,61.18754525706011,-4.6188391069942725,-0.14008705265439372,8.355454097286774,7.515422312937967
E_trend288_cd12_sl12_tp15,30,3.0,1,288.0,12,0.012,0.015,195.3494628170402,-2.3252685914798974,3710,0,52.129380053908356,-4.882606479135802,-1.4756369432482566,7.362551062011999,6.62365864234843
F_trend288_cd6_sl10_tp20,30,3.0,1,288.0,6,0.01,0.02,195.3050328845755,-2.347483557712252,3796,0,48.840885142255004,-5.078860622065719,-1.5321092835282057,7.536365316666955,6.7800929289619924
H_bb20_30_trend576_cd12,20,3.0,1,576.0,12,0.01,0.015,189.48915432155357,-5.255422839223215,6395,0,49.1164972634871,-10.60519250897687,-2.8074280977201473,12.531119081963354,11.272889176910006
G_bb20_28_trend288_cd12,20,2.8,1,288.0,12,0.012,0.015,189.04763867132164,-5.476180664339182,6479,0,52.106806605957715,-11.116408258934996,-2.9900816266006194,12.667135948143327,11.395318795619543
A_base_no_filter,30,3.0,1,,0,0.0,0.0,187.87751952363539,-6.061240238182307,8669,0,61.62187103472142,-12.334791284875877,-1.4020222751730407,16.98060303079724,15.274934432023217
1 name bb_period bb_std lev trend_ema cooldown_bars stop_loss take_profit final ret trades liq win dd sharpe fee rebate
2 D_trend288_cd12 30 3.0 1 288.0 12 0.0 0.0 199.86477350989745 -0.06761324505127675 3488 0 60.321100917431195 -3.9479783254082292 -0.026217988255613115 7.044972103443261 6.337776620729888
3 B_trend288 30 3.0 1 288.0 0 0.0 0.0 199.7540264365565 -0.1229867817217496 3668 0 62.05016357688113 -3.996173585769327 -0.046036770219455056 7.41289979748533 6.668913917641575
4 C_trend576 30 3.0 1 576.0 0 0.0 0.0 199.20628338025136 -0.39685830987431814 4143 0 61.18754525706011 -4.6188391069942725 -0.14008705265439372 8.355454097286774 7.515422312937967
5 E_trend288_cd12_sl12_tp15 30 3.0 1 288.0 12 0.012 0.015 195.3494628170402 -2.3252685914798974 3710 0 52.129380053908356 -4.882606479135802 -1.4756369432482566 7.362551062011999 6.62365864234843
6 F_trend288_cd6_sl10_tp20 30 3.0 1 288.0 6 0.01 0.02 195.3050328845755 -2.347483557712252 3796 0 48.840885142255004 -5.078860622065719 -1.5321092835282057 7.536365316666955 6.7800929289619924
7 H_bb20_30_trend576_cd12 20 3.0 1 576.0 12 0.01 0.015 189.48915432155357 -5.255422839223215 6395 0 49.1164972634871 -10.60519250897687 -2.8074280977201473 12.531119081963354 11.272889176910006
8 G_bb20_28_trend288_cd12 20 2.8 1 288.0 12 0.012 0.015 189.04763867132164 -5.476180664339182 6479 0 52.106806605957715 -11.116408258934996 -2.9900816266006194 12.667135948143327 11.395318795619543
9 A_base_no_filter 30 3.0 1 0 0.0 0.0 187.87751952363539 -6.061240238182307 8669 0 61.62187103472142 -12.334791284875877 -1.4020222751730407 16.98060303079724 15.274934432023217

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strategy/results/bb_5m_practical_upgrade_daily.csv
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label,final_equity,return_pct,trade_count,win_rate_pct,liq_count,max_drawdown,total_fee,total_rebate,sharpe
"Baseline (BB30/3.0, no filters)",187.87751952363539,-6.061240238182307,8669,61.62187103472142,0,-12.334791284875877,16.98060303079724,15.274934432023217,-1.4020222751730407
Practical (BB30/3.2 + EMA + BW + cooldown),202.98777028457513,1.4938851422875672,2208,64.4927536231884,0,-2.2659000933991535,4.492191078113778,4.042051704177064,0.5747142550615898
1 label final_equity return_pct trade_count win_rate_pct liq_count max_drawdown total_fee total_rebate sharpe
2 Baseline (BB30/3.0, no filters) 187.87751952363539 -6.061240238182307 8669 61.62187103472142 0 -12.334791284875877 16.98060303079724 15.274934432023217 -1.4020222751730407
3 Practical (BB30/3.2 + EMA + BW + cooldown) 202.98777028457513 1.4938851422875672 2208 64.4927536231884 0 -2.2659000933991535 4.492191078113778 4.042051704177064 0.5747142550615898

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strategy/results/bb_feb2025_daily_20260228_122306.csv
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@@ -1,30 +0,0 @@
datetime,equity,pnl
2025-01-31,200.61200795007642,0.0
2025-02-01,171.90057309729912,-28.711434852777302
2025-02-02,128.95415766898418,-42.946415428314936
2025-02-03,125.57720361282368,-3.3769540561605
2025-02-04,205.81457179504082,80.23736818221714
2025-02-05,263.5266635751208,57.71209178007996
2025-02-06,313.4609154424834,49.934251867362605
2025-02-07,269.76261120078874,-43.69830424169464
2025-02-08,409.60795899114123,139.8453477903525
2025-02-09,359.82531489433495,-49.78264409680628
2025-02-10,472.7027689877503,112.87745409341534
2025-02-11,407.0815536146114,-65.62121537313891
2025-02-12,655.3670520430319,248.2854984284205
2025-02-13,619.0898255604814,-36.27722648255053
2025-02-14,575.3115910800449,-43.77823448043648
2025-02-15,576.7473792992255,1.4357882191806084
2025-02-16,625.4928812555371,48.745501956311614
2025-02-17,620.6351423585791,-4.85773889695804
2025-02-18,593.2745197106462,-27.360622647932814
2025-02-19,618.1151654923001,24.840645781653848
2025-02-20,788.2927136416205,170.1775481493204
2025-02-21,842.7610928461016,54.46837920448115
2025-02-22,820.589661113476,-22.171431732625592
2025-02-23,883.3327944501593,62.743133336683286
2025-02-24,844.8847328879968,-38.44806156216248
2025-02-25,704.0864911962235,-140.79824169177334
2025-02-26,646.8147129944842,-57.27177820173927
2025-02-27,1027.487595519961,380.67288252547667
2025-02-28,986.1656934699068,-41.321902050054064
1 datetime equity pnl
2 2025-01-31 200.61200795007642 0.0
3 2025-02-01 171.90057309729912 -28.711434852777302
4 2025-02-02 128.95415766898418 -42.946415428314936
5 2025-02-03 125.57720361282368 -3.3769540561605
6 2025-02-04 205.81457179504082 80.23736818221714
7 2025-02-05 263.5266635751208 57.71209178007996
8 2025-02-06 313.4609154424834 49.934251867362605
9 2025-02-07 269.76261120078874 -43.69830424169464
10 2025-02-08 409.60795899114123 139.8453477903525
11 2025-02-09 359.82531489433495 -49.78264409680628
12 2025-02-10 472.7027689877503 112.87745409341534
13 2025-02-11 407.0815536146114 -65.62121537313891
14 2025-02-12 655.3670520430319 248.2854984284205
15 2025-02-13 619.0898255604814 -36.27722648255053
16 2025-02-14 575.3115910800449 -43.77823448043648
17 2025-02-15 576.7473792992255 1.4357882191806084
18 2025-02-16 625.4928812555371 48.745501956311614
19 2025-02-17 620.6351423585791 -4.85773889695804
20 2025-02-18 593.2745197106462 -27.360622647932814
21 2025-02-19 618.1151654923001 24.840645781653848
22 2025-02-20 788.2927136416205 170.1775481493204
23 2025-02-21 842.7610928461016 54.46837920448115
24 2025-02-22 820.589661113476 -22.171431732625592
25 2025-02-23 883.3327944501593 62.743133336683286
26 2025-02-24 844.8847328879968 -38.44806156216248
27 2025-02-25 704.0864911962235 -140.79824169177334
28 2025-02-26 646.8147129944842 -57.27177820173927
29 2025-02-27 1027.487595519961 380.67288252547667
30 2025-02-28 986.1656934699068 -41.321902050054064

8065
strategy/results/bb_feb2025_equity_20260228_122306.csv
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strategy/results/bb_feb2025_trades_20260228_122306.csv
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@@ -1,307 +0,0 @@
entry_time,exit_time,side,entry_price,exit_price,qty,margin,gross_pnl,fee,net_pnl
2025-01-31 17:40:00,2025-01-31 18:30:00,long,3366.19,3332.70,0.3525,11.86,-11.86,0.00,-11.86
2025-01-31 18:35:00,2025-01-31 19:30:00,long,3309.95,3337.23,0.0567,1.88,1.55,0.09,1.45
2025-01-31 19:30:00,2025-01-31 20:20:00,short,3339.55,3296.01,0.1695,5.66,7.38,0.28,7.10
2025-01-31 20:20:00,2025-02-01 01:35:00,long,3284.13,3328.63,0.3561,11.70,15.85,0.59,15.26
2025-02-01 01:35:00,2025-02-01 02:30:00,short,3328.63,3312.28,0.0637,2.12,1.04,0.11,0.94
2025-02-01 02:30:00,2025-02-01 06:05:00,long,3304.11,3302.18,0.6364,21.03,-1.23,1.05,-2.28
2025-02-01 06:05:00,2025-02-01 06:20:00,short,3302.18,3292.25,0.0635,2.10,0.63,0.10,0.53
2025-02-01 06:20:00,2025-02-01 09:35:00,long,3272.61,3240.05,0.6331,20.72,-20.72,0.00,-20.72
2025-02-01 09:35:00,2025-02-01 11:50:00,long,3229.77,3248.51,0.3492,11.28,6.54,0.57,5.98
2025-02-01 11:50:00,2025-02-01 13:45:00,short,3252.37,3250.41,0.3560,11.58,0.70,0.58,0.12
2025-02-01 13:45:00,2025-02-01 19:25:00,long,3243.14,3210.87,0.5975,19.38,-19.38,0.00,-19.38
2025-02-01 22:30:00,2025-02-02 03:10:00,long,3134.76,3103.57,0.3413,10.70,-10.70,0.00,-10.70
2025-02-02 03:10:00,2025-02-02 04:20:00,long,3081.33,3105.39,0.3247,10.00,7.81,0.50,7.31
2025-02-02 04:20:00,2025-02-02 06:20:00,short,3104.64,3135.53,0.1694,5.26,-5.26,0.00,-5.26
2025-02-02 06:20:00,2025-02-02 06:45:00,short,3134.11,3111.66,0.0542,1.70,1.22,0.08,1.13
2025-02-02 06:45:00,2025-02-02 11:35:00,long,3089.90,3059.16,0.3280,10.14,-10.14,0.00,-10.14
2025-02-02 13:10:00,2025-02-02 14:45:00,short,3076.73,3088.50,0.1559,4.80,-1.84,0.24,-2.08
2025-02-02 14:45:00,2025-02-02 16:35:00,long,3074.19,3043.60,0.5035,15.48,-15.48,0.00,-15.48
2025-02-02 16:35:00,2025-02-02 16:55:00,long,3034.41,3004.22,0.0467,1.42,-1.42,0.00,-1.42
2025-02-02 17:35:00,2025-02-02 17:55:00,long,2987.55,2957.82,0.0469,1.40,-1.40,0.00,-1.40
2025-02-02 17:55:00,2025-02-02 18:35:00,long,2957.58,2928.15,0.2819,8.34,-8.34,0.00,-8.34
2025-02-02 18:35:00,2025-02-02 18:40:00,long,2945.76,2925.60,0.0441,1.30,-0.89,0.06,-0.95
2025-02-03 00:40:00,2025-02-03 01:40:00,long,2769.76,2742.20,0.0479,1.33,-1.33,0.00,-1.33
2025-02-03 01:40:00,2025-02-03 01:45:00,long,2740.19,2712.93,0.0479,1.31,-1.31,0.00,-1.31
2025-02-03 01:45:00,2025-02-03 01:50:00,long,2662.45,2635.96,0.0488,1.30,-1.30,0.00,-1.30
2025-02-03 01:50:00,2025-02-03 01:55:00,long,2562.77,2537.27,0.0502,1.29,-1.29,0.00,-1.29
2025-02-03 01:55:00,2025-02-03 02:05:00,long,2454.95,2430.53,0.0518,1.27,-1.27,0.00,-1.27
2025-02-03 02:05:00,2025-02-03 03:05:00,long,2249.82,2516.19,0.0559,1.26,14.90,0.07,14.83
2025-02-03 03:05:00,2025-02-03 03:15:00,short,2516.19,2541.22,0.0559,1.41,-1.41,0.00,-1.41
2025-02-03 03:15:00,2025-02-03 07:15:00,short,2522.28,2547.38,0.3411,8.60,-8.60,0.00,-8.60
2025-02-03 09:55:00,2025-02-03 10:35:00,long,2562.28,2602.32,0.0508,1.30,2.03,0.07,1.97
2025-02-03 10:35:00,2025-02-03 15:20:00,short,2620.62,2646.70,0.4918,12.89,-12.89,0.00,-12.89
2025-02-03 15:20:00,2025-02-03 15:40:00,short,2686.08,2712.80,0.1318,3.54,-3.54,0.00,-3.54
2025-02-03 16:50:00,2025-02-03 17:00:00,short,2728.16,2686.09,0.0421,1.15,1.77,0.06,1.71
2025-02-03 17:00:00,2025-02-03 18:35:00,long,2686.09,2732.11,0.0434,1.16,1.99,0.06,1.94
2025-02-03 18:35:00,2025-02-03 20:05:00,short,2748.14,2712.90,0.4241,11.65,14.94,0.58,14.37
2025-02-03 20:05:00,2025-02-03 21:25:00,long,2712.90,2722.03,0.0487,1.32,0.44,0.07,0.38
2025-02-03 21:25:00,2025-02-03 21:35:00,short,2724.90,2752.02,0.1456,3.97,-3.97,0.00,-3.97
2025-02-03 21:35:00,2025-02-03 21:50:00,short,2742.51,2769.79,0.0468,1.28,-1.28,0.00,-1.28
2025-02-03 21:50:00,2025-02-03 21:55:00,short,2787.57,2815.31,0.0455,1.27,-1.27,0.00,-1.27
2025-02-04 00:25:00,2025-02-04 01:35:00,long,2855.42,2857.98,0.1353,3.86,0.35,0.19,0.15
2025-02-04 01:35:00,2025-02-04 02:15:00,short,2857.98,2838.59,0.0451,1.29,0.87,0.06,0.81
2025-02-04 02:15:00,2025-02-04 02:55:00,long,2836.40,2808.18,0.1370,3.89,-3.89,0.00,-3.89
2025-02-04 04:00:00,2025-02-04 05:00:00,short,2817.83,2770.62,0.0446,1.26,2.10,0.06,2.04
2025-02-04 05:00:00,2025-02-04 05:05:00,long,2770.62,2743.05,0.0460,1.28,-1.28,0.00,-1.28
2025-02-04 05:05:00,2025-02-04 05:10:00,long,2743.16,2715.87,0.0460,1.26,-1.26,0.00,-1.26
2025-02-04 05:15:00,2025-02-04 12:30:00,long,2693.84,2809.54,0.4642,12.50,53.71,0.65,53.05
2025-02-04 12:30:00,2025-02-04 13:50:00,short,2809.54,2807.58,0.0631,1.77,0.12,0.09,0.04
2025-02-04 13:50:00,2025-02-04 14:30:00,long,2799.27,2834.88,0.1894,5.30,6.74,0.27,6.47
2025-02-04 14:30:00,2025-02-04 15:10:00,short,2839.49,2775.91,0.1935,5.49,12.30,0.27,12.03
2025-02-04 15:10:00,2025-02-04 15:15:00,long,2775.91,2748.28,0.0703,1.95,-1.95,0.00,-1.95
2025-02-04 15:15:00,2025-02-04 17:00:00,long,2751.30,2805.60,0.0702,1.93,3.81,0.10,3.71
2025-02-04 17:00:00,2025-02-04 17:10:00,short,2808.59,2836.54,0.2099,5.90,-5.90,0.00,-5.90
2025-02-04 17:10:00,2025-02-04 19:45:00,short,2846.44,2801.21,0.3969,11.30,17.95,0.56,17.40
2025-02-04 19:45:00,2025-02-04 19:55:00,long,2801.21,2773.33,0.0741,2.07,-2.07,0.00,-2.07
2025-02-04 21:00:00,2025-02-04 21:35:00,long,2703.76,2676.86,0.2272,6.14,-6.14,0.00,-6.14
2025-02-04 21:40:00,2025-02-04 23:10:00,long,2654.59,2727.16,0.0749,1.99,5.44,0.10,5.33
2025-02-04 23:10:00,2025-02-05 01:00:00,short,2738.14,2765.38,0.4522,12.38,-12.38,0.00,-12.38
2025-02-05 01:00:00,2025-02-05 01:55:00,short,2756.72,2720.88,0.0713,1.97,2.56,0.10,2.46
2025-02-05 01:55:00,2025-02-05 02:45:00,long,2720.88,2741.10,0.0731,1.99,1.48,0.10,1.38
2025-02-05 02:45:00,2025-02-05 05:20:00,short,2743.59,2709.41,0.2186,6.00,7.47,0.30,7.18
2025-02-05 05:20:00,2025-02-05 05:50:00,long,2709.41,2756.42,0.0764,2.07,3.59,0.11,3.49
2025-02-05 05:50:00,2025-02-05 05:55:00,short,2756.42,2783.85,0.0764,2.10,-2.10,0.00,-2.10
2025-02-05 05:55:00,2025-02-05 06:35:00,short,2784.74,2739.99,0.0748,2.08,3.35,0.10,3.24
2025-02-05 06:35:00,2025-02-05 07:25:00,long,2739.99,2757.79,0.0772,2.11,1.37,0.11,1.27
2025-02-05 07:25:00,2025-02-05 08:30:00,short,2764.87,2756.66,0.2300,6.36,1.89,0.32,1.57
2025-02-05 08:30:00,2025-02-05 08:45:00,long,2756.66,2771.74,0.0776,2.14,1.17,0.11,1.06
2025-02-05 08:45:00,2025-02-05 09:30:00,short,2778.61,2755.62,0.4598,12.78,10.57,0.63,9.94
2025-02-05 09:30:00,2025-02-05 11:00:00,long,2755.62,2786.77,0.0813,2.24,2.53,0.11,2.42
2025-02-05 11:00:00,2025-02-05 14:30:00,short,2800.55,2802.87,0.8044,22.53,-1.86,1.13,-2.99
2025-02-05 14:30:00,2025-02-05 15:05:00,long,2790.88,2763.11,0.7836,21.87,-21.87,0.00,-21.87
2025-02-05 15:05:00,2025-02-05 16:50:00,long,2744.10,2775.99,0.2167,5.95,6.91,0.30,6.61
2025-02-05 16:50:00,2025-02-05 18:10:00,short,2775.99,2725.69,0.0741,2.06,3.73,0.10,3.62
2025-02-05 18:10:00,2025-02-05 19:30:00,long,2720.49,2766.71,0.2301,6.26,10.64,0.32,10.32
2025-02-05 19:30:00,2025-02-05 20:30:00,short,2766.71,2768.49,0.0792,2.19,-0.14,0.11,-0.25
2025-02-05 20:30:00,2025-02-05 21:30:00,long,2756.09,2783.38,0.4706,12.97,12.84,0.65,12.19
2025-02-05 21:30:00,2025-02-05 22:20:00,short,2795.25,2753.68,0.8149,22.78,33.88,1.12,32.76
2025-02-05 22:20:00,2025-02-05 23:05:00,long,2753.68,2780.84,0.0951,2.62,2.58,0.13,2.45
2025-02-05 23:05:00,2025-02-06 00:30:00,short,2780.84,2775.45,0.0950,2.64,0.51,0.13,0.38
2025-02-06 00:30:00,2025-02-06 01:20:00,long,2775.45,2805.21,0.0996,2.77,2.97,0.14,2.83
2025-02-06 01:20:00,2025-02-06 03:00:00,short,2805.21,2804.21,0.0995,2.79,0.10,0.14,-0.04
2025-02-06 03:00:00,2025-02-06 04:20:00,long,2796.38,2826.26,0.5946,16.63,17.77,0.84,16.93
2025-02-06 04:20:00,2025-02-06 07:50:00,short,2839.60,2829.19,1.0242,29.08,10.66,1.45,9.21
2025-02-06 07:50:00,2025-02-06 09:10:00,long,2829.19,2845.31,0.1071,3.03,1.73,0.15,1.57
2025-02-06 09:10:00,2025-02-06 10:35:00,short,2849.15,2825.90,0.6379,18.17,14.83,0.90,13.93
2025-02-06 10:35:00,2025-02-06 11:30:00,long,2825.90,2797.78,0.1123,3.17,-3.17,0.00,-3.17
2025-02-06 13:25:00,2025-02-06 14:15:00,short,2785.98,2758.89,0.3373,9.40,9.14,0.47,8.67
2025-02-06 14:15:00,2025-02-06 15:40:00,long,2738.42,2711.17,1.1623,31.83,-31.83,0.00,-31.83
2025-02-06 15:40:00,2025-02-06 18:00:00,long,2707.99,2681.04,0.1066,2.89,-2.89,0.00,-2.89
2025-02-06 18:00:00,2025-02-06 18:35:00,long,2690.59,2705.89,0.3183,8.56,4.87,0.43,4.44
2025-02-06 18:35:00,2025-02-06 18:50:00,short,2705.89,2693.34,0.1071,2.90,1.34,0.14,1.20
2025-02-06 18:50:00,2025-02-06 19:10:00,long,2689.95,2709.26,0.6468,17.40,12.49,0.88,11.61
2025-02-06 19:10:00,2025-02-06 21:15:00,short,2711.61,2694.02,1.1198,30.36,19.70,1.51,18.19
2025-02-06 21:15:00,2025-02-06 22:10:00,long,2682.78,2656.08,0.3542,9.50,-9.50,0.00,-9.50
2025-02-06 22:10:00,2025-02-07 01:50:00,long,2668.85,2726.99,0.3471,9.26,20.18,0.47,19.71
2025-02-07 01:50:00,2025-02-07 03:30:00,short,2729.26,2710.94,1.2531,34.20,22.96,1.70,21.26
2025-02-07 03:30:00,2025-02-07 06:25:00,long,2706.54,2679.61,1.3297,35.99,-35.99,0.00,-35.99
2025-02-07 06:25:00,2025-02-07 07:15:00,long,2673.26,2726.92,0.3628,9.70,19.46,0.49,18.97
2025-02-07 07:15:00,2025-02-07 08:00:00,short,2726.92,2707.93,0.1258,3.43,2.39,0.17,2.22
2025-02-07 08:00:00,2025-02-07 08:30:00,long,2707.93,2725.89,0.1274,3.45,2.29,0.17,2.11
2025-02-07 08:30:00,2025-02-07 11:50:00,short,2733.17,2760.36,1.2605,34.45,-34.45,0.00,-34.45
2025-02-07 11:50:00,2025-02-07 13:30:00,short,2761.05,2729.62,0.6733,18.59,21.16,0.92,20.24
2025-02-07 13:30:00,2025-02-07 13:40:00,long,2729.62,2765.55,0.1210,3.30,4.35,0.17,4.18
2025-02-07 13:40:00,2025-02-07 15:00:00,short,2781.45,2761.31,0.7106,19.76,14.31,0.98,13.33
2025-02-07 15:00:00,2025-02-07 15:20:00,long,2746.10,2718.77,0.7475,20.53,-20.53,0.00,-20.53
2025-02-07 15:55:00,2025-02-07 17:15:00,long,2701.65,2674.77,1.1946,32.27,-32.27,0.00,-32.27
2025-02-07 17:15:00,2025-02-07 18:05:00,long,2666.13,2693.25,0.1092,2.91,2.96,0.15,2.81
2025-02-07 18:05:00,2025-02-07 18:50:00,short,2693.25,2678.83,0.1091,2.94,1.57,0.15,1.43
2025-02-07 18:50:00,2025-02-07 20:00:00,long,2667.70,2641.16,0.6583,17.56,-17.56,0.00,-17.56
2025-02-07 20:00:00,2025-02-07 20:25:00,long,2638.14,2618.87,0.3136,8.27,-6.04,0.41,-6.45
2025-02-08 00:20:00,2025-02-08 01:00:00,short,2659.12,2634.34,0.3218,8.56,7.97,0.42,7.55
2025-02-08 01:00:00,2025-02-08 03:00:00,long,2628.99,2646.02,0.3338,8.78,5.68,0.44,5.24
2025-02-08 03:00:00,2025-02-08 05:20:00,short,2647.28,2620.52,0.6764,17.91,18.10,0.89,17.21
2025-02-08 05:20:00,2025-02-08 06:25:00,long,2617.45,2629.40,0.3597,9.42,4.30,0.47,3.83
2025-02-08 06:25:00,2025-02-08 06:55:00,short,2629.40,2613.76,0.1208,3.18,1.89,0.16,1.73
2025-02-08 06:55:00,2025-02-08 07:45:00,long,2601.17,2633.06,0.7309,19.01,23.31,0.96,22.35
2025-02-08 07:45:00,2025-02-08 08:50:00,short,2633.06,2606.67,0.1294,3.41,3.41,0.17,3.25
2025-02-08 08:50:00,2025-02-08 10:20:00,long,2606.67,2626.76,0.1319,3.44,2.65,0.17,2.48
2025-02-08 10:20:00,2025-02-08 12:50:00,short,2622.21,2599.41,0.7957,20.87,18.14,1.03,17.11
2025-02-08 12:50:00,2025-02-08 13:35:00,long,2599.41,2624.48,0.1393,3.62,3.49,0.18,3.31
2025-02-08 13:35:00,2025-02-08 15:30:00,short,2624.48,2598.21,0.1392,3.65,3.66,0.18,3.48
2025-02-08 15:30:00,2025-02-08 18:25:00,long,2603.81,2632.47,1.4512,37.79,41.59,1.91,39.68
2025-02-08 18:25:00,2025-02-09 02:20:00,short,2634.47,2636.97,1.5320,40.36,-3.83,2.02,-5.85
2025-02-09 02:20:00,2025-02-09 03:20:00,long,2636.14,2646.89,0.4695,12.38,5.05,0.62,4.43
2025-02-09 03:20:00,2025-02-09 03:40:00,short,2646.89,2673.23,0.1574,4.17,-4.17,0.00,-4.17
2025-02-09 03:40:00,2025-02-09 06:05:00,short,2671.40,2652.55,0.9271,24.77,17.47,1.23,16.24
2025-02-09 06:05:00,2025-02-09 08:00:00,long,2652.55,2664.00,0.1611,4.27,1.84,0.21,1.63
2025-02-09 08:00:00,2025-02-09 09:05:00,short,2666.15,2656.54,0.4818,12.85,4.63,0.64,3.99
2025-02-09 09:05:00,2025-02-09 13:45:00,long,2654.90,2613.25,1.6309,43.30,-67.93,2.13,-70.06
2025-02-10 00:05:00,2025-02-10 01:00:00,short,2652.29,2635.88,0.1397,3.71,2.29,0.18,2.11
2025-02-10 01:00:00,2025-02-10 01:20:00,long,2635.88,2609.65,0.1413,3.72,-3.72,0.00,-3.72
2025-02-10 01:40:00,2025-02-10 02:40:00,long,2564.36,2587.75,0.4298,11.02,10.05,0.56,9.50
2025-02-10 02:40:00,2025-02-10 03:25:00,short,2588.86,2614.61,0.4372,11.32,-11.32,0.00,-11.32
2025-02-10 03:25:00,2025-02-10 03:30:00,short,2614.68,2640.69,0.1398,3.66,-3.66,0.00,-3.66
2025-02-10 03:30:00,2025-02-10 04:45:00,short,2636.16,2622.80,0.4117,10.85,5.50,0.54,4.96
2025-02-10 04:45:00,2025-02-10 05:45:00,long,2622.80,2646.46,0.1396,3.66,3.30,0.18,3.12
2025-02-10 05:45:00,2025-02-10 07:30:00,short,2645.39,2629.08,0.8401,22.22,13.70,1.10,12.59
2025-02-10 07:30:00,2025-02-10 08:05:00,long,2630.07,2644.13,0.4342,11.42,6.10,0.57,5.53
2025-02-10 08:05:00,2025-02-10 09:35:00,short,2647.45,2639.48,0.8720,23.09,6.95,1.15,5.80
2025-02-10 09:35:00,2025-02-10 09:55:00,long,2639.48,2650.07,0.1478,3.90,1.56,0.20,1.37
2025-02-10 09:55:00,2025-02-10 10:40:00,short,2655.49,2644.54,0.4411,11.71,4.83,0.58,4.25
2025-02-10 10:40:00,2025-02-10 11:35:00,long,2642.87,2652.34,0.4480,11.84,4.24,0.59,3.65
2025-02-10 11:35:00,2025-02-10 12:45:00,short,2657.30,2633.02,0.8958,23.80,21.75,1.18,20.57
2025-02-10 12:45:00,2025-02-10 13:15:00,long,2633.02,2650.03,0.1586,4.18,2.70,0.21,2.49
2025-02-10 13:15:00,2025-02-10 14:35:00,short,2671.82,2646.19,1.5391,41.12,39.46,2.04,37.42
2025-02-10 14:35:00,2025-02-10 15:35:00,long,2646.19,2658.15,0.1720,4.55,2.06,0.23,1.83
2025-02-10 15:35:00,2025-02-11 00:35:00,short,2670.79,2709.24,1.6854,45.01,-64.81,2.28,-67.10
2025-02-12 00:15:00,2025-02-12 00:40:00,short,2611.68,2592.98,0.1567,4.09,2.93,0.20,2.73
2025-02-12 00:40:00,2025-02-12 01:45:00,long,2592.98,2619.60,0.1588,4.12,4.23,0.21,4.02
2025-02-12 01:45:00,2025-02-12 02:45:00,short,2619.60,2603.26,0.1586,4.15,2.59,0.21,2.39
2025-02-12 02:45:00,2025-02-12 03:05:00,long,2598.85,2573.00,0.4811,12.50,-12.50,0.00,-12.50
2025-02-12 03:05:00,2025-02-12 05:55:00,long,2581.44,2607.92,0.9400,24.27,24.89,1.23,23.67
2025-02-12 05:55:00,2025-02-12 06:30:00,short,2609.15,2599.27,0.9799,25.57,9.68,1.27,8.41
2025-02-12 06:30:00,2025-02-12 07:15:00,long,2599.20,2614.10,0.5009,13.02,7.46,0.65,6.81
2025-02-12 07:15:00,2025-02-12 08:55:00,short,2628.75,2626.85,0.9991,26.26,1.90,1.31,0.59
2025-02-12 08:55:00,2025-02-12 09:50:00,long,2618.62,2625.17,1.6518,43.26,10.83,2.17,8.66
2025-02-12 09:50:00,2025-02-12 10:45:00,short,2627.75,2617.02,0.5085,13.36,5.46,0.67,4.79
2025-02-12 10:45:00,2025-02-12 11:45:00,long,2615.02,2633.86,0.5159,13.49,9.72,0.68,9.04
2025-02-12 11:45:00,2025-02-12 13:30:00,short,2645.19,2587.95,1.7023,45.03,97.44,2.20,95.23
2025-02-12 13:30:00,2025-02-12 13:35:00,long,2587.95,2562.20,0.2131,5.52,-5.52,0.00,-5.52
2025-02-12 13:35:00,2025-02-12 15:25:00,long,2566.68,2609.81,1.2760,32.75,55.03,1.66,53.37
2025-02-12 15:25:00,2025-02-12 16:05:00,short,2609.81,2594.26,0.2289,5.97,3.56,0.30,3.26
2025-02-12 16:05:00,2025-02-12 19:00:00,long,2587.48,2666.76,1.3842,35.82,109.74,1.85,107.89
2025-02-12 19:00:00,2025-02-12 20:10:00,short,2666.76,2693.30,0.2649,7.07,-7.07,0.00,-7.07
2025-02-12 20:10:00,2025-02-12 22:20:00,short,2694.91,2721.73,1.5423,41.56,-41.56,0.00,-41.56
2025-02-12 22:20:00,2025-02-12 22:25:00,short,2726.17,2753.30,0.2404,6.55,-6.55,0.00,-6.55
2025-02-12 22:25:00,2025-02-13 02:45:00,short,2758.15,2732.83,0.7292,20.11,18.47,1.00,17.47
2025-02-13 02:45:00,2025-02-13 03:05:00,long,2732.89,2740.00,0.7625,20.84,5.42,1.04,4.38
2025-02-13 03:05:00,2025-02-13 04:15:00,short,2742.19,2738.73,1.5238,41.78,5.28,2.09,3.19
2025-02-13 04:15:00,2025-02-13 05:05:00,long,2734.53,2705.60,2.5344,69.30,-73.32,3.43,-76.75
2025-02-14 00:10:00,2025-02-14 00:30:00,long,2664.50,2679.49,0.2374,6.32,3.56,0.32,3.24
2025-02-14 00:30:00,2025-02-14 03:45:00,short,2694.22,2717.47,2.3138,62.34,-53.79,3.14,-56.94
2025-02-15 00:20:00,2025-02-15 01:35:00,long,2709.90,2722.22,0.6420,17.40,7.90,0.87,7.03
2025-02-15 01:35:00,2025-02-15 05:15:00,short,2728.37,2717.24,1.2866,35.10,14.32,1.75,12.58
2025-02-15 05:15:00,2025-02-15 09:05:00,long,2697.87,2709.66,2.1924,59.15,25.84,2.97,22.87
2025-02-15 09:05:00,2025-02-15 09:25:00,short,2709.66,2699.68,0.2282,6.18,2.28,0.31,1.97
2025-02-15 09:25:00,2025-02-15 10:35:00,long,2697.64,2703.29,2.2899,61.77,12.93,3.10,9.84
2025-02-15 10:35:00,2025-02-15 12:35:00,short,2703.29,2705.52,0.2319,6.27,-0.52,0.31,-0.83
2025-02-15 12:35:00,2025-02-15 14:35:00,long,2702.54,2675.65,2.3027,62.23,-62.23,0.00,-62.23
2025-02-15 14:35:00,2025-02-15 18:05:00,long,2681.28,2691.49,1.2538,33.62,12.81,1.69,11.12
2025-02-15 18:05:00,2025-02-15 19:40:00,short,2699.63,2696.01,1.2593,34.00,4.56,1.70,2.86
2025-02-15 19:40:00,2025-02-15 21:45:00,long,2696.01,2697.70,0.2118,5.71,0.36,0.29,0.07
2025-02-15 21:45:00,2025-02-15 22:25:00,short,2699.29,2689.26,0.6337,17.11,6.36,0.85,5.51
2025-02-15 22:25:00,2025-02-16 00:15:00,long,2689.30,2699.21,1.2834,34.51,12.72,1.73,10.99
2025-02-16 00:15:00,2025-02-16 01:00:00,short,2699.21,2697.19,0.2274,6.14,0.46,0.31,0.15
2025-02-16 01:00:00,2025-02-16 01:55:00,long,2693.58,2699.28,2.2701,61.15,12.94,3.06,9.88
2025-02-16 01:55:00,2025-02-16 02:50:00,short,2699.28,2687.86,0.2299,6.21,2.63,0.31,2.32
2025-02-16 02:50:00,2025-02-16 03:40:00,long,2687.86,2692.43,0.2316,6.23,1.06,0.31,0.75
2025-02-16 03:40:00,2025-02-16 07:00:00,short,2705.72,2705.51,2.2597,61.14,0.48,3.06,-2.58
2025-02-16 07:00:00,2025-02-16 11:20:00,long,2697.91,2709.10,1.3719,37.01,15.34,1.86,13.48
2025-02-16 11:20:00,2025-02-16 13:05:00,short,2709.10,2701.20,0.2322,6.29,1.83,0.31,1.52
2025-02-16 13:05:00,2025-02-16 14:45:00,long,2692.37,2694.97,2.2958,61.81,5.97,3.09,2.87
2025-02-16 14:45:00,2025-02-16 16:05:00,short,2694.97,2691.43,0.2338,6.30,0.83,0.31,0.51
2025-02-16 16:05:00,2025-02-16 17:10:00,long,2691.43,2700.23,0.2341,6.30,2.06,0.32,1.74
2025-02-16 17:10:00,2025-02-16 17:35:00,short,2700.23,2690.21,0.2339,6.32,2.34,0.31,2.03
2025-02-16 17:35:00,2025-02-16 18:30:00,long,2690.21,2663.44,0.2354,6.33,-6.33,0.00,-6.33
2025-02-16 18:30:00,2025-02-16 21:35:00,long,2671.06,2683.42,1.4152,37.80,17.50,1.90,15.60
2025-02-16 21:35:00,2025-02-16 22:25:00,short,2683.42,2676.54,0.2386,6.40,1.64,0.32,1.32
2025-02-16 22:25:00,2025-02-17 01:10:00,long,2665.35,2672.11,2.3805,63.45,16.10,3.18,12.92
2025-02-17 01:10:00,2025-02-17 01:25:00,short,2672.11,2662.19,0.7673,20.50,7.61,1.02,6.59
2025-02-17 01:25:00,2025-02-17 01:35:00,long,2662.19,2673.50,0.2589,6.89,2.93,0.35,2.58
2025-02-17 01:35:00,2025-02-17 02:45:00,short,2673.50,2671.64,0.2586,6.91,0.48,0.35,0.14
2025-02-17 02:45:00,2025-02-17 05:00:00,long,2671.64,2645.05,0.2587,6.91,-6.91,0.00,-6.91
2025-02-17 05:00:00,2025-02-17 07:05:00,long,2651.53,2689.24,0.7736,20.51,29.18,1.04,28.14
2025-02-17 07:05:00,2025-02-17 07:40:00,short,2690.56,2676.20,0.7922,21.32,11.37,1.06,10.31
2025-02-17 07:40:00,2025-02-17 08:10:00,long,2676.20,2695.94,0.2692,7.20,5.31,0.36,4.95
2025-02-17 08:10:00,2025-02-17 09:10:00,short,2695.94,2722.76,0.2689,7.25,-7.25,0.00,-7.25
2025-02-17 09:40:00,2025-02-17 10:05:00,short,2736.34,2763.57,0.7857,21.50,-21.50,0.00,-21.50
2025-02-17 10:05:00,2025-02-17 13:05:00,short,2761.76,2760.91,2.4755,68.37,2.11,3.42,-1.31
2025-02-17 13:05:00,2025-02-17 13:20:00,long,2760.91,2776.41,0.2499,6.90,3.87,0.35,3.53
2025-02-17 13:20:00,2025-02-17 14:10:00,short,2776.41,2804.03,0.2497,6.93,-6.93,0.00,-6.93
2025-02-17 14:10:00,2025-02-17 15:00:00,short,2811.25,2839.22,0.2440,6.86,-6.86,0.00,-6.86
2025-02-17 15:00:00,2025-02-17 15:30:00,short,2832.87,2798.40,0.2396,6.79,8.26,0.34,7.93
2025-02-17 15:30:00,2025-02-17 15:45:00,long,2792.30,2764.52,0.7355,20.54,-20.54,0.00,-20.54
2025-02-17 15:45:00,2025-02-17 16:25:00,long,2757.54,2730.10,0.2411,6.65,-6.65,0.00,-6.65
2025-02-17 17:15:00,2025-02-17 18:00:00,short,2743.01,2721.78,0.2398,6.58,5.09,0.33,4.76
2025-02-17 18:00:00,2025-02-17 19:00:00,long,2715.50,2688.48,1.4578,39.59,-39.59,0.00,-39.59
2025-02-18 00:05:00,2025-02-18 00:20:00,short,2752.15,2724.25,0.2341,6.44,6.53,0.32,6.21
2025-02-18 00:20:00,2025-02-18 01:10:00,long,2724.25,2729.53,0.2386,6.50,1.26,0.33,0.93
2025-02-18 01:10:00,2025-02-18 02:35:00,short,2734.60,2716.91,0.7123,19.48,12.60,0.97,11.63
2025-02-18 02:35:00,2025-02-18 05:20:00,long,2711.82,2684.84,2.4188,65.59,-65.59,0.00,-65.59
2025-02-18 05:20:00,2025-02-18 05:25:00,long,2685.58,2691.82,0.2206,5.92,1.38,0.30,1.08
2025-02-19 00:05:00,2025-02-19 01:15:00,short,2674.68,2656.68,0.6723,17.98,12.11,0.89,11.21
2025-02-19 01:15:00,2025-02-19 02:10:00,long,2656.68,2683.92,0.2296,6.10,6.26,0.31,5.95
2025-02-19 02:10:00,2025-02-19 08:25:00,short,2692.69,2719.48,2.2807,61.41,-61.41,0.00,-61.41
2025-02-19 08:25:00,2025-02-19 08:55:00,short,2718.72,2705.06,0.6077,16.52,8.30,0.82,7.48
2025-02-19 08:55:00,2025-02-19 09:50:00,long,2705.06,2727.55,0.2062,5.58,4.64,0.28,4.36
2025-02-19 09:50:00,2025-02-19 12:05:00,short,2730.39,2722.46,2.0483,55.93,16.25,2.79,13.46
2025-02-19 12:05:00,2025-02-19 13:35:00,long,2719.24,2717.61,1.2605,34.28,-2.06,1.71,-3.77
2025-02-19 13:35:00,2025-02-19 14:15:00,short,2717.61,2708.98,0.2087,5.67,1.80,0.28,1.52
2025-02-19 14:15:00,2025-02-19 14:30:00,long,2708.98,2724.34,0.2098,5.68,3.22,0.29,2.94
2025-02-19 14:30:00,2025-02-19 14:40:00,short,2724.39,2697.44,0.6285,17.12,16.94,0.85,16.09
2025-02-19 14:40:00,2025-02-19 16:15:00,long,2697.44,2717.43,0.2173,5.86,4.35,0.30,4.05
2025-02-19 16:15:00,2025-02-19 19:00:00,short,2717.43,2698.14,0.2171,5.90,4.19,0.29,3.90
2025-02-19 19:00:00,2025-02-19 19:20:00,long,2698.14,2721.11,0.2200,5.94,5.05,0.30,4.75
2025-02-19 19:20:00,2025-02-19 21:50:00,short,2720.31,2712.24,2.1896,59.56,17.68,2.97,14.71
2025-02-19 21:50:00,2025-02-19 22:45:00,long,2708.93,2721.91,0.6742,18.26,8.75,0.92,7.83
2025-02-19 22:45:00,2025-02-20 00:15:00,short,2721.91,2710.13,0.2265,6.17,2.67,0.31,2.36
2025-02-20 00:15:00,2025-02-20 00:50:00,long,2710.13,2730.17,0.2381,6.45,4.77,0.32,4.45
2025-02-20 00:50:00,2025-02-20 07:05:00,short,2745.50,2726.14,2.3358,64.13,45.21,3.18,42.03
2025-02-20 07:05:00,2025-02-20 07:30:00,long,2724.21,2732.73,1.5121,41.19,12.89,2.07,10.82
2025-02-20 07:30:00,2025-02-20 08:20:00,short,2732.73,2728.47,0.2550,6.97,1.09,0.35,0.74
2025-02-20 08:20:00,2025-02-20 10:30:00,long,2728.06,2739.32,0.7664,20.91,8.63,1.05,7.58
2025-02-20 10:30:00,2025-02-20 13:20:00,short,2739.32,2733.94,0.2569,7.04,1.38,0.35,1.03
2025-02-20 13:20:00,2025-02-20 13:30:00,long,2733.94,2742.63,0.2577,7.04,2.24,0.35,1.89
2025-02-20 13:30:00,2025-02-20 14:45:00,short,2753.10,2742.37,2.5215,69.42,27.04,3.46,23.58
2025-02-20 14:45:00,2025-02-20 18:00:00,long,2719.86,2739.70,2.6135,71.08,51.85,3.58,48.27
2025-02-20 18:00:00,2025-02-20 21:10:00,short,2751.70,2740.43,2.7671,76.14,31.18,3.79,27.39
2025-02-20 21:10:00,2025-02-20 22:35:00,long,2740.43,2731.73,0.2899,7.94,-2.52,0.40,-2.92
2025-02-20 22:35:00,2025-02-21 01:25:00,short,2735.00,2725.79,0.8664,23.70,7.99,1.18,6.81
2025-02-21 01:25:00,2025-02-21 03:10:00,long,2734.60,2739.68,3.0438,83.24,15.45,4.17,11.28
2025-02-21 03:10:00,2025-02-21 05:20:00,short,2743.79,2746.43,0.9159,25.13,-2.42,1.26,-3.68
2025-02-21 05:20:00,2025-02-21 05:45:00,long,2746.43,2755.37,0.3038,8.34,2.72,0.42,2.30
2025-02-21 05:45:00,2025-02-21 06:25:00,short,2755.37,2754.18,0.3035,8.36,0.36,0.42,-0.06
2025-02-21 06:25:00,2025-02-21 06:35:00,long,2754.18,2758.21,0.9100,25.06,3.67,1.25,2.42
2025-02-21 06:35:00,2025-02-21 08:15:00,short,2763.46,2751.08,0.9064,25.05,11.22,1.25,9.97
2025-02-21 08:15:00,2025-02-21 09:10:00,long,2750.85,2757.07,0.9217,25.36,5.74,1.27,4.47
2025-02-21 09:10:00,2025-02-21 09:15:00,short,2757.07,2784.50,0.3078,8.49,-8.49,0.00,-8.49
2025-02-21 09:15:00,2025-02-21 11:05:00,short,2821.82,2794.33,0.8890,25.08,24.44,1.24,23.20
2025-02-21 11:05:00,2025-02-21 13:05:00,long,2789.32,2822.14,0.9249,25.80,30.36,1.31,29.06
2025-02-21 13:05:00,2025-02-21 14:30:00,short,2831.74,2792.99,0.9389,26.59,36.38,1.31,35.07
2025-02-21 14:30:00,2025-02-21 15:25:00,long,2792.99,2765.20,0.3306,9.23,-9.23,0.00,-9.23
2025-02-21 15:25:00,2025-02-21 15:30:00,long,2771.48,2743.91,0.3296,9.14,-9.14,0.00,-9.14
2025-02-21 15:30:00,2025-02-21 15:40:00,long,2748.04,2720.70,0.3290,9.04,-9.04,0.00,-9.04
2025-02-21 15:40:00,2025-02-21 17:35:00,long,2682.67,2655.98,0.9964,26.73,-26.73,0.00,-26.73
2025-02-21 19:00:00,2025-02-21 19:25:00,long,2667.07,2640.53,1.9283,51.43,-51.43,0.00,-51.43
2025-02-21 21:25:00,2025-02-21 22:25:00,long,2618.21,2659.02,0.9301,24.35,37.96,1.24,36.73
2025-02-21 22:25:00,2025-02-22 01:05:00,short,2665.74,2682.29,3.2220,85.89,-53.31,4.32,-57.63
2025-02-23 02:35:00,2025-02-23 06:15:00,long,2754.54,2791.39,0.3003,8.27,11.07,0.42,10.65
2025-02-23 06:15:00,2025-02-23 08:55:00,short,2808.69,2802.39,1.7714,49.75,11.15,2.48,8.67
2025-02-23 08:55:00,2025-02-23 13:00:00,long,2801.31,2798.34,3.0108,84.34,-8.94,4.21,-13.16
2025-02-23 13:00:00,2025-02-23 14:25:00,short,2810.25,2805.27,0.8780,24.67,4.37,1.23,3.14
2025-02-23 14:25:00,2025-02-23 17:35:00,long,2797.18,2817.68,2.9617,82.84,60.71,4.17,56.53
2025-02-23 17:35:00,2025-02-23 18:30:00,short,2819.26,2810.50,1.8691,52.69,16.38,2.63,13.75
2025-02-23 18:30:00,2025-02-23 20:55:00,long,2801.61,2804.82,1.8971,53.15,6.10,2.66,3.43
2025-02-23 20:55:00,2025-02-23 23:10:00,short,2806.87,2834.79,0.9528,26.74,-26.74,0.00,-26.74
2025-02-23 23:10:00,2025-02-24 00:40:00,short,2838.39,2802.76,1.8652,52.94,66.46,2.61,63.85
2025-02-24 00:40:00,2025-02-24 02:20:00,long,2802.76,2774.87,0.3425,9.60,-9.60,0.00,-9.60
2025-02-24 02:20:00,2025-02-24 03:25:00,long,2764.41,2736.90,2.0441,56.51,-56.51,0.00,-56.51
2025-02-24 03:30:00,2025-02-24 05:30:00,long,2704.01,2723.00,0.9828,26.57,18.66,1.34,17.32
2025-02-24 05:30:00,2025-02-24 07:20:00,short,2723.00,2720.30,0.3329,9.07,0.90,0.45,0.45
2025-02-24 07:20:00,2025-02-24 08:20:00,long,2717.35,2690.31,1.9946,54.20,-54.20,0.00,-54.20
2025-02-24 08:20:00,2025-02-24 08:25:00,long,2690.91,2678.65,0.3157,8.50,-3.87,0.42,-4.29
2025-02-25 00:10:00,2025-02-25 07:05:00,long,2474.53,2432.73,3.4747,85.98,-145.23,4.23,-149.46
2025-02-26 01:15:00,2025-02-26 03:15:00,short,2485.63,2505.94,2.8479,70.79,-57.84,3.57,-61.41
2025-02-27 00:05:00,2025-02-27 01:05:00,short,2343.55,2317.15,0.2787,6.53,7.36,0.32,7.03
2025-02-27 01:05:00,2025-02-27 01:20:00,long,2317.15,2354.05,0.2848,6.60,10.51,0.34,10.17
2025-02-27 01:20:00,2025-02-27 03:25:00,short,2360.61,2329.18,2.8186,66.54,88.59,3.28,85.30
2025-02-27 03:25:00,2025-02-27 04:35:00,long,2325.72,2302.58,0.9679,22.51,-22.51,0.00,-22.51
2025-02-27 05:15:00,2025-02-27 07:35:00,short,2342.03,2365.33,0.9281,21.74,-21.74,0.00,-21.74
2025-02-27 07:35:00,2025-02-27 08:00:00,short,2365.83,2352.30,0.2981,7.05,4.03,0.35,3.68
2025-02-27 08:00:00,2025-02-27 09:05:00,long,2346.32,2358.48,1.8051,42.35,21.95,2.13,19.82
2025-02-27 09:05:00,2025-02-27 13:30:00,short,2365.58,2340.68,3.0516,72.19,76.00,3.57,72.43
2025-02-27 13:30:00,2025-02-27 13:45:00,long,2340.80,2359.11,1.0188,23.85,18.65,1.20,17.45
2025-02-27 13:45:00,2025-02-27 13:50:00,short,2359.11,2334.15,0.3439,8.11,8.58,0.40,8.18
2025-02-27 13:50:00,2025-02-27 14:30:00,long,2334.15,2350.31,0.3509,8.19,5.67,0.41,5.26
2025-02-27 14:30:00,2025-02-27 14:45:00,short,2349.54,2317.31,1.0521,24.72,33.90,1.22,32.68
2025-02-27 14:45:00,2025-02-27 15:00:00,long,2317.31,2294.26,0.3692,8.55,-8.55,0.00,-8.55
2025-02-27 16:15:00,2025-02-27 16:25:00,short,2334.15,2315.18,0.3627,8.46,6.88,0.42,6.46
2025-02-27 16:25:00,2025-02-27 18:00:00,long,2307.12,2324.01,2.2016,50.79,37.19,2.56,34.63
2025-02-27 18:00:00,2025-02-27 19:05:00,short,2328.70,2320.04,3.7833,88.10,32.76,4.39,28.37
2025-02-27 19:05:00,2025-02-27 19:30:00,long,2313.75,2290.73,2.3443,54.24,-54.24,0.00,-54.24
2025-02-27 19:30:00,2025-02-27 20:45:00,long,2271.11,2248.51,1.1205,25.45,-25.45,0.00,-25.45
2025-02-27 20:45:00,2025-02-27 23:35:00,long,2250.80,2307.13,3.7528,84.47,211.42,4.33,207.09
2025-02-27 23:35:00,2025-02-28 00:30:00,short,2307.13,2299.32,0.4455,10.28,3.48,0.51,2.97
2025-02-28 00:30:00,2025-02-28 01:25:00,long,2291.71,2259.98,2.8031,64.24,-88.93,3.17,-92.10
1 entry_time exit_time side entry_price exit_price qty margin gross_pnl fee net_pnl
2 2025-01-31 17:40:00 2025-01-31 18:30:00 long 3366.19 3332.70 0.3525 11.86 -11.86 0.00 -11.86
3 2025-01-31 18:35:00 2025-01-31 19:30:00 long 3309.95 3337.23 0.0567 1.88 1.55 0.09 1.45
4 2025-01-31 19:30:00 2025-01-31 20:20:00 short 3339.55 3296.01 0.1695 5.66 7.38 0.28 7.10
5 2025-01-31 20:20:00 2025-02-01 01:35:00 long 3284.13 3328.63 0.3561 11.70 15.85 0.59 15.26
6 2025-02-01 01:35:00 2025-02-01 02:30:00 short 3328.63 3312.28 0.0637 2.12 1.04 0.11 0.94
7 2025-02-01 02:30:00 2025-02-01 06:05:00 long 3304.11 3302.18 0.6364 21.03 -1.23 1.05 -2.28
8 2025-02-01 06:05:00 2025-02-01 06:20:00 short 3302.18 3292.25 0.0635 2.10 0.63 0.10 0.53
9 2025-02-01 06:20:00 2025-02-01 09:35:00 long 3272.61 3240.05 0.6331 20.72 -20.72 0.00 -20.72
10 2025-02-01 09:35:00 2025-02-01 11:50:00 long 3229.77 3248.51 0.3492 11.28 6.54 0.57 5.98
11 2025-02-01 11:50:00 2025-02-01 13:45:00 short 3252.37 3250.41 0.3560 11.58 0.70 0.58 0.12
12 2025-02-01 13:45:00 2025-02-01 19:25:00 long 3243.14 3210.87 0.5975 19.38 -19.38 0.00 -19.38
13 2025-02-01 22:30:00 2025-02-02 03:10:00 long 3134.76 3103.57 0.3413 10.70 -10.70 0.00 -10.70
14 2025-02-02 03:10:00 2025-02-02 04:20:00 long 3081.33 3105.39 0.3247 10.00 7.81 0.50 7.31
15 2025-02-02 04:20:00 2025-02-02 06:20:00 short 3104.64 3135.53 0.1694 5.26 -5.26 0.00 -5.26
16 2025-02-02 06:20:00 2025-02-02 06:45:00 short 3134.11 3111.66 0.0542 1.70 1.22 0.08 1.13
17 2025-02-02 06:45:00 2025-02-02 11:35:00 long 3089.90 3059.16 0.3280 10.14 -10.14 0.00 -10.14
18 2025-02-02 13:10:00 2025-02-02 14:45:00 short 3076.73 3088.50 0.1559 4.80 -1.84 0.24 -2.08
19 2025-02-02 14:45:00 2025-02-02 16:35:00 long 3074.19 3043.60 0.5035 15.48 -15.48 0.00 -15.48
20 2025-02-02 16:35:00 2025-02-02 16:55:00 long 3034.41 3004.22 0.0467 1.42 -1.42 0.00 -1.42
21 2025-02-02 17:35:00 2025-02-02 17:55:00 long 2987.55 2957.82 0.0469 1.40 -1.40 0.00 -1.40
22 2025-02-02 17:55:00 2025-02-02 18:35:00 long 2957.58 2928.15 0.2819 8.34 -8.34 0.00 -8.34
23 2025-02-02 18:35:00 2025-02-02 18:40:00 long 2945.76 2925.60 0.0441 1.30 -0.89 0.06 -0.95
24 2025-02-03 00:40:00 2025-02-03 01:40:00 long 2769.76 2742.20 0.0479 1.33 -1.33 0.00 -1.33
25 2025-02-03 01:40:00 2025-02-03 01:45:00 long 2740.19 2712.93 0.0479 1.31 -1.31 0.00 -1.31
26 2025-02-03 01:45:00 2025-02-03 01:50:00 long 2662.45 2635.96 0.0488 1.30 -1.30 0.00 -1.30
27 2025-02-03 01:50:00 2025-02-03 01:55:00 long 2562.77 2537.27 0.0502 1.29 -1.29 0.00 -1.29
28 2025-02-03 01:55:00 2025-02-03 02:05:00 long 2454.95 2430.53 0.0518 1.27 -1.27 0.00 -1.27
29 2025-02-03 02:05:00 2025-02-03 03:05:00 long 2249.82 2516.19 0.0559 1.26 14.90 0.07 14.83
30 2025-02-03 03:05:00 2025-02-03 03:15:00 short 2516.19 2541.22 0.0559 1.41 -1.41 0.00 -1.41
31 2025-02-03 03:15:00 2025-02-03 07:15:00 short 2522.28 2547.38 0.3411 8.60 -8.60 0.00 -8.60
32 2025-02-03 09:55:00 2025-02-03 10:35:00 long 2562.28 2602.32 0.0508 1.30 2.03 0.07 1.97
33 2025-02-03 10:35:00 2025-02-03 15:20:00 short 2620.62 2646.70 0.4918 12.89 -12.89 0.00 -12.89
34 2025-02-03 15:20:00 2025-02-03 15:40:00 short 2686.08 2712.80 0.1318 3.54 -3.54 0.00 -3.54
35 2025-02-03 16:50:00 2025-02-03 17:00:00 short 2728.16 2686.09 0.0421 1.15 1.77 0.06 1.71
36 2025-02-03 17:00:00 2025-02-03 18:35:00 long 2686.09 2732.11 0.0434 1.16 1.99 0.06 1.94
37 2025-02-03 18:35:00 2025-02-03 20:05:00 short 2748.14 2712.90 0.4241 11.65 14.94 0.58 14.37
38 2025-02-03 20:05:00 2025-02-03 21:25:00 long 2712.90 2722.03 0.0487 1.32 0.44 0.07 0.38
39 2025-02-03 21:25:00 2025-02-03 21:35:00 short 2724.90 2752.02 0.1456 3.97 -3.97 0.00 -3.97
40 2025-02-03 21:35:00 2025-02-03 21:50:00 short 2742.51 2769.79 0.0468 1.28 -1.28 0.00 -1.28
41 2025-02-03 21:50:00 2025-02-03 21:55:00 short 2787.57 2815.31 0.0455 1.27 -1.27 0.00 -1.27
42 2025-02-04 00:25:00 2025-02-04 01:35:00 long 2855.42 2857.98 0.1353 3.86 0.35 0.19 0.15
43 2025-02-04 01:35:00 2025-02-04 02:15:00 short 2857.98 2838.59 0.0451 1.29 0.87 0.06 0.81
44 2025-02-04 02:15:00 2025-02-04 02:55:00 long 2836.40 2808.18 0.1370 3.89 -3.89 0.00 -3.89
45 2025-02-04 04:00:00 2025-02-04 05:00:00 short 2817.83 2770.62 0.0446 1.26 2.10 0.06 2.04
46 2025-02-04 05:00:00 2025-02-04 05:05:00 long 2770.62 2743.05 0.0460 1.28 -1.28 0.00 -1.28
47 2025-02-04 05:05:00 2025-02-04 05:10:00 long 2743.16 2715.87 0.0460 1.26 -1.26 0.00 -1.26
48 2025-02-04 05:15:00 2025-02-04 12:30:00 long 2693.84 2809.54 0.4642 12.50 53.71 0.65 53.05
49 2025-02-04 12:30:00 2025-02-04 13:50:00 short 2809.54 2807.58 0.0631 1.77 0.12 0.09 0.04
50 2025-02-04 13:50:00 2025-02-04 14:30:00 long 2799.27 2834.88 0.1894 5.30 6.74 0.27 6.47
51 2025-02-04 14:30:00 2025-02-04 15:10:00 short 2839.49 2775.91 0.1935 5.49 12.30 0.27 12.03
52 2025-02-04 15:10:00 2025-02-04 15:15:00 long 2775.91 2748.28 0.0703 1.95 -1.95 0.00 -1.95
53 2025-02-04 15:15:00 2025-02-04 17:00:00 long 2751.30 2805.60 0.0702 1.93 3.81 0.10 3.71
54 2025-02-04 17:00:00 2025-02-04 17:10:00 short 2808.59 2836.54 0.2099 5.90 -5.90 0.00 -5.90
55 2025-02-04 17:10:00 2025-02-04 19:45:00 short 2846.44 2801.21 0.3969 11.30 17.95 0.56 17.40
56 2025-02-04 19:45:00 2025-02-04 19:55:00 long 2801.21 2773.33 0.0741 2.07 -2.07 0.00 -2.07
57 2025-02-04 21:00:00 2025-02-04 21:35:00 long 2703.76 2676.86 0.2272 6.14 -6.14 0.00 -6.14
58 2025-02-04 21:40:00 2025-02-04 23:10:00 long 2654.59 2727.16 0.0749 1.99 5.44 0.10 5.33
59 2025-02-04 23:10:00 2025-02-05 01:00:00 short 2738.14 2765.38 0.4522 12.38 -12.38 0.00 -12.38
60 2025-02-05 01:00:00 2025-02-05 01:55:00 short 2756.72 2720.88 0.0713 1.97 2.56 0.10 2.46
61 2025-02-05 01:55:00 2025-02-05 02:45:00 long 2720.88 2741.10 0.0731 1.99 1.48 0.10 1.38
62 2025-02-05 02:45:00 2025-02-05 05:20:00 short 2743.59 2709.41 0.2186 6.00 7.47 0.30 7.18
63 2025-02-05 05:20:00 2025-02-05 05:50:00 long 2709.41 2756.42 0.0764 2.07 3.59 0.11 3.49
64 2025-02-05 05:50:00 2025-02-05 05:55:00 short 2756.42 2783.85 0.0764 2.10 -2.10 0.00 -2.10
65 2025-02-05 05:55:00 2025-02-05 06:35:00 short 2784.74 2739.99 0.0748 2.08 3.35 0.10 3.24
66 2025-02-05 06:35:00 2025-02-05 07:25:00 long 2739.99 2757.79 0.0772 2.11 1.37 0.11 1.27
67 2025-02-05 07:25:00 2025-02-05 08:30:00 short 2764.87 2756.66 0.2300 6.36 1.89 0.32 1.57
68 2025-02-05 08:30:00 2025-02-05 08:45:00 long 2756.66 2771.74 0.0776 2.14 1.17 0.11 1.06
69 2025-02-05 08:45:00 2025-02-05 09:30:00 short 2778.61 2755.62 0.4598 12.78 10.57 0.63 9.94
70 2025-02-05 09:30:00 2025-02-05 11:00:00 long 2755.62 2786.77 0.0813 2.24 2.53 0.11 2.42
71 2025-02-05 11:00:00 2025-02-05 14:30:00 short 2800.55 2802.87 0.8044 22.53 -1.86 1.13 -2.99
72 2025-02-05 14:30:00 2025-02-05 15:05:00 long 2790.88 2763.11 0.7836 21.87 -21.87 0.00 -21.87
73 2025-02-05 15:05:00 2025-02-05 16:50:00 long 2744.10 2775.99 0.2167 5.95 6.91 0.30 6.61
74 2025-02-05 16:50:00 2025-02-05 18:10:00 short 2775.99 2725.69 0.0741 2.06 3.73 0.10 3.62
75 2025-02-05 18:10:00 2025-02-05 19:30:00 long 2720.49 2766.71 0.2301 6.26 10.64 0.32 10.32
76 2025-02-05 19:30:00 2025-02-05 20:30:00 short 2766.71 2768.49 0.0792 2.19 -0.14 0.11 -0.25
77 2025-02-05 20:30:00 2025-02-05 21:30:00 long 2756.09 2783.38 0.4706 12.97 12.84 0.65 12.19
78 2025-02-05 21:30:00 2025-02-05 22:20:00 short 2795.25 2753.68 0.8149 22.78 33.88 1.12 32.76
79 2025-02-05 22:20:00 2025-02-05 23:05:00 long 2753.68 2780.84 0.0951 2.62 2.58 0.13 2.45
80 2025-02-05 23:05:00 2025-02-06 00:30:00 short 2780.84 2775.45 0.0950 2.64 0.51 0.13 0.38
81 2025-02-06 00:30:00 2025-02-06 01:20:00 long 2775.45 2805.21 0.0996 2.77 2.97 0.14 2.83
82 2025-02-06 01:20:00 2025-02-06 03:00:00 short 2805.21 2804.21 0.0995 2.79 0.10 0.14 -0.04
83 2025-02-06 03:00:00 2025-02-06 04:20:00 long 2796.38 2826.26 0.5946 16.63 17.77 0.84 16.93
84 2025-02-06 04:20:00 2025-02-06 07:50:00 short 2839.60 2829.19 1.0242 29.08 10.66 1.45 9.21
85 2025-02-06 07:50:00 2025-02-06 09:10:00 long 2829.19 2845.31 0.1071 3.03 1.73 0.15 1.57
86 2025-02-06 09:10:00 2025-02-06 10:35:00 short 2849.15 2825.90 0.6379 18.17 14.83 0.90 13.93
87 2025-02-06 10:35:00 2025-02-06 11:30:00 long 2825.90 2797.78 0.1123 3.17 -3.17 0.00 -3.17
88 2025-02-06 13:25:00 2025-02-06 14:15:00 short 2785.98 2758.89 0.3373 9.40 9.14 0.47 8.67
89 2025-02-06 14:15:00 2025-02-06 15:40:00 long 2738.42 2711.17 1.1623 31.83 -31.83 0.00 -31.83
90 2025-02-06 15:40:00 2025-02-06 18:00:00 long 2707.99 2681.04 0.1066 2.89 -2.89 0.00 -2.89
91 2025-02-06 18:00:00 2025-02-06 18:35:00 long 2690.59 2705.89 0.3183 8.56 4.87 0.43 4.44
92 2025-02-06 18:35:00 2025-02-06 18:50:00 short 2705.89 2693.34 0.1071 2.90 1.34 0.14 1.20
93 2025-02-06 18:50:00 2025-02-06 19:10:00 long 2689.95 2709.26 0.6468 17.40 12.49 0.88 11.61
94 2025-02-06 19:10:00 2025-02-06 21:15:00 short 2711.61 2694.02 1.1198 30.36 19.70 1.51 18.19
95 2025-02-06 21:15:00 2025-02-06 22:10:00 long 2682.78 2656.08 0.3542 9.50 -9.50 0.00 -9.50
96 2025-02-06 22:10:00 2025-02-07 01:50:00 long 2668.85 2726.99 0.3471 9.26 20.18 0.47 19.71
97 2025-02-07 01:50:00 2025-02-07 03:30:00 short 2729.26 2710.94 1.2531 34.20 22.96 1.70 21.26
98 2025-02-07 03:30:00 2025-02-07 06:25:00 long 2706.54 2679.61 1.3297 35.99 -35.99 0.00 -35.99
99 2025-02-07 06:25:00 2025-02-07 07:15:00 long 2673.26 2726.92 0.3628 9.70 19.46 0.49 18.97
100 2025-02-07 07:15:00 2025-02-07 08:00:00 short 2726.92 2707.93 0.1258 3.43 2.39 0.17 2.22
101 2025-02-07 08:00:00 2025-02-07 08:30:00 long 2707.93 2725.89 0.1274 3.45 2.29 0.17 2.11
102 2025-02-07 08:30:00 2025-02-07 11:50:00 short 2733.17 2760.36 1.2605 34.45 -34.45 0.00 -34.45
103 2025-02-07 11:50:00 2025-02-07 13:30:00 short 2761.05 2729.62 0.6733 18.59 21.16 0.92 20.24
104 2025-02-07 13:30:00 2025-02-07 13:40:00 long 2729.62 2765.55 0.1210 3.30 4.35 0.17 4.18
105 2025-02-07 13:40:00 2025-02-07 15:00:00 short 2781.45 2761.31 0.7106 19.76 14.31 0.98 13.33
106 2025-02-07 15:00:00 2025-02-07 15:20:00 long 2746.10 2718.77 0.7475 20.53 -20.53 0.00 -20.53
107 2025-02-07 15:55:00 2025-02-07 17:15:00 long 2701.65 2674.77 1.1946 32.27 -32.27 0.00 -32.27
108 2025-02-07 17:15:00 2025-02-07 18:05:00 long 2666.13 2693.25 0.1092 2.91 2.96 0.15 2.81
109 2025-02-07 18:05:00 2025-02-07 18:50:00 short 2693.25 2678.83 0.1091 2.94 1.57 0.15 1.43
110 2025-02-07 18:50:00 2025-02-07 20:00:00 long 2667.70 2641.16 0.6583 17.56 -17.56 0.00 -17.56
111 2025-02-07 20:00:00 2025-02-07 20:25:00 long 2638.14 2618.87 0.3136 8.27 -6.04 0.41 -6.45
112 2025-02-08 00:20:00 2025-02-08 01:00:00 short 2659.12 2634.34 0.3218 8.56 7.97 0.42 7.55
113 2025-02-08 01:00:00 2025-02-08 03:00:00 long 2628.99 2646.02 0.3338 8.78 5.68 0.44 5.24
114 2025-02-08 03:00:00 2025-02-08 05:20:00 short 2647.28 2620.52 0.6764 17.91 18.10 0.89 17.21
115 2025-02-08 05:20:00 2025-02-08 06:25:00 long 2617.45 2629.40 0.3597 9.42 4.30 0.47 3.83
116 2025-02-08 06:25:00 2025-02-08 06:55:00 short 2629.40 2613.76 0.1208 3.18 1.89 0.16 1.73
117 2025-02-08 06:55:00 2025-02-08 07:45:00 long 2601.17 2633.06 0.7309 19.01 23.31 0.96 22.35
118 2025-02-08 07:45:00 2025-02-08 08:50:00 short 2633.06 2606.67 0.1294 3.41 3.41 0.17 3.25
119 2025-02-08 08:50:00 2025-02-08 10:20:00 long 2606.67 2626.76 0.1319 3.44 2.65 0.17 2.48
120 2025-02-08 10:20:00 2025-02-08 12:50:00 short 2622.21 2599.41 0.7957 20.87 18.14 1.03 17.11
121 2025-02-08 12:50:00 2025-02-08 13:35:00 long 2599.41 2624.48 0.1393 3.62 3.49 0.18 3.31
122 2025-02-08 13:35:00 2025-02-08 15:30:00 short 2624.48 2598.21 0.1392 3.65 3.66 0.18 3.48
123 2025-02-08 15:30:00 2025-02-08 18:25:00 long 2603.81 2632.47 1.4512 37.79 41.59 1.91 39.68
124 2025-02-08 18:25:00 2025-02-09 02:20:00 short 2634.47 2636.97 1.5320 40.36 -3.83 2.02 -5.85
125 2025-02-09 02:20:00 2025-02-09 03:20:00 long 2636.14 2646.89 0.4695 12.38 5.05 0.62 4.43
126 2025-02-09 03:20:00 2025-02-09 03:40:00 short 2646.89 2673.23 0.1574 4.17 -4.17 0.00 -4.17
127 2025-02-09 03:40:00 2025-02-09 06:05:00 short 2671.40 2652.55 0.9271 24.77 17.47 1.23 16.24
128 2025-02-09 06:05:00 2025-02-09 08:00:00 long 2652.55 2664.00 0.1611 4.27 1.84 0.21 1.63
129 2025-02-09 08:00:00 2025-02-09 09:05:00 short 2666.15 2656.54 0.4818 12.85 4.63 0.64 3.99
130 2025-02-09 09:05:00 2025-02-09 13:45:00 long 2654.90 2613.25 1.6309 43.30 -67.93 2.13 -70.06
131 2025-02-10 00:05:00 2025-02-10 01:00:00 short 2652.29 2635.88 0.1397 3.71 2.29 0.18 2.11
132 2025-02-10 01:00:00 2025-02-10 01:20:00 long 2635.88 2609.65 0.1413 3.72 -3.72 0.00 -3.72
133 2025-02-10 01:40:00 2025-02-10 02:40:00 long 2564.36 2587.75 0.4298 11.02 10.05 0.56 9.50
134 2025-02-10 02:40:00 2025-02-10 03:25:00 short 2588.86 2614.61 0.4372 11.32 -11.32 0.00 -11.32
135 2025-02-10 03:25:00 2025-02-10 03:30:00 short 2614.68 2640.69 0.1398 3.66 -3.66 0.00 -3.66
136 2025-02-10 03:30:00 2025-02-10 04:45:00 short 2636.16 2622.80 0.4117 10.85 5.50 0.54 4.96
137 2025-02-10 04:45:00 2025-02-10 05:45:00 long 2622.80 2646.46 0.1396 3.66 3.30 0.18 3.12
138 2025-02-10 05:45:00 2025-02-10 07:30:00 short 2645.39 2629.08 0.8401 22.22 13.70 1.10 12.59
139 2025-02-10 07:30:00 2025-02-10 08:05:00 long 2630.07 2644.13 0.4342 11.42 6.10 0.57 5.53
140 2025-02-10 08:05:00 2025-02-10 09:35:00 short 2647.45 2639.48 0.8720 23.09 6.95 1.15 5.80
141 2025-02-10 09:35:00 2025-02-10 09:55:00 long 2639.48 2650.07 0.1478 3.90 1.56 0.20 1.37
142 2025-02-10 09:55:00 2025-02-10 10:40:00 short 2655.49 2644.54 0.4411 11.71 4.83 0.58 4.25
143 2025-02-10 10:40:00 2025-02-10 11:35:00 long 2642.87 2652.34 0.4480 11.84 4.24 0.59 3.65
144 2025-02-10 11:35:00 2025-02-10 12:45:00 short 2657.30 2633.02 0.8958 23.80 21.75 1.18 20.57
145 2025-02-10 12:45:00 2025-02-10 13:15:00 long 2633.02 2650.03 0.1586 4.18 2.70 0.21 2.49
146 2025-02-10 13:15:00 2025-02-10 14:35:00 short 2671.82 2646.19 1.5391 41.12 39.46 2.04 37.42
147 2025-02-10 14:35:00 2025-02-10 15:35:00 long 2646.19 2658.15 0.1720 4.55 2.06 0.23 1.83
148 2025-02-10 15:35:00 2025-02-11 00:35:00 short 2670.79 2709.24 1.6854 45.01 -64.81 2.28 -67.10
149 2025-02-12 00:15:00 2025-02-12 00:40:00 short 2611.68 2592.98 0.1567 4.09 2.93 0.20 2.73
150 2025-02-12 00:40:00 2025-02-12 01:45:00 long 2592.98 2619.60 0.1588 4.12 4.23 0.21 4.02
151 2025-02-12 01:45:00 2025-02-12 02:45:00 short 2619.60 2603.26 0.1586 4.15 2.59 0.21 2.39
152 2025-02-12 02:45:00 2025-02-12 03:05:00 long 2598.85 2573.00 0.4811 12.50 -12.50 0.00 -12.50
153 2025-02-12 03:05:00 2025-02-12 05:55:00 long 2581.44 2607.92 0.9400 24.27 24.89 1.23 23.67
154 2025-02-12 05:55:00 2025-02-12 06:30:00 short 2609.15 2599.27 0.9799 25.57 9.68 1.27 8.41
155 2025-02-12 06:30:00 2025-02-12 07:15:00 long 2599.20 2614.10 0.5009 13.02 7.46 0.65 6.81
156 2025-02-12 07:15:00 2025-02-12 08:55:00 short 2628.75 2626.85 0.9991 26.26 1.90 1.31 0.59
157 2025-02-12 08:55:00 2025-02-12 09:50:00 long 2618.62 2625.17 1.6518 43.26 10.83 2.17 8.66
158 2025-02-12 09:50:00 2025-02-12 10:45:00 short 2627.75 2617.02 0.5085 13.36 5.46 0.67 4.79
159 2025-02-12 10:45:00 2025-02-12 11:45:00 long 2615.02 2633.86 0.5159 13.49 9.72 0.68 9.04
160 2025-02-12 11:45:00 2025-02-12 13:30:00 short 2645.19 2587.95 1.7023 45.03 97.44 2.20 95.23
161 2025-02-12 13:30:00 2025-02-12 13:35:00 long 2587.95 2562.20 0.2131 5.52 -5.52 0.00 -5.52
162 2025-02-12 13:35:00 2025-02-12 15:25:00 long 2566.68 2609.81 1.2760 32.75 55.03 1.66 53.37
163 2025-02-12 15:25:00 2025-02-12 16:05:00 short 2609.81 2594.26 0.2289 5.97 3.56 0.30 3.26
164 2025-02-12 16:05:00 2025-02-12 19:00:00 long 2587.48 2666.76 1.3842 35.82 109.74 1.85 107.89
165 2025-02-12 19:00:00 2025-02-12 20:10:00 short 2666.76 2693.30 0.2649 7.07 -7.07 0.00 -7.07
166 2025-02-12 20:10:00 2025-02-12 22:20:00 short 2694.91 2721.73 1.5423 41.56 -41.56 0.00 -41.56
167 2025-02-12 22:20:00 2025-02-12 22:25:00 short 2726.17 2753.30 0.2404 6.55 -6.55 0.00 -6.55
168 2025-02-12 22:25:00 2025-02-13 02:45:00 short 2758.15 2732.83 0.7292 20.11 18.47 1.00 17.47
169 2025-02-13 02:45:00 2025-02-13 03:05:00 long 2732.89 2740.00 0.7625 20.84 5.42 1.04 4.38
170 2025-02-13 03:05:00 2025-02-13 04:15:00 short 2742.19 2738.73 1.5238 41.78 5.28 2.09 3.19
171 2025-02-13 04:15:00 2025-02-13 05:05:00 long 2734.53 2705.60 2.5344 69.30 -73.32 3.43 -76.75
172 2025-02-14 00:10:00 2025-02-14 00:30:00 long 2664.50 2679.49 0.2374 6.32 3.56 0.32 3.24
173 2025-02-14 00:30:00 2025-02-14 03:45:00 short 2694.22 2717.47 2.3138 62.34 -53.79 3.14 -56.94
174 2025-02-15 00:20:00 2025-02-15 01:35:00 long 2709.90 2722.22 0.6420 17.40 7.90 0.87 7.03
175 2025-02-15 01:35:00 2025-02-15 05:15:00 short 2728.37 2717.24 1.2866 35.10 14.32 1.75 12.58
176 2025-02-15 05:15:00 2025-02-15 09:05:00 long 2697.87 2709.66 2.1924 59.15 25.84 2.97 22.87
177 2025-02-15 09:05:00 2025-02-15 09:25:00 short 2709.66 2699.68 0.2282 6.18 2.28 0.31 1.97
178 2025-02-15 09:25:00 2025-02-15 10:35:00 long 2697.64 2703.29 2.2899 61.77 12.93 3.10 9.84
179 2025-02-15 10:35:00 2025-02-15 12:35:00 short 2703.29 2705.52 0.2319 6.27 -0.52 0.31 -0.83
180 2025-02-15 12:35:00 2025-02-15 14:35:00 long 2702.54 2675.65 2.3027 62.23 -62.23 0.00 -62.23
181 2025-02-15 14:35:00 2025-02-15 18:05:00 long 2681.28 2691.49 1.2538 33.62 12.81 1.69 11.12
182 2025-02-15 18:05:00 2025-02-15 19:40:00 short 2699.63 2696.01 1.2593 34.00 4.56 1.70 2.86
183 2025-02-15 19:40:00 2025-02-15 21:45:00 long 2696.01 2697.70 0.2118 5.71 0.36 0.29 0.07
184 2025-02-15 21:45:00 2025-02-15 22:25:00 short 2699.29 2689.26 0.6337 17.11 6.36 0.85 5.51
185 2025-02-15 22:25:00 2025-02-16 00:15:00 long 2689.30 2699.21 1.2834 34.51 12.72 1.73 10.99
186 2025-02-16 00:15:00 2025-02-16 01:00:00 short 2699.21 2697.19 0.2274 6.14 0.46 0.31 0.15
187 2025-02-16 01:00:00 2025-02-16 01:55:00 long 2693.58 2699.28 2.2701 61.15 12.94 3.06 9.88
188 2025-02-16 01:55:00 2025-02-16 02:50:00 short 2699.28 2687.86 0.2299 6.21 2.63 0.31 2.32
189 2025-02-16 02:50:00 2025-02-16 03:40:00 long 2687.86 2692.43 0.2316 6.23 1.06 0.31 0.75
190 2025-02-16 03:40:00 2025-02-16 07:00:00 short 2705.72 2705.51 2.2597 61.14 0.48 3.06 -2.58
191 2025-02-16 07:00:00 2025-02-16 11:20:00 long 2697.91 2709.10 1.3719 37.01 15.34 1.86 13.48
192 2025-02-16 11:20:00 2025-02-16 13:05:00 short 2709.10 2701.20 0.2322 6.29 1.83 0.31 1.52
193 2025-02-16 13:05:00 2025-02-16 14:45:00 long 2692.37 2694.97 2.2958 61.81 5.97 3.09 2.87
194 2025-02-16 14:45:00 2025-02-16 16:05:00 short 2694.97 2691.43 0.2338 6.30 0.83 0.31 0.51
195 2025-02-16 16:05:00 2025-02-16 17:10:00 long 2691.43 2700.23 0.2341 6.30 2.06 0.32 1.74
196 2025-02-16 17:10:00 2025-02-16 17:35:00 short 2700.23 2690.21 0.2339 6.32 2.34 0.31 2.03
197 2025-02-16 17:35:00 2025-02-16 18:30:00 long 2690.21 2663.44 0.2354 6.33 -6.33 0.00 -6.33
198 2025-02-16 18:30:00 2025-02-16 21:35:00 long 2671.06 2683.42 1.4152 37.80 17.50 1.90 15.60
199 2025-02-16 21:35:00 2025-02-16 22:25:00 short 2683.42 2676.54 0.2386 6.40 1.64 0.32 1.32
200 2025-02-16 22:25:00 2025-02-17 01:10:00 long 2665.35 2672.11 2.3805 63.45 16.10 3.18 12.92
201 2025-02-17 01:10:00 2025-02-17 01:25:00 short 2672.11 2662.19 0.7673 20.50 7.61 1.02 6.59
202 2025-02-17 01:25:00 2025-02-17 01:35:00 long 2662.19 2673.50 0.2589 6.89 2.93 0.35 2.58
203 2025-02-17 01:35:00 2025-02-17 02:45:00 short 2673.50 2671.64 0.2586 6.91 0.48 0.35 0.14
204 2025-02-17 02:45:00 2025-02-17 05:00:00 long 2671.64 2645.05 0.2587 6.91 -6.91 0.00 -6.91
205 2025-02-17 05:00:00 2025-02-17 07:05:00 long 2651.53 2689.24 0.7736 20.51 29.18 1.04 28.14
206 2025-02-17 07:05:00 2025-02-17 07:40:00 short 2690.56 2676.20 0.7922 21.32 11.37 1.06 10.31
207 2025-02-17 07:40:00 2025-02-17 08:10:00 long 2676.20 2695.94 0.2692 7.20 5.31 0.36 4.95
208 2025-02-17 08:10:00 2025-02-17 09:10:00 short 2695.94 2722.76 0.2689 7.25 -7.25 0.00 -7.25
209 2025-02-17 09:40:00 2025-02-17 10:05:00 short 2736.34 2763.57 0.7857 21.50 -21.50 0.00 -21.50
210 2025-02-17 10:05:00 2025-02-17 13:05:00 short 2761.76 2760.91 2.4755 68.37 2.11 3.42 -1.31
211 2025-02-17 13:05:00 2025-02-17 13:20:00 long 2760.91 2776.41 0.2499 6.90 3.87 0.35 3.53
212 2025-02-17 13:20:00 2025-02-17 14:10:00 short 2776.41 2804.03 0.2497 6.93 -6.93 0.00 -6.93
213 2025-02-17 14:10:00 2025-02-17 15:00:00 short 2811.25 2839.22 0.2440 6.86 -6.86 0.00 -6.86
214 2025-02-17 15:00:00 2025-02-17 15:30:00 short 2832.87 2798.40 0.2396 6.79 8.26 0.34 7.93
215 2025-02-17 15:30:00 2025-02-17 15:45:00 long 2792.30 2764.52 0.7355 20.54 -20.54 0.00 -20.54
216 2025-02-17 15:45:00 2025-02-17 16:25:00 long 2757.54 2730.10 0.2411 6.65 -6.65 0.00 -6.65
217 2025-02-17 17:15:00 2025-02-17 18:00:00 short 2743.01 2721.78 0.2398 6.58 5.09 0.33 4.76
218 2025-02-17 18:00:00 2025-02-17 19:00:00 long 2715.50 2688.48 1.4578 39.59 -39.59 0.00 -39.59
219 2025-02-18 00:05:00 2025-02-18 00:20:00 short 2752.15 2724.25 0.2341 6.44 6.53 0.32 6.21
220 2025-02-18 00:20:00 2025-02-18 01:10:00 long 2724.25 2729.53 0.2386 6.50 1.26 0.33 0.93
221 2025-02-18 01:10:00 2025-02-18 02:35:00 short 2734.60 2716.91 0.7123 19.48 12.60 0.97 11.63
222 2025-02-18 02:35:00 2025-02-18 05:20:00 long 2711.82 2684.84 2.4188 65.59 -65.59 0.00 -65.59
223 2025-02-18 05:20:00 2025-02-18 05:25:00 long 2685.58 2691.82 0.2206 5.92 1.38 0.30 1.08
224 2025-02-19 00:05:00 2025-02-19 01:15:00 short 2674.68 2656.68 0.6723 17.98 12.11 0.89 11.21
225 2025-02-19 01:15:00 2025-02-19 02:10:00 long 2656.68 2683.92 0.2296 6.10 6.26 0.31 5.95
226 2025-02-19 02:10:00 2025-02-19 08:25:00 short 2692.69 2719.48 2.2807 61.41 -61.41 0.00 -61.41
227 2025-02-19 08:25:00 2025-02-19 08:55:00 short 2718.72 2705.06 0.6077 16.52 8.30 0.82 7.48
228 2025-02-19 08:55:00 2025-02-19 09:50:00 long 2705.06 2727.55 0.2062 5.58 4.64 0.28 4.36
229 2025-02-19 09:50:00 2025-02-19 12:05:00 short 2730.39 2722.46 2.0483 55.93 16.25 2.79 13.46
230 2025-02-19 12:05:00 2025-02-19 13:35:00 long 2719.24 2717.61 1.2605 34.28 -2.06 1.71 -3.77
231 2025-02-19 13:35:00 2025-02-19 14:15:00 short 2717.61 2708.98 0.2087 5.67 1.80 0.28 1.52
232 2025-02-19 14:15:00 2025-02-19 14:30:00 long 2708.98 2724.34 0.2098 5.68 3.22 0.29 2.94
233 2025-02-19 14:30:00 2025-02-19 14:40:00 short 2724.39 2697.44 0.6285 17.12 16.94 0.85 16.09
234 2025-02-19 14:40:00 2025-02-19 16:15:00 long 2697.44 2717.43 0.2173 5.86 4.35 0.30 4.05
235 2025-02-19 16:15:00 2025-02-19 19:00:00 short 2717.43 2698.14 0.2171 5.90 4.19 0.29 3.90
236 2025-02-19 19:00:00 2025-02-19 19:20:00 long 2698.14 2721.11 0.2200 5.94 5.05 0.30 4.75
237 2025-02-19 19:20:00 2025-02-19 21:50:00 short 2720.31 2712.24 2.1896 59.56 17.68 2.97 14.71
238 2025-02-19 21:50:00 2025-02-19 22:45:00 long 2708.93 2721.91 0.6742 18.26 8.75 0.92 7.83
239 2025-02-19 22:45:00 2025-02-20 00:15:00 short 2721.91 2710.13 0.2265 6.17 2.67 0.31 2.36
240 2025-02-20 00:15:00 2025-02-20 00:50:00 long 2710.13 2730.17 0.2381 6.45 4.77 0.32 4.45
241 2025-02-20 00:50:00 2025-02-20 07:05:00 short 2745.50 2726.14 2.3358 64.13 45.21 3.18 42.03
242 2025-02-20 07:05:00 2025-02-20 07:30:00 long 2724.21 2732.73 1.5121 41.19 12.89 2.07 10.82
243 2025-02-20 07:30:00 2025-02-20 08:20:00 short 2732.73 2728.47 0.2550 6.97 1.09 0.35 0.74
244 2025-02-20 08:20:00 2025-02-20 10:30:00 long 2728.06 2739.32 0.7664 20.91 8.63 1.05 7.58
245 2025-02-20 10:30:00 2025-02-20 13:20:00 short 2739.32 2733.94 0.2569 7.04 1.38 0.35 1.03
246 2025-02-20 13:20:00 2025-02-20 13:30:00 long 2733.94 2742.63 0.2577 7.04 2.24 0.35 1.89
247 2025-02-20 13:30:00 2025-02-20 14:45:00 short 2753.10 2742.37 2.5215 69.42 27.04 3.46 23.58
248 2025-02-20 14:45:00 2025-02-20 18:00:00 long 2719.86 2739.70 2.6135 71.08 51.85 3.58 48.27
249 2025-02-20 18:00:00 2025-02-20 21:10:00 short 2751.70 2740.43 2.7671 76.14 31.18 3.79 27.39
250 2025-02-20 21:10:00 2025-02-20 22:35:00 long 2740.43 2731.73 0.2899 7.94 -2.52 0.40 -2.92
251 2025-02-20 22:35:00 2025-02-21 01:25:00 short 2735.00 2725.79 0.8664 23.70 7.99 1.18 6.81
252 2025-02-21 01:25:00 2025-02-21 03:10:00 long 2734.60 2739.68 3.0438 83.24 15.45 4.17 11.28
253 2025-02-21 03:10:00 2025-02-21 05:20:00 short 2743.79 2746.43 0.9159 25.13 -2.42 1.26 -3.68
254 2025-02-21 05:20:00 2025-02-21 05:45:00 long 2746.43 2755.37 0.3038 8.34 2.72 0.42 2.30
255 2025-02-21 05:45:00 2025-02-21 06:25:00 short 2755.37 2754.18 0.3035 8.36 0.36 0.42 -0.06
256 2025-02-21 06:25:00 2025-02-21 06:35:00 long 2754.18 2758.21 0.9100 25.06 3.67 1.25 2.42
257 2025-02-21 06:35:00 2025-02-21 08:15:00 short 2763.46 2751.08 0.9064 25.05 11.22 1.25 9.97
258 2025-02-21 08:15:00 2025-02-21 09:10:00 long 2750.85 2757.07 0.9217 25.36 5.74 1.27 4.47
259 2025-02-21 09:10:00 2025-02-21 09:15:00 short 2757.07 2784.50 0.3078 8.49 -8.49 0.00 -8.49
260 2025-02-21 09:15:00 2025-02-21 11:05:00 short 2821.82 2794.33 0.8890 25.08 24.44 1.24 23.20
261 2025-02-21 11:05:00 2025-02-21 13:05:00 long 2789.32 2822.14 0.9249 25.80 30.36 1.31 29.06
262 2025-02-21 13:05:00 2025-02-21 14:30:00 short 2831.74 2792.99 0.9389 26.59 36.38 1.31 35.07
263 2025-02-21 14:30:00 2025-02-21 15:25:00 long 2792.99 2765.20 0.3306 9.23 -9.23 0.00 -9.23
264 2025-02-21 15:25:00 2025-02-21 15:30:00 long 2771.48 2743.91 0.3296 9.14 -9.14 0.00 -9.14
265 2025-02-21 15:30:00 2025-02-21 15:40:00 long 2748.04 2720.70 0.3290 9.04 -9.04 0.00 -9.04
266 2025-02-21 15:40:00 2025-02-21 17:35:00 long 2682.67 2655.98 0.9964 26.73 -26.73 0.00 -26.73
267 2025-02-21 19:00:00 2025-02-21 19:25:00 long 2667.07 2640.53 1.9283 51.43 -51.43 0.00 -51.43
268 2025-02-21 21:25:00 2025-02-21 22:25:00 long 2618.21 2659.02 0.9301 24.35 37.96 1.24 36.73
269 2025-02-21 22:25:00 2025-02-22 01:05:00 short 2665.74 2682.29 3.2220 85.89 -53.31 4.32 -57.63
270 2025-02-23 02:35:00 2025-02-23 06:15:00 long 2754.54 2791.39 0.3003 8.27 11.07 0.42 10.65
271 2025-02-23 06:15:00 2025-02-23 08:55:00 short 2808.69 2802.39 1.7714 49.75 11.15 2.48 8.67
272 2025-02-23 08:55:00 2025-02-23 13:00:00 long 2801.31 2798.34 3.0108 84.34 -8.94 4.21 -13.16
273 2025-02-23 13:00:00 2025-02-23 14:25:00 short 2810.25 2805.27 0.8780 24.67 4.37 1.23 3.14
274 2025-02-23 14:25:00 2025-02-23 17:35:00 long 2797.18 2817.68 2.9617 82.84 60.71 4.17 56.53
275 2025-02-23 17:35:00 2025-02-23 18:30:00 short 2819.26 2810.50 1.8691 52.69 16.38 2.63 13.75
276 2025-02-23 18:30:00 2025-02-23 20:55:00 long 2801.61 2804.82 1.8971 53.15 6.10 2.66 3.43
277 2025-02-23 20:55:00 2025-02-23 23:10:00 short 2806.87 2834.79 0.9528 26.74 -26.74 0.00 -26.74
278 2025-02-23 23:10:00 2025-02-24 00:40:00 short 2838.39 2802.76 1.8652 52.94 66.46 2.61 63.85
279 2025-02-24 00:40:00 2025-02-24 02:20:00 long 2802.76 2774.87 0.3425 9.60 -9.60 0.00 -9.60
280 2025-02-24 02:20:00 2025-02-24 03:25:00 long 2764.41 2736.90 2.0441 56.51 -56.51 0.00 -56.51
281 2025-02-24 03:30:00 2025-02-24 05:30:00 long 2704.01 2723.00 0.9828 26.57 18.66 1.34 17.32
282 2025-02-24 05:30:00 2025-02-24 07:20:00 short 2723.00 2720.30 0.3329 9.07 0.90 0.45 0.45
283 2025-02-24 07:20:00 2025-02-24 08:20:00 long 2717.35 2690.31 1.9946 54.20 -54.20 0.00 -54.20
284 2025-02-24 08:20:00 2025-02-24 08:25:00 long 2690.91 2678.65 0.3157 8.50 -3.87 0.42 -4.29
285 2025-02-25 00:10:00 2025-02-25 07:05:00 long 2474.53 2432.73 3.4747 85.98 -145.23 4.23 -149.46
286 2025-02-26 01:15:00 2025-02-26 03:15:00 short 2485.63 2505.94 2.8479 70.79 -57.84 3.57 -61.41
287 2025-02-27 00:05:00 2025-02-27 01:05:00 short 2343.55 2317.15 0.2787 6.53 7.36 0.32 7.03
288 2025-02-27 01:05:00 2025-02-27 01:20:00 long 2317.15 2354.05 0.2848 6.60 10.51 0.34 10.17
289 2025-02-27 01:20:00 2025-02-27 03:25:00 short 2360.61 2329.18 2.8186 66.54 88.59 3.28 85.30
290 2025-02-27 03:25:00 2025-02-27 04:35:00 long 2325.72 2302.58 0.9679 22.51 -22.51 0.00 -22.51
291 2025-02-27 05:15:00 2025-02-27 07:35:00 short 2342.03 2365.33 0.9281 21.74 -21.74 0.00 -21.74
292 2025-02-27 07:35:00 2025-02-27 08:00:00 short 2365.83 2352.30 0.2981 7.05 4.03 0.35 3.68
293 2025-02-27 08:00:00 2025-02-27 09:05:00 long 2346.32 2358.48 1.8051 42.35 21.95 2.13 19.82
294 2025-02-27 09:05:00 2025-02-27 13:30:00 short 2365.58 2340.68 3.0516 72.19 76.00 3.57 72.43
295 2025-02-27 13:30:00 2025-02-27 13:45:00 long 2340.80 2359.11 1.0188 23.85 18.65 1.20 17.45
296 2025-02-27 13:45:00 2025-02-27 13:50:00 short 2359.11 2334.15 0.3439 8.11 8.58 0.40 8.18
297 2025-02-27 13:50:00 2025-02-27 14:30:00 long 2334.15 2350.31 0.3509 8.19 5.67 0.41 5.26
298 2025-02-27 14:30:00 2025-02-27 14:45:00 short 2349.54 2317.31 1.0521 24.72 33.90 1.22 32.68
299 2025-02-27 14:45:00 2025-02-27 15:00:00 long 2317.31 2294.26 0.3692 8.55 -8.55 0.00 -8.55
300 2025-02-27 16:15:00 2025-02-27 16:25:00 short 2334.15 2315.18 0.3627 8.46 6.88 0.42 6.46
301 2025-02-27 16:25:00 2025-02-27 18:00:00 long 2307.12 2324.01 2.2016 50.79 37.19 2.56 34.63
302 2025-02-27 18:00:00 2025-02-27 19:05:00 short 2328.70 2320.04 3.7833 88.10 32.76 4.39 28.37
303 2025-02-27 19:05:00 2025-02-27 19:30:00 long 2313.75 2290.73 2.3443 54.24 -54.24 0.00 -54.24
304 2025-02-27 19:30:00 2025-02-27 20:45:00 long 2271.11 2248.51 1.1205 25.45 -25.45 0.00 -25.45
305 2025-02-27 20:45:00 2025-02-27 23:35:00 long 2250.80 2307.13 3.7528 84.47 211.42 4.33 207.09
306 2025-02-27 23:35:00 2025-02-28 00:30:00 short 2307.13 2299.32 0.4455 10.28 3.48 0.51 2.97
307 2025-02-28 00:30:00 2025-02-28 01:25:00 long 2291.71 2259.98 2.8031 64.24 -88.93 3.17 -92.10

26
strategy/results/bb_full_grid_5m_20260226_214834.csv
View File

@@ -1,26 +0,0 @@
period,std,final_eq,ret_pct,n_trades,win_rate,sharpe,max_dd_u,max_dd_pct,stable_score
801,0.5,10.34628311811287,-94.82685844094357,11410,31.0692375109553,-0.8073996538916506,-208.98636227084955,104.49318113542478,-188.1101991959832
401,400.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
401,800.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
401,600.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
401,200.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
401,0.5,3.0098224997841756,-98.49508875010791,17114,34.983054808928365,-1.24562923011871,-197.42490102080006,98.71245051040003,-192.41259991985248
201,200.5,9.7202672056566,-95.1398663971717,24243,20.735882522790085,-0.805097998664356,-220.7396343409598,110.3698171704799,-193.0968961175279
201,400.5,9.7202672056566,-95.1398663971717,24243,20.735882522790085,-0.805097998664356,-220.7396343409598,110.3698171704799,-193.0968961175279
201,600.5,9.7202672056566,-95.1398663971717,24243,20.735882522790085,-0.805097998664356,-220.7396343409598,110.3698171704799,-193.0968961175279
201,800.5,9.7202672056566,-95.1398663971717,24243,20.735882522790085,-0.805097998664356,-220.7396343409598,110.3698171704799,-193.0968961175279
201,0.5,0.8960823320970013,-99.5519588339515,24082,38.36060127896354,-1.1894204188425357,-201.2616833266314,100.6308416633157,-194.3296771907145
1,200.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,0.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,800.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,600.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,400.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
601,0.5,3.1473924625026317,-98.42630376874868,13683,32.53672440254331,-0.749362829772547,-247.87957672599615,123.93978836299809,-206.57048841641773
601,200.5,3.0597660326602996,-98.47011698366985,13680,19.1812865497076,-0.18459283577348395,-553.0425949461219,276.52129747306094,-321.90226899140043
601,400.5,3.0597660326602996,-98.47011698366985,13680,19.1812865497076,-0.18459283577348395,-553.0425949461219,276.52129747306094,-321.90226899140043
601,600.5,3.0597660326602996,-98.47011698366985,13680,19.1812865497076,-0.18459283577348395,-553.0425949461219,276.52129747306094,-321.90226899140043
601,800.5,3.0597660326602996,-98.47011698366985,13680,19.1812865497076,-0.18459283577348395,-553.0425949461219,276.52129747306094,-321.90226899140043
801,200.5,118.75268396562913,-40.623658017185434,11418,19.451742862147487,-0.04049557086835689,-1080.0440346768748,540.0220173384374,-473.1272187383557
801,400.5,118.75268396562913,-40.623658017185434,11418,19.451742862147487,-0.04049557086835689,-1080.0440346768748,540.0220173384374,-473.1272187383557
801,600.5,118.75268396562913,-40.623658017185434,11418,19.451742862147487,-0.04049557086835689,-1080.0440346768748,540.0220173384374,-473.1272187383557
801,800.5,118.75268396562913,-40.623658017185434,11418,19.451742862147487,-0.04049557086835689,-1080.0440346768748,540.0220173384374,-473.1272187383557
1 period std final_eq ret_pct n_trades win_rate sharpe max_dd_u max_dd_pct stable_score
2 801 0.5 10.34628311811287 -94.82685844094357 11410 31.0692375109553 -0.8073996538916506 -208.98636227084955 104.49318113542478 -188.1101991959832
3 401 400.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
4 401 800.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
5 401 600.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
6 401 200.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
7 401 0.5 3.0098224997841756 -98.49508875010791 17114 34.983054808928365 -1.24562923011871 -197.42490102080006 98.71245051040003 -192.41259991985248
8 201 200.5 9.7202672056566 -95.1398663971717 24243 20.735882522790085 -0.805097998664356 -220.7396343409598 110.3698171704799 -193.0968961175279
9 201 400.5 9.7202672056566 -95.1398663971717 24243 20.735882522790085 -0.805097998664356 -220.7396343409598 110.3698171704799 -193.0968961175279
10 201 600.5 9.7202672056566 -95.1398663971717 24243 20.735882522790085 -0.805097998664356 -220.7396343409598 110.3698171704799 -193.0968961175279
11 201 800.5 9.7202672056566 -95.1398663971717 24243 20.735882522790085 -0.805097998664356 -220.7396343409598 110.3698171704799 -193.0968961175279
12 201 0.5 0.8960823320970013 -99.5519588339515 24082 38.36060127896354 -1.1894204188425357 -201.2616833266314 100.6308416633157 -194.3296771907145
13 1 200.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
14 1 0.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
15 1 800.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
16 1 600.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
17 1 400.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
18 601 0.5 3.1473924625026317 -98.42630376874868 13683 32.53672440254331 -0.749362829772547 -247.87957672599615 123.93978836299809 -206.57048841641773
19 601 200.5 3.0597660326602996 -98.47011698366985 13680 19.1812865497076 -0.18459283577348395 -553.0425949461219 276.52129747306094 -321.90226899140043
20 601 400.5 3.0597660326602996 -98.47011698366985 13680 19.1812865497076 -0.18459283577348395 -553.0425949461219 276.52129747306094 -321.90226899140043
21 601 600.5 3.0597660326602996 -98.47011698366985 13680 19.1812865497076 -0.18459283577348395 -553.0425949461219 276.52129747306094 -321.90226899140043
22 601 800.5 3.0597660326602996 -98.47011698366985 13680 19.1812865497076 -0.18459283577348395 -553.0425949461219 276.52129747306094 -321.90226899140043
23 801 200.5 118.75268396562913 -40.623658017185434 11418 19.451742862147487 -0.04049557086835689 -1080.0440346768748 540.0220173384374 -473.1272187383557
24 801 400.5 118.75268396562913 -40.623658017185434 11418 19.451742862147487 -0.04049557086835689 -1080.0440346768748 540.0220173384374 -473.1272187383557
25 801 600.5 118.75268396562913 -40.623658017185434 11418 19.451742862147487 -0.04049557086835689 -1080.0440346768748 540.0220173384374 -473.1272187383557
26 801 800.5 118.75268396562913 -40.623658017185434 11418 19.451742862147487 -0.04049557086835689 -1080.0440346768748 540.0220173384374 -473.1272187383557

26
strategy/results/bb_full_grid_5m_20260226_215016.csv
View File

@@ -1,26 +0,0 @@
period,std,final_eq,ret_pct,n_trades,win_rate,sharpe,max_dd_u,max_dd_pct,stable_score
801,0.5,181.43296931677935,-9.283515341610325,4050,30.0,-0.09570947529404288,-101.50183051832926,50.75091525916463,-51.03276125247054
601,0.5,93.62899070862777,-53.18550464568612,4692,31.60699062233589,-0.5815001553833595,-197.11225415652723,98.55612707826361,-139.00840817289733
401,0.5,62.32779858408545,-68.83610070795727,5759,33.79058864386178,-1.4213594190051202,-161.96245274664543,80.98122637332271,-150.67739483467687
201,0.5,89.02955479967403,-55.485222600162984,8469,35.48234738457906,-0.5773758988733515,-241.43620122699747,120.71810061349872,-158.98821387744218
401,400.5,44.38579511338957,-77.80710244330521,5793,17.262213015708614,-0.6994195729006223,-253.93642559214132,126.96821279607067,-187.7747075549692
401,200.5,44.38579511338957,-77.80710244330521,5793,17.262213015708614,-0.6994195729006223,-253.93642559214132,126.96821279607067,-187.7747075549692
401,800.5,44.38579511338957,-77.80710244330521,5793,17.262213015708614,-0.6994195729006223,-253.93642559214132,126.96821279607067,-187.7747075549692
401,600.5,44.38579511338957,-77.80710244330521,5793,17.262213015708614,-0.6994195729006223,-253.93642559214132,126.96821279607067,-187.7747075549692
801,600.5,218.96542562281235,9.482712811406174,4053,17.66592647421663,0.03389213813921764,-506.2632850511136,253.1316425255568,-192.61589555136865
801,400.5,218.96542562281235,9.482712811406174,4053,17.66592647421663,0.03389213813921764,-506.2632850511136,253.1316425255568,-192.61589555136865
801,200.5,218.96542562281235,9.482712811406174,4053,17.66592647421663,0.03389213813921764,-506.2632850511136,253.1316425255568,-192.61589555136865
801,800.5,218.96542562281235,9.482712811406174,4053,17.66592647421663,0.03389213813921764,-506.2632850511136,253.1316425255568,-192.61589555136865
1,200.5,0.0006825550434322981,-99.99965872247829,104946,30.22220951727555,-2.7404631170214495,-228.6355754207877,114.31778771039384,-224.33944629505078
1,800.5,0.0006825550434322981,-99.99965872247829,104946,30.22220951727555,-2.7404631170214495,-228.6355754207877,114.31778771039384,-224.33944629505078
1,600.5,0.0006825550434322981,-99.99965872247829,104946,30.22220951727555,-2.7404631170214495,-228.6355754207877,114.31778771039384,-224.33944629505078
1,400.5,0.0006825550434322981,-99.99965872247829,104946,30.22220951727555,-2.7404631170214495,-228.6355754207877,114.31778771039384,-224.33944629505078
1,0.5,0.0006825550434322981,-99.99965872247829,104946,30.22220951727555,-2.7404631170214495,-228.6355754207877,114.31778771039384,-224.33944629505078
601,200.5,37.28309984246756,-81.35845007876623,4691,17.11788531230015,-0.5168501866457095,-401.54175143780486,200.77087571890243,-248.1773528936367
601,400.5,37.28309984246756,-81.35845007876623,4691,17.11788531230015,-0.5168501866457095,-401.54175143780486,200.77087571890243,-248.1773528936367
601,600.5,37.28309984246756,-81.35845007876623,4691,17.11788531230015,-0.5168501866457095,-401.54175143780486,200.77087571890243,-248.1773528936367
601,800.5,37.28309984246756,-81.35845007876623,4691,17.11788531230015,-0.5168501866457095,-401.54175143780486,200.77087571890243,-248.1773528936367
201,800.5,199.9590164846017,-0.020491757699147684,8549,18.306234647327173,-5.261669526742126e-05,-683.826528940572,341.913264470286,-273.5517347342712
201,600.5,199.9590164846017,-0.020491757699147684,8549,18.306234647327173,-5.261669526742126e-05,-683.826528940572,341.913264470286,-273.5517347342712
201,400.5,199.9590164846017,-0.020491757699147684,8549,18.306234647327173,-5.261669526742126e-05,-683.826528940572,341.913264470286,-273.5517347342712
201,200.5,199.9590164846017,-0.020491757699147684,8549,18.306234647327173,-5.261669526742126e-05,-683.826528940572,341.913264470286,-273.5517347342712
1 period std final_eq ret_pct n_trades win_rate sharpe max_dd_u max_dd_pct stable_score
2 801 0.5 181.43296931677935 -9.283515341610325 4050 30.0 -0.09570947529404288 -101.50183051832926 50.75091525916463 -51.03276125247054
3 601 0.5 93.62899070862777 -53.18550464568612 4692 31.60699062233589 -0.5815001553833595 -197.11225415652723 98.55612707826361 -139.00840817289733
4 401 0.5 62.32779858408545 -68.83610070795727 5759 33.79058864386178 -1.4213594190051202 -161.96245274664543 80.98122637332271 -150.67739483467687
5 201 0.5 89.02955479967403 -55.485222600162984 8469 35.48234738457906 -0.5773758988733515 -241.43620122699747 120.71810061349872 -158.98821387744218
6 401 400.5 44.38579511338957 -77.80710244330521 5793 17.262213015708614 -0.6994195729006223 -253.93642559214132 126.96821279607067 -187.7747075549692
7 401 200.5 44.38579511338957 -77.80710244330521 5793 17.262213015708614 -0.6994195729006223 -253.93642559214132 126.96821279607067 -187.7747075549692
8 401 800.5 44.38579511338957 -77.80710244330521 5793 17.262213015708614 -0.6994195729006223 -253.93642559214132 126.96821279607067 -187.7747075549692
9 401 600.5 44.38579511338957 -77.80710244330521 5793 17.262213015708614 -0.6994195729006223 -253.93642559214132 126.96821279607067 -187.7747075549692
10 801 600.5 218.96542562281235 9.482712811406174 4053 17.66592647421663 0.03389213813921764 -506.2632850511136 253.1316425255568 -192.61589555136865
11 801 400.5 218.96542562281235 9.482712811406174 4053 17.66592647421663 0.03389213813921764 -506.2632850511136 253.1316425255568 -192.61589555136865
12 801 200.5 218.96542562281235 9.482712811406174 4053 17.66592647421663 0.03389213813921764 -506.2632850511136 253.1316425255568 -192.61589555136865
13 801 800.5 218.96542562281235 9.482712811406174 4053 17.66592647421663 0.03389213813921764 -506.2632850511136 253.1316425255568 -192.61589555136865
14 1 200.5 0.0006825550434322981 -99.99965872247829 104946 30.22220951727555 -2.7404631170214495 -228.6355754207877 114.31778771039384 -224.33944629505078
15 1 800.5 0.0006825550434322981 -99.99965872247829 104946 30.22220951727555 -2.7404631170214495 -228.6355754207877 114.31778771039384 -224.33944629505078
16 1 600.5 0.0006825550434322981 -99.99965872247829 104946 30.22220951727555 -2.7404631170214495 -228.6355754207877 114.31778771039384 -224.33944629505078
17 1 400.5 0.0006825550434322981 -99.99965872247829 104946 30.22220951727555 -2.7404631170214495 -228.6355754207877 114.31778771039384 -224.33944629505078
18 1 0.5 0.0006825550434322981 -99.99965872247829 104946 30.22220951727555 -2.7404631170214495 -228.6355754207877 114.31778771039384 -224.33944629505078
19 601 200.5 37.28309984246756 -81.35845007876623 4691 17.11788531230015 -0.5168501866457095 -401.54175143780486 200.77087571890243 -248.1773528936367
20 601 400.5 37.28309984246756 -81.35845007876623 4691 17.11788531230015 -0.5168501866457095 -401.54175143780486 200.77087571890243 -248.1773528936367
21 601 600.5 37.28309984246756 -81.35845007876623 4691 17.11788531230015 -0.5168501866457095 -401.54175143780486 200.77087571890243 -248.1773528936367
22 601 800.5 37.28309984246756 -81.35845007876623 4691 17.11788531230015 -0.5168501866457095 -401.54175143780486 200.77087571890243 -248.1773528936367
23 201 800.5 199.9590164846017 -0.020491757699147684 8549 18.306234647327173 -5.261669526742126e-05 -683.826528940572 341.913264470286 -273.5517347342712
24 201 600.5 199.9590164846017 -0.020491757699147684 8549 18.306234647327173 -5.261669526742126e-05 -683.826528940572 341.913264470286 -273.5517347342712
25 201 400.5 199.9590164846017 -0.020491757699147684 8549 18.306234647327173 -5.261669526742126e-05 -683.826528940572 341.913264470286 -273.5517347342712
26 201 200.5 199.9590164846017 -0.020491757699147684 8549 18.306234647327173 -5.261669526742126e-05 -683.826528940572 341.913264470286 -273.5517347342712

101
strategy/results/bb_full_grid_5m_20260226_220149.csv
View File

@@ -1,101 +0,0 @@
period,std,final_eq,ret_pct,n_trades,win_rate,sharpe,max_dd_u,max_dd_pct,stable_score
301,800.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,900.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,400.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,300.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,200.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,100.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,700.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,600.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,500.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
901,0.5,13.45175555605595,-93.27412222197202,10264,30.670303975058456,-0.8952537525217249,-189.5983790965677,94.79918954828385,-179.8565188908598
801,0.5,10.34628311811287,-94.82685844094357,11410,31.0692375109553,-0.8073996538916506,-208.98636227084955,104.49318113542478,-188.1101991959832
501,0.5,1.8986708832397656,-99.05066455838012,15372,33.36586000520427,-0.8822266044169348,-198.31333975387093,99.15666987693547,-188.96271971293172
701,0.5,1.6035402845204254,-99.19822985773979,12485,31.229475370444533,-0.8874406539711374,-198.89129268300212,99.44564634150106,-189.40403477859428
101,0.5,2.9344181713149435,-98.53279091434253,35484,40.70003381805884,-1.0207595955600905,-200.5343407319586,100.2671703659793,-190.99564235384705
401,700.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
401,900.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
401,800.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
401,600.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
401,100.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
401,500.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
401,400.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
401,200.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
401,300.5,6.006954718238241,-96.99652264088088,17153,19.413513671077943,-0.554621338370883,-218.38418033295903,109.19209016647953,-191.0056508345151
401,0.5,3.0098224997841756,-98.49508875010791,17114,34.983054808928365,-1.24562923011871,-197.42490102080006,98.71245051040003,-192.41259991985248
201,900.5,9.7202672056566,-95.1398663971717,24243,20.735882522790085,-0.805097998664356,-220.7396343409598,110.3698171704799,-193.0968961175279
201,800.5,9.7202672056566,-95.1398663971717,24243,20.735882522790085,-0.805097998664356,-220.7396343409598,110.3698171704799,-193.0968961175279
201,700.5,9.7202672056566,-95.1398663971717,24243,20.735882522790085,-0.805097998664356,-220.7396343409598,110.3698171704799,-193.0968961175279
201,600.5,9.7202672056566,-95.1398663971717,24243,20.735882522790085,-0.805097998664356,-220.7396343409598,110.3698171704799,-193.0968961175279
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201,400.5,9.7202672056566,-95.1398663971717,24243,20.735882522790085,-0.805097998664356,-220.7396343409598,110.3698171704799,-193.0968961175279
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201,100.5,9.7202672056566,-95.1398663971717,24243,20.735882522790085,-0.805097998664356,-220.7396343409598,110.3698171704799,-193.0968961175279
201,200.5,9.7202672056566,-95.1398663971717,24243,20.735882522790085,-0.805097998664356,-220.7396343409598,110.3698171704799,-193.0968961175279
301,0.5,3.288758436016597,-98.3556207819917,18999,36.78614663929681,-0.9796482828217248,-210.34848032205863,105.17424016102932,-194.25079230467585
201,0.5,0.8960823320970013,-99.5519588339515,24082,38.36060127896354,-1.1894204188425357,-201.2616833266314,100.6308416633157,-194.3296771907145
1,100.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,0.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,900.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,600.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,400.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,300.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,200.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,500.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,800.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,700.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
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601,600.5,3.0597660326602996,-98.47011698366985,13680,19.1812865497076,-0.18459283577348395,-553.0425949461219,276.52129747306094,-321.90226899140043
601,400.5,3.0597660326602996,-98.47011698366985,13680,19.1812865497076,-0.18459283577348395,-553.0425949461219,276.52129747306094,-321.90226899140043
601,300.5,3.0597660326602996,-98.47011698366985,13680,19.1812865497076,-0.18459283577348395,-553.0425949461219,276.52129747306094,-321.90226899140043
601,500.5,3.0597660326602996,-98.47011698366985,13680,19.1812865497076,-0.18459283577348395,-553.0425949461219,276.52129747306094,-321.90226899140043
601,100.5,3.0597660326602996,-98.47011698366985,13680,19.1812865497076,-0.18459283577348395,-553.0425949461219,276.52129747306094,-321.90226899140043
601,200.5,3.0597660326602996,-98.47011698366985,13680,19.1812865497076,-0.18459283577348395,-553.0425949461219,276.52129747306094,-321.90226899140043
701,700.5,14.503672746728883,-92.74816362663556,12501,19.598432125429966,-0.15690585751773198,-643.6658567532712,321.8329283766356,-352.0973766181569
701,900.5,14.503672746728883,-92.74816362663556,12501,19.598432125429966,-0.15690585751773198,-643.6658567532712,321.8329283766356,-352.0973766181569
701,800.5,14.503672746728883,-92.74816362663556,12501,19.598432125429966,-0.15690585751773198,-643.6658567532712,321.8329283766356,-352.0973766181569
701,600.5,14.503672746728883,-92.74816362663556,12501,19.598432125429966,-0.15690585751773198,-643.6658567532712,321.8329283766356,-352.0973766181569
701,500.5,14.503672746728883,-92.74816362663556,12501,19.598432125429966,-0.15690585751773198,-643.6658567532712,321.8329283766356,-352.0973766181569
701,400.5,14.503672746728883,-92.74816362663556,12501,19.598432125429966,-0.15690585751773198,-643.6658567532712,321.8329283766356,-352.0973766181569
701,300.5,14.503672746728883,-92.74816362663556,12501,19.598432125429966,-0.15690585751773198,-643.6658567532712,321.8329283766356,-352.0973766181569
701,200.5,14.503672746728883,-92.74816362663556,12501,19.598432125429966,-0.15690585751773198,-643.6658567532712,321.8329283766356,-352.0973766181569
701,100.5,14.503672746728883,-92.74816362663556,12501,19.598432125429966,-0.15690585751773198,-643.6658567532712,321.8329283766356,-352.0973766181569
801,600.5,118.75268396562913,-40.623658017185434,11418,19.451742862147487,-0.04049557086835689,-1080.0440346768748,540.0220173384374,-473.1272187383557
801,900.5,118.75268396562913,-40.623658017185434,11418,19.451742862147487,-0.04049557086835689,-1080.0440346768748,540.0220173384374,-473.1272187383557
801,800.5,118.75268396562913,-40.623658017185434,11418,19.451742862147487,-0.04049557086835689,-1080.0440346768748,540.0220173384374,-473.1272187383557
801,700.5,118.75268396562913,-40.623658017185434,11418,19.451742862147487,-0.04049557086835689,-1080.0440346768748,540.0220173384374,-473.1272187383557
801,300.5,118.75268396562913,-40.623658017185434,11418,19.451742862147487,-0.04049557086835689,-1080.0440346768748,540.0220173384374,-473.1272187383557
801,500.5,118.75268396562913,-40.623658017185434,11418,19.451742862147487,-0.04049557086835689,-1080.0440346768748,540.0220173384374,-473.1272187383557
801,400.5,118.75268396562913,-40.623658017185434,11418,19.451742862147487,-0.04049557086835689,-1080.0440346768748,540.0220173384374,-473.1272187383557
801,200.5,118.75268396562913,-40.623658017185434,11418,19.451742862147487,-0.04049557086835689,-1080.0440346768748,540.0220173384374,-473.1272187383557
801,100.5,118.75268396562913,-40.623658017185434,11418,19.451742862147487,-0.04049557086835689,-1080.0440346768748,540.0220173384374,-473.1272187383557
901,100.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,200.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,300.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,400.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,500.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,600.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,700.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,800.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,900.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
1 period std final_eq ret_pct n_trades win_rate sharpe max_dd_u max_dd_pct stable_score
2 301 800.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
3 301 900.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
4 301 400.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
5 301 300.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
6 301 200.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
7 301 100.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
8 301 700.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
9 301 600.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
10 301 500.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
11 901 0.5 13.45175555605595 -93.27412222197202 10264 30.670303975058456 -0.8952537525217249 -189.5983790965677 94.79918954828385 -179.8565188908598
12 801 0.5 10.34628311811287 -94.82685844094357 11410 31.0692375109553 -0.8073996538916506 -208.98636227084955 104.49318113542478 -188.1101991959832
13 501 0.5 1.8986708832397656 -99.05066455838012 15372 33.36586000520427 -0.8822266044169348 -198.31333975387093 99.15666987693547 -188.96271971293172
14 701 0.5 1.6035402845204254 -99.19822985773979 12485 31.229475370444533 -0.8874406539711374 -198.89129268300212 99.44564634150106 -189.40403477859428
15 101 0.5 2.9344181713149435 -98.53279091434253 35484 40.70003381805884 -1.0207595955600905 -200.5343407319586 100.2671703659793 -190.99564235384705
16 401 700.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
17 401 900.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
18 401 800.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
19 401 600.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
20 401 100.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
21 401 500.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
22 401 400.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
23 401 200.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
24 401 300.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
25 401 0.5 3.0098224997841756 -98.49508875010791 17114 34.983054808928365 -1.24562923011871 -197.42490102080006 98.71245051040003 -192.41259991985248
26 201 900.5 9.7202672056566 -95.1398663971717 24243 20.735882522790085 -0.805097998664356 -220.7396343409598 110.3698171704799 -193.0968961175279
27 201 800.5 9.7202672056566 -95.1398663971717 24243 20.735882522790085 -0.805097998664356 -220.7396343409598 110.3698171704799 -193.0968961175279
28 201 700.5 9.7202672056566 -95.1398663971717 24243 20.735882522790085 -0.805097998664356 -220.7396343409598 110.3698171704799 -193.0968961175279
29 201 600.5 9.7202672056566 -95.1398663971717 24243 20.735882522790085 -0.805097998664356 -220.7396343409598 110.3698171704799 -193.0968961175279
30 201 500.5 9.7202672056566 -95.1398663971717 24243 20.735882522790085 -0.805097998664356 -220.7396343409598 110.3698171704799 -193.0968961175279
31 201 400.5 9.7202672056566 -95.1398663971717 24243 20.735882522790085 -0.805097998664356 -220.7396343409598 110.3698171704799 -193.0968961175279
32 201 300.5 9.7202672056566 -95.1398663971717 24243 20.735882522790085 -0.805097998664356 -220.7396343409598 110.3698171704799 -193.0968961175279
33 201 100.5 9.7202672056566 -95.1398663971717 24243 20.735882522790085 -0.805097998664356 -220.7396343409598 110.3698171704799 -193.0968961175279
34 201 200.5 9.7202672056566 -95.1398663971717 24243 20.735882522790085 -0.805097998664356 -220.7396343409598 110.3698171704799 -193.0968961175279
35 301 0.5 3.288758436016597 -98.3556207819917 18999 36.78614663929681 -0.9796482828217248 -210.34848032205863 105.17424016102932 -194.25079230467585
36 201 0.5 0.8960823320970013 -99.5519588339515 24082 38.36060127896354 -1.1894204188425357 -201.2616833266314 100.6308416633157 -194.3296771907145
37 1 100.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
38 1 0.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
39 1 900.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
40 1 600.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
41 1 400.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
42 1 300.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
43 1 200.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
44 1 500.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
45 1 800.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
46 1 700.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
47 601 0.5 3.1473924625026317 -98.42630376874868 13683 32.53672440254331 -0.749362829772547 -247.87957672599615 123.93978836299809 -206.57048841641773
48 501 300.5 2.0680528205830533 -98.96597358970847 15381 19.29003315779208 -0.5182752037530948 -256.6618012939444 128.3309006469722 -207.84999655232338
49 501 200.5 2.0680528205830533 -98.96597358970847 15381 19.29003315779208 -0.5182752037530948 -256.6618012939444 128.3309006469722 -207.84999655232338
50 501 600.5 2.0680528205830533 -98.96597358970847 15381 19.29003315779208 -0.5182752037530948 -256.6618012939444 128.3309006469722 -207.84999655232338
51 501 100.5 2.0680528205830533 -98.96597358970847 15381 19.29003315779208 -0.5182752037530948 -256.6618012939444 128.3309006469722 -207.84999655232338
52 501 500.5 2.0680528205830533 -98.96597358970847 15381 19.29003315779208 -0.5182752037530948 -256.6618012939444 128.3309006469722 -207.84999655232338
53 501 700.5 2.0680528205830533 -98.96597358970847 15381 19.29003315779208 -0.5182752037530948 -256.6618012939444 128.3309006469722 -207.84999655232338
54 501 800.5 2.0680528205830533 -98.96597358970847 15381 19.29003315779208 -0.5182752037530948 -256.6618012939444 128.3309006469722 -207.84999655232338
55 501 900.5 2.0680528205830533 -98.96597358970847 15381 19.29003315779208 -0.5182752037530948 -256.6618012939444 128.3309006469722 -207.84999655232338
56 501 400.5 2.0680528205830533 -98.96597358970847 15381 19.29003315779208 -0.5182752037530948 -256.6618012939444 128.3309006469722 -207.84999655232338
57 101 900.5 2.46021630053702 -98.7698918497315 36119 21.254741271906752 -0.4895899161479696 -271.6273063072573 135.81365315362865 -213.29589336641004
58 101 100.5 2.46021630053702 -98.7698918497315 36119 21.254741271906752 -0.4895899161479696 -271.6273063072573 135.81365315362865 -213.29589336641004
59 101 200.5 2.46021630053702 -98.7698918497315 36119 21.254741271906752 -0.4895899161479696 -271.6273063072573 135.81365315362865 -213.29589336641004
60 101 300.5 2.46021630053702 -98.7698918497315 36119 21.254741271906752 -0.4895899161479696 -271.6273063072573 135.81365315362865 -213.29589336641004
61 101 400.5 2.46021630053702 -98.7698918497315 36119 21.254741271906752 -0.4895899161479696 -271.6273063072573 135.81365315362865 -213.29589336641004
62 101 500.5 2.46021630053702 -98.7698918497315 36119 21.254741271906752 -0.4895899161479696 -271.6273063072573 135.81365315362865 -213.29589336641004
63 101 600.5 2.46021630053702 -98.7698918497315 36119 21.254741271906752 -0.4895899161479696 -271.6273063072573 135.81365315362865 -213.29589336641004
64 101 700.5 2.46021630053702 -98.7698918497315 36119 21.254741271906752 -0.4895899161479696 -271.6273063072573 135.81365315362865 -213.29589336641004
65 101 800.5 2.46021630053702 -98.7698918497315 36119 21.254741271906752 -0.4895899161479696 -271.6273063072573 135.81365315362865 -213.29589336641004
66 601 700.5 3.0597660326602996 -98.47011698366985 13680 19.1812865497076 -0.18459283577348395 -553.0425949461219 276.52129747306094 -321.90226899140043
67 601 900.5 3.0597660326602996 -98.47011698366985 13680 19.1812865497076 -0.18459283577348395 -553.0425949461219 276.52129747306094 -321.90226899140043
68 601 800.5 3.0597660326602996 -98.47011698366985 13680 19.1812865497076 -0.18459283577348395 -553.0425949461219 276.52129747306094 -321.90226899140043
69 601 600.5 3.0597660326602996 -98.47011698366985 13680 19.1812865497076 -0.18459283577348395 -553.0425949461219 276.52129747306094 -321.90226899140043
70 601 400.5 3.0597660326602996 -98.47011698366985 13680 19.1812865497076 -0.18459283577348395 -553.0425949461219 276.52129747306094 -321.90226899140043
71 601 300.5 3.0597660326602996 -98.47011698366985 13680 19.1812865497076 -0.18459283577348395 -553.0425949461219 276.52129747306094 -321.90226899140043
72 601 500.5 3.0597660326602996 -98.47011698366985 13680 19.1812865497076 -0.18459283577348395 -553.0425949461219 276.52129747306094 -321.90226899140043
73 601 100.5 3.0597660326602996 -98.47011698366985 13680 19.1812865497076 -0.18459283577348395 -553.0425949461219 276.52129747306094 -321.90226899140043
74 601 200.5 3.0597660326602996 -98.47011698366985 13680 19.1812865497076 -0.18459283577348395 -553.0425949461219 276.52129747306094 -321.90226899140043
75 701 700.5 14.503672746728883 -92.74816362663556 12501 19.598432125429966 -0.15690585751773198 -643.6658567532712 321.8329283766356 -352.0973766181569
76 701 900.5 14.503672746728883 -92.74816362663556 12501 19.598432125429966 -0.15690585751773198 -643.6658567532712 321.8329283766356 -352.0973766181569
77 701 800.5 14.503672746728883 -92.74816362663556 12501 19.598432125429966 -0.15690585751773198 -643.6658567532712 321.8329283766356 -352.0973766181569
78 701 600.5 14.503672746728883 -92.74816362663556 12501 19.598432125429966 -0.15690585751773198 -643.6658567532712 321.8329283766356 -352.0973766181569
79 701 500.5 14.503672746728883 -92.74816362663556 12501 19.598432125429966 -0.15690585751773198 -643.6658567532712 321.8329283766356 -352.0973766181569
80 701 400.5 14.503672746728883 -92.74816362663556 12501 19.598432125429966 -0.15690585751773198 -643.6658567532712 321.8329283766356 -352.0973766181569
81 701 300.5 14.503672746728883 -92.74816362663556 12501 19.598432125429966 -0.15690585751773198 -643.6658567532712 321.8329283766356 -352.0973766181569
82 701 200.5 14.503672746728883 -92.74816362663556 12501 19.598432125429966 -0.15690585751773198 -643.6658567532712 321.8329283766356 -352.0973766181569
83 701 100.5 14.503672746728883 -92.74816362663556 12501 19.598432125429966 -0.15690585751773198 -643.6658567532712 321.8329283766356 -352.0973766181569
84 801 600.5 118.75268396562913 -40.623658017185434 11418 19.451742862147487 -0.04049557086835689 -1080.0440346768748 540.0220173384374 -473.1272187383557
85 801 900.5 118.75268396562913 -40.623658017185434 11418 19.451742862147487 -0.04049557086835689 -1080.0440346768748 540.0220173384374 -473.1272187383557
86 801 800.5 118.75268396562913 -40.623658017185434 11418 19.451742862147487 -0.04049557086835689 -1080.0440346768748 540.0220173384374 -473.1272187383557
87 801 700.5 118.75268396562913 -40.623658017185434 11418 19.451742862147487 -0.04049557086835689 -1080.0440346768748 540.0220173384374 -473.1272187383557
88 801 300.5 118.75268396562913 -40.623658017185434 11418 19.451742862147487 -0.04049557086835689 -1080.0440346768748 540.0220173384374 -473.1272187383557
89 801 500.5 118.75268396562913 -40.623658017185434 11418 19.451742862147487 -0.04049557086835689 -1080.0440346768748 540.0220173384374 -473.1272187383557
90 801 400.5 118.75268396562913 -40.623658017185434 11418 19.451742862147487 -0.04049557086835689 -1080.0440346768748 540.0220173384374 -473.1272187383557
91 801 200.5 118.75268396562913 -40.623658017185434 11418 19.451742862147487 -0.04049557086835689 -1080.0440346768748 540.0220173384374 -473.1272187383557
92 801 100.5 118.75268396562913 -40.623658017185434 11418 19.451742862147487 -0.04049557086835689 -1080.0440346768748 540.0220173384374 -473.1272187383557
93 901 100.5 563.7908687274055 181.89543436370275 10270 19.561830574488802 0.09336862353854584 -1726.9726968393975 863.4863484196987 -507.77322088959374
94 901 200.5 563.7908687274055 181.89543436370275 10270 19.561830574488802 0.09336862353854584 -1726.9726968393975 863.4863484196987 -507.77322088959374
95 901 300.5 563.7908687274055 181.89543436370275 10270 19.561830574488802 0.09336862353854584 -1726.9726968393975 863.4863484196987 -507.77322088959374
96 901 400.5 563.7908687274055 181.89543436370275 10270 19.561830574488802 0.09336862353854584 -1726.9726968393975 863.4863484196987 -507.77322088959374
97 901 500.5 563.7908687274055 181.89543436370275 10270 19.561830574488802 0.09336862353854584 -1726.9726968393975 863.4863484196987 -507.77322088959374
98 901 600.5 563.7908687274055 181.89543436370275 10270 19.561830574488802 0.09336862353854584 -1726.9726968393975 863.4863484196987 -507.77322088959374
99 901 700.5 563.7908687274055 181.89543436370275 10270 19.561830574488802 0.09336862353854584 -1726.9726968393975 863.4863484196987 -507.77322088959374
100 901 800.5 563.7908687274055 181.89543436370275 10270 19.561830574488802 0.09336862353854584 -1726.9726968393975 863.4863484196987 -507.77322088959374
101 901 900.5 563.7908687274055 181.89543436370275 10270 19.561830574488802 0.09336862353854584 -1726.9726968393975 863.4863484196987 -507.77322088959374

17
strategy/results/bb_full_grid_5m_20260226_220319.csv
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@@ -1,17 +0,0 @@
period,std,final_eq,ret_pct,n_trades,win_rate,sharpe,max_dd_u,max_dd_pct,stable_score
751,250.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,500.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,750.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
501,0.5,1.8986708832397656,-99.05066455838012,15372,33.36586000520427,-0.8822266044169348,-198.31333975387093,99.15666987693547,-188.96271971293172
751,0.5,6.787536497792063,-96.60623175110396,11769,31.701928795989463,-0.8973785204314527,-208.02359739762167,104.01179869881084,-190.58421295533006
251,0.5,1.2582263129595197,-99.37088684352024,21094,37.47985209064189,-1.1405413234039807,-199.63192120071835,99.81596060035918,-192.91015120465536
251,250.5,11.523950753177985,-94.23802462341101,21200,20.42924528301887,-0.4469101538511351,-243.2936639641133,121.64683198205665,-196.91841205526995
251,500.5,11.523950753177985,-94.23802462341101,21200,20.42924528301887,-0.4469101538511351,-243.2936639641133,121.64683198205665,-196.91841205526995
251,750.5,11.523950753177985,-94.23802462341101,21200,20.42924528301887,-0.4469101538511351,-243.2936639641133,121.64683198205665,-196.91841205526995
1,0.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,250.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,500.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,750.5,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
501,250.5,2.0680528205830533,-98.96597358970847,15381,19.29003315779208,-0.5182752037530948,-256.6618012939444,128.3309006469722,-207.84999655232338
501,500.5,2.0680528205830533,-98.96597358970847,15381,19.29003315779208,-0.5182752037530948,-256.6618012939444,128.3309006469722,-207.84999655232338
501,750.5,2.0680528205830533,-98.96597358970847,15381,19.29003315779208,-0.5182752037530948,-256.6618012939444,128.3309006469722,-207.84999655232338
1 period std final_eq ret_pct n_trades win_rate sharpe max_dd_u max_dd_pct stable_score
2 751 250.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
3 751 500.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
4 751 750.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
5 501 0.5 1.8986708832397656 -99.05066455838012 15372 33.36586000520427 -0.8822266044169348 -198.31333975387093 99.15666987693547 -188.96271971293172
6 751 0.5 6.787536497792063 -96.60623175110396 11769 31.701928795989463 -0.8973785204314527 -208.02359739762167 104.01179869881084 -190.58421295533006
7 251 0.5 1.2582263129595197 -99.37088684352024 21094 37.47985209064189 -1.1405413234039807 -199.63192120071835 99.81596060035918 -192.91015120465536
8 251 250.5 11.523950753177985 -94.23802462341101 21200 20.42924528301887 -0.4469101538511351 -243.2936639641133 121.64683198205665 -196.91841205526995
9 251 500.5 11.523950753177985 -94.23802462341101 21200 20.42924528301887 -0.4469101538511351 -243.2936639641133 121.64683198205665 -196.91841205526995
10 251 750.5 11.523950753177985 -94.23802462341101 21200 20.42924528301887 -0.4469101538511351 -243.2936639641133 121.64683198205665 -196.91841205526995
11 1 0.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
12 1 250.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
13 1 500.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
14 1 750.5 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
15 501 250.5 2.0680528205830533 -98.96597358970847 15381 19.29003315779208 -0.5182752037530948 -256.6618012939444 128.3309006469722 -207.84999655232338
16 501 500.5 2.0680528205830533 -98.96597358970847 15381 19.29003315779208 -0.5182752037530948 -256.6618012939444 128.3309006469722 -207.84999655232338
17 501 750.5 2.0680528205830533 -98.96597358970847 15381 19.29003315779208 -0.5182752037530948 -256.6618012939444 128.3309006469722 -207.84999655232338

401
strategy/results/bb_full_grid_5m_20260226_222227.csv
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@@ -1,401 +0,0 @@
period,std,final_eq,ret_pct,n_trades,win_rate,sharpe,max_dd_u,max_dd_pct,stable_score
301,100.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,600.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,300.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,350.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,400.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,450.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,500.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,550.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,650.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,200.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,750.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,800.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,850.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,900.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,950.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,50.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,250.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,700.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
301,150.5,52.83984459402632,-73.58007770298684,19058,20.495330045125407,-0.3757296908035501,-226.550345134987,113.27517256749351,-168.70897204662427
751,350.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,800.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,300.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,150.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,100.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,50.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,950.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,900.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,250.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,850.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,750.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,700.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,650.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,600.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,550.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,500.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,450.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,400.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
751,200.5,127.53055242971492,-36.23472378514254,11780,19.898132427843805,-0.1076378009333422,-345.62059572323346,172.81029786161673,-175.77461568563604
901,0.5,13.45175555605595,-93.27412222197202,10264,30.670303975058456,-0.8952537525217249,-189.5983790965677,94.79918954828385,-179.8565188908598
851,0.5,13.139093314987317,-93.43045334250634,10724,31.90041029466617,-0.8457807204487175,-197.00597073746417,98.50298536873208,-182.38221028287663
351,550.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
351,800.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
351,600.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
351,650.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
351,700.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
351,750.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
351,450.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
351,850.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
351,500.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
351,200.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
351,400.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
351,950.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
351,350.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
351,300.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
351,250.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
351,150.5,9.606531110456654,-95.19673444477168,17857,19.476955815646523,-0.6356682943106791,-200.96548233515287,100.48274116757642,-183.21094691056095
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901,100.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,50.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,250.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,300.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,350.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,400.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,450.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,500.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,550.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,600.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,650.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,700.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,750.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,800.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,850.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,900.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,950.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
901,200.5,563.7908687274055,181.89543436370275,10270,19.561830574488802,0.09336862353854584,-1726.9726968393975,863.4863484196987,-507.77322088959374
1 period std final_eq ret_pct n_trades win_rate sharpe max_dd_u max_dd_pct stable_score
2 301 100.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
3 301 600.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
4 301 300.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
5 301 350.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
6 301 400.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
7 301 450.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
8 301 500.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
9 301 550.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
10 301 650.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
11 301 200.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
12 301 750.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
13 301 800.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
14 301 850.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
15 301 900.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
16 301 950.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
17 301 50.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
18 301 250.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
19 301 700.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
20 301 150.5 52.83984459402632 -73.58007770298684 19058 20.495330045125407 -0.3757296908035501 -226.550345134987 113.27517256749351 -168.70897204662427
21 751 350.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
22 751 800.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
23 751 300.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
24 751 150.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
25 751 100.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
26 751 50.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
27 751 950.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
28 751 900.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
29 751 250.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
30 751 850.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
31 751 750.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
32 751 700.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
33 751 650.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
34 751 600.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
35 751 550.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
36 751 500.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
37 751 450.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
38 751 400.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
39 751 200.5 127.53055242971492 -36.23472378514254 11780 19.898132427843805 -0.1076378009333422 -345.62059572323346 172.81029786161673 -175.77461568563604
40 901 0.5 13.45175555605595 -93.27412222197202 10264 30.670303975058456 -0.8952537525217249 -189.5983790965677 94.79918954828385 -179.8565188908598
41 851 0.5 13.139093314987317 -93.43045334250634 10724 31.90041029466617 -0.8457807204487175 -197.00597073746417 98.50298536873208 -182.38221028287663
42 351 550.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
43 351 800.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
44 351 600.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
45 351 650.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
46 351 700.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
47 351 750.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
48 351 450.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
49 351 850.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
50 351 500.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
51 351 200.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
52 351 400.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
53 351 950.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
54 351 350.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
55 351 300.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
56 351 250.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
57 351 150.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
58 351 100.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
59 351 50.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
60 351 900.5 9.606531110456654 -95.19673444477168 17857 19.476955815646523 -0.6356682943106791 -200.96548233515287 100.48274116757642 -183.21094691056095
61 801 0.5 10.34628311811287 -94.82685844094357 11410 31.0692375109553 -0.8073996538916506 -208.98636227084955 104.49318113542478 -188.1101991959832
62 551 0.5 4.519023184638675 -97.74048840768066 14150 33.36395759717314 -0.9378467165440548 -198.0691055074856 99.0345527537428 -188.22229120920355
63 501 0.5 1.8986708832397656 -99.05066455838012 15372 33.36586000520427 -0.8822266044169348 -198.31333975387093 99.15666987693547 -188.96271971293172
64 701 0.5 1.6035402845204254 -99.19822985773979 12485 31.229475370444533 -0.8874406539711374 -198.89129268300212 99.44564634150106 -189.40403477859428
65 551 350.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
66 551 600.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
67 551 300.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
68 551 950.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
69 551 900.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
70 551 850.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
71 551 800.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
72 551 700.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
73 551 650.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
74 551 750.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
75 551 550.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
76 551 450.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
77 551 400.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
78 551 50.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
79 551 100.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
80 551 150.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
81 551 200.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
82 551 250.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
83 551 500.5 11.436749188725278 -94.28162540563736 14159 19.6200296631118 -0.5181379138095433 -224.62755043239733 112.31377521619868 -190.3503005443108
84 751 0.5 6.787536497792063 -96.60623175110396 11769 31.701928795989463 -0.8973785204314527 -208.02359739762167 104.01179869881084 -190.58421295533006
85 101 0.5 2.9344181713149435 -98.53279091434253 35484 40.70003381805884 -1.0207595955600905 -200.5343407319586 100.2671703659793 -190.99564235384705
86 401 750.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
87 401 600.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
88 401 650.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
89 401 700.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
90 401 950.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
91 401 800.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
92 401 850.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
93 401 900.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
94 401 500.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
95 401 550.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
96 401 300.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
97 401 450.5 6.006954718238241 -96.99652264088088 17153 19.413513671077943 -0.554621338370883 -218.38418033295903 109.19209016647953 -191.0056508345151
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366 851 900.5 316.5964254122197 58.298212706109844 10734 20.18818706912614 0.06259789806970045 -1385.5342422406081 692.7671211203041 -495.164309413297
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373 851 450.5 316.5964254122197 58.298212706109844 10734 20.18818706912614 0.06259789806970045 -1385.5342422406081 692.7671211203041 -495.164309413297
374 851 400.5 316.5964254122197 58.298212706109844 10734 20.18818706912614 0.06259789806970045 -1385.5342422406081 692.7671211203041 -495.164309413297
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377 851 250.5 316.5964254122197 58.298212706109844 10734 20.18818706912614 0.06259789806970045 -1385.5342422406081 692.7671211203041 -495.164309413297
378 851 200.5 316.5964254122197 58.298212706109844 10734 20.18818706912614 0.06259789806970045 -1385.5342422406081 692.7671211203041 -495.164309413297
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226
strategy/results/bb_full_grid_5m_20260226_222638.csv
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@@ -1,226 +0,0 @@
period,std,final_eq,ret_pct,n_trades,win_rate,sharpe,max_dd_u,max_dd_pct,stable_score
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375,700.0,5.937868901039763,-97.03106554948012,17595,19.30093776641091,-1.0463498951646035,-219.2851323144315,109.64256615721575,-197.30131721722796
375,550.0,5.937868901039763,-97.03106554948012,17595,19.30093776641091,-1.0463498951646035,-219.2851323144315,109.64256615721575,-197.30131721722796
375,600.0,5.937868901039763,-97.03106554948012,17595,19.30093776641091,-1.0463498951646035,-219.2851323144315,109.64256615721575,-197.30131721722796
375,650.0,5.937868901039763,-97.03106554948012,17595,19.30093776641091,-1.0463498951646035,-219.2851323144315,109.64256615721575,-197.30131721722796
375,750.0,5.937868901039763,-97.03106554948012,17595,19.30093776641091,-1.0463498951646035,-219.2851323144315,109.64256615721575,-197.30131721722796
375,800.0,5.937868901039763,-97.03106554948012,17595,19.30093776641091,-1.0463498951646035,-219.2851323144315,109.64256615721575,-197.30131721722796
375,100.0,5.937868901039763,-97.03106554948012,17595,19.30093776641091,-1.0463498951646035,-219.2851323144315,109.64256615721575,-197.30131721722796
375,200.0,5.937868901039763,-97.03106554948012,17595,19.30093776641091,-1.0463498951646035,-219.2851323144315,109.64256615721575,-197.30131721722796
50,100.0,0.20229758805618503,-99.89885120597191,52558,22.158377411621448,-0.977563863811585,-230.59754074358267,115.29877037179132,-203.868633869144
50,750.0,0.20229758805618503,-99.89885120597191,52558,22.158377411621448,-0.977563863811585,-230.59754074358267,115.29877037179132,-203.868633869144
50,700.0,0.20229758805618503,-99.89885120597191,52558,22.158377411621448,-0.977563863811585,-230.59754074358267,115.29877037179132,-203.868633869144
50,650.0,0.20229758805618503,-99.89885120597191,52558,22.158377411621448,-0.977563863811585,-230.59754074358267,115.29877037179132,-203.868633869144
50,600.0,0.20229758805618503,-99.89885120597191,52558,22.158377411621448,-0.977563863811585,-230.59754074358267,115.29877037179132,-203.868633869144
50,550.0,0.20229758805618503,-99.89885120597191,52558,22.158377411621448,-0.977563863811585,-230.59754074358267,115.29877037179132,-203.868633869144
50,500.0,0.20229758805618503,-99.89885120597191,52558,22.158377411621448,-0.977563863811585,-230.59754074358267,115.29877037179132,-203.868633869144
50,450.0,0.20229758805618503,-99.89885120597191,52558,22.158377411621448,-0.977563863811585,-230.59754074358267,115.29877037179132,-203.868633869144
50,150.0,0.20229758805618503,-99.89885120597191,52558,22.158377411621448,-0.977563863811585,-230.59754074358267,115.29877037179132,-203.868633869144
50,400.0,0.20229758805618503,-99.89885120597191,52558,22.158377411621448,-0.977563863811585,-230.59754074358267,115.29877037179132,-203.868633869144
50,350.0,0.20229758805618503,-99.89885120597191,52558,22.158377411621448,-0.977563863811585,-230.59754074358267,115.29877037179132,-203.868633869144
50,300.0,0.20229758805618503,-99.89885120597191,52558,22.158377411621448,-0.977563863811585,-230.59754074358267,115.29877037179132,-203.868633869144
50,250.0,0.20229758805618503,-99.89885120597191,52558,22.158377411621448,-0.977563863811585,-230.59754074358267,115.29877037179132,-203.868633869144
50,200.0,0.20229758805618503,-99.89885120597191,52558,22.158377411621448,-0.977563863811585,-230.59754074358267,115.29877037179132,-203.868633869144
50,800.0,0.20229758805618503,-99.89885120597191,52558,22.158377411621448,-0.977563863811585,-230.59754074358267,115.29877037179132,-203.868633869144
125,100.0,1.334577627395088,-99.33271118630246,32325,21.11987625676721,-0.4820672247830791,-256.24427480478045,128.12213740239022,-207.6152278056116
125,800.0,1.334577627395088,-99.33271118630246,32325,21.11987625676721,-0.4820672247830791,-256.24427480478045,128.12213740239022,-207.6152278056116
125,150.0,1.334577627395088,-99.33271118630246,32325,21.11987625676721,-0.4820672247830791,-256.24427480478045,128.12213740239022,-207.6152278056116
125,700.0,1.334577627395088,-99.33271118630246,32325,21.11987625676721,-0.4820672247830791,-256.24427480478045,128.12213740239022,-207.6152278056116
125,650.0,1.334577627395088,-99.33271118630246,32325,21.11987625676721,-0.4820672247830791,-256.24427480478045,128.12213740239022,-207.6152278056116
125,600.0,1.334577627395088,-99.33271118630246,32325,21.11987625676721,-0.4820672247830791,-256.24427480478045,128.12213740239022,-207.6152278056116
125,550.0,1.334577627395088,-99.33271118630246,32325,21.11987625676721,-0.4820672247830791,-256.24427480478045,128.12213740239022,-207.6152278056116
125,500.0,1.334577627395088,-99.33271118630246,32325,21.11987625676721,-0.4820672247830791,-256.24427480478045,128.12213740239022,-207.6152278056116
125,450.0,1.334577627395088,-99.33271118630246,32325,21.11987625676721,-0.4820672247830791,-256.24427480478045,128.12213740239022,-207.6152278056116
125,400.0,1.334577627395088,-99.33271118630246,32325,21.11987625676721,-0.4820672247830791,-256.24427480478045,128.12213740239022,-207.6152278056116
125,350.0,1.334577627395088,-99.33271118630246,32325,21.11987625676721,-0.4820672247830791,-256.24427480478045,128.12213740239022,-207.6152278056116
125,300.0,1.334577627395088,-99.33271118630246,32325,21.11987625676721,-0.4820672247830791,-256.24427480478045,128.12213740239022,-207.6152278056116
125,250.0,1.334577627395088,-99.33271118630246,32325,21.11987625676721,-0.4820672247830791,-256.24427480478045,128.12213740239022,-207.6152278056116
125,200.0,1.334577627395088,-99.33271118630246,32325,21.11987625676721,-0.4820672247830791,-256.24427480478045,128.12213740239022,-207.6152278056116
125,750.0,1.334577627395088,-99.33271118630246,32325,21.11987625676721,-0.4820672247830791,-256.24427480478045,128.12213740239022,-207.6152278056116
150,100.0,0.7433418443261947,-99.62832907783691,29333,20.301367060989328,-0.5789198728571853,-271.1227462874435,135.56137314372174,-215.02446606710055
150,150.0,0.7433418443261947,-99.62832907783691,29333,20.301367060989328,-0.5789198728571853,-271.1227462874435,135.56137314372174,-215.02446606710055
150,250.0,0.7433418443261947,-99.62832907783691,29333,20.301367060989328,-0.5789198728571853,-271.1227462874435,135.56137314372174,-215.02446606710055
150,300.0,0.7433418443261947,-99.62832907783691,29333,20.301367060989328,-0.5789198728571853,-271.1227462874435,135.56137314372174,-215.02446606710055
150,350.0,0.7433418443261947,-99.62832907783691,29333,20.301367060989328,-0.5789198728571853,-271.1227462874435,135.56137314372174,-215.02446606710055
150,400.0,0.7433418443261947,-99.62832907783691,29333,20.301367060989328,-0.5789198728571853,-271.1227462874435,135.56137314372174,-215.02446606710055
150,450.0,0.7433418443261947,-99.62832907783691,29333,20.301367060989328,-0.5789198728571853,-271.1227462874435,135.56137314372174,-215.02446606710055
150,500.0,0.7433418443261947,-99.62832907783691,29333,20.301367060989328,-0.5789198728571853,-271.1227462874435,135.56137314372174,-215.02446606710055
150,550.0,0.7433418443261947,-99.62832907783691,29333,20.301367060989328,-0.5789198728571853,-271.1227462874435,135.56137314372174,-215.02446606710055
150,600.0,0.7433418443261947,-99.62832907783691,29333,20.301367060989328,-0.5789198728571853,-271.1227462874435,135.56137314372174,-215.02446606710055
150,650.0,0.7433418443261947,-99.62832907783691,29333,20.301367060989328,-0.5789198728571853,-271.1227462874435,135.56137314372174,-215.02446606710055
150,700.0,0.7433418443261947,-99.62832907783691,29333,20.301367060989328,-0.5789198728571853,-271.1227462874435,135.56137314372174,-215.02446606710055
150,750.0,0.7433418443261947,-99.62832907783691,29333,20.301367060989328,-0.5789198728571853,-271.1227462874435,135.56137314372174,-215.02446606710055
150,800.0,0.7433418443261947,-99.62832907783691,29333,20.301367060989328,-0.5789198728571853,-271.1227462874435,135.56137314372174,-215.02446606710055
150,200.0,0.7433418443261947,-99.62832907783691,29333,20.301367060989328,-0.5789198728571853,-271.1227462874435,135.56137314372174,-215.02446606710055
100,800.0,6.102892798816175,-96.94855360059191,36266,21.471902057023108,-0.4248636518486794,-301.4147033407358,150.7073516703679,-222.6127987590704
100,750.0,6.102892798816175,-96.94855360059191,36266,21.471902057023108,-0.4248636518486794,-301.4147033407358,150.7073516703679,-222.6127987590704
100,400.0,6.102892798816175,-96.94855360059191,36266,21.471902057023108,-0.4248636518486794,-301.4147033407358,150.7073516703679,-222.6127987590704
100,650.0,6.102892798816175,-96.94855360059191,36266,21.471902057023108,-0.4248636518486794,-301.4147033407358,150.7073516703679,-222.6127987590704
100,600.0,6.102892798816175,-96.94855360059191,36266,21.471902057023108,-0.4248636518486794,-301.4147033407358,150.7073516703679,-222.6127987590704
100,550.0,6.102892798816175,-96.94855360059191,36266,21.471902057023108,-0.4248636518486794,-301.4147033407358,150.7073516703679,-222.6127987590704
100,500.0,6.102892798816175,-96.94855360059191,36266,21.471902057023108,-0.4248636518486794,-301.4147033407358,150.7073516703679,-222.6127987590704
100,450.0,6.102892798816175,-96.94855360059191,36266,21.471902057023108,-0.4248636518486794,-301.4147033407358,150.7073516703679,-222.6127987590704
100,700.0,6.102892798816175,-96.94855360059191,36266,21.471902057023108,-0.4248636518486794,-301.4147033407358,150.7073516703679,-222.6127987590704
100,350.0,6.102892798816175,-96.94855360059191,36266,21.471902057023108,-0.4248636518486794,-301.4147033407358,150.7073516703679,-222.6127987590704
100,250.0,6.102892798816175,-96.94855360059191,36266,21.471902057023108,-0.4248636518486794,-301.4147033407358,150.7073516703679,-222.6127987590704
100,200.0,6.102892798816175,-96.94855360059191,36266,21.471902057023108,-0.4248636518486794,-301.4147033407358,150.7073516703679,-222.6127987590704
100,150.0,6.102892798816175,-96.94855360059191,36266,21.471902057023108,-0.4248636518486794,-301.4147033407358,150.7073516703679,-222.6127987590704
100,100.0,6.102892798816175,-96.94855360059191,36266,21.471902057023108,-0.4248636518486794,-301.4147033407358,150.7073516703679,-222.6127987590704
100,300.0,6.102892798816175,-96.94855360059191,36266,21.471902057023108,-0.4248636518486794,-301.4147033407358,150.7073516703679,-222.6127987590704
175,400.0,12.4581987388331,-93.77090063058345,26341,20.64462245169128,-0.43133451172875364,-347.90454143788213,173.95227071894107,-238.10873134648136
175,700.0,12.4581987388331,-93.77090063058345,26341,20.64462245169128,-0.43133451172875364,-347.90454143788213,173.95227071894107,-238.10873134648136
175,650.0,12.4581987388331,-93.77090063058345,26341,20.64462245169128,-0.43133451172875364,-347.90454143788213,173.95227071894107,-238.10873134648136
175,600.0,12.4581987388331,-93.77090063058345,26341,20.64462245169128,-0.43133451172875364,-347.90454143788213,173.95227071894107,-238.10873134648136
175,550.0,12.4581987388331,-93.77090063058345,26341,20.64462245169128,-0.43133451172875364,-347.90454143788213,173.95227071894107,-238.10873134648136
175,500.0,12.4581987388331,-93.77090063058345,26341,20.64462245169128,-0.43133451172875364,-347.90454143788213,173.95227071894107,-238.10873134648136
175,450.0,12.4581987388331,-93.77090063058345,26341,20.64462245169128,-0.43133451172875364,-347.90454143788213,173.95227071894107,-238.10873134648136
175,250.0,12.4581987388331,-93.77090063058345,26341,20.64462245169128,-0.43133451172875364,-347.90454143788213,173.95227071894107,-238.10873134648136
175,350.0,12.4581987388331,-93.77090063058345,26341,20.64462245169128,-0.43133451172875364,-347.90454143788213,173.95227071894107,-238.10873134648136
175,300.0,12.4581987388331,-93.77090063058345,26341,20.64462245169128,-0.43133451172875364,-347.90454143788213,173.95227071894107,-238.10873134648136
175,200.0,12.4581987388331,-93.77090063058345,26341,20.64462245169128,-0.43133451172875364,-347.90454143788213,173.95227071894107,-238.10873134648136
175,150.0,12.4581987388331,-93.77090063058345,26341,20.64462245169128,-0.43133451172875364,-347.90454143788213,173.95227071894107,-238.10873134648136
175,100.0,12.4581987388331,-93.77090063058345,26341,20.64462245169128,-0.43133451172875364,-347.90454143788213,173.95227071894107,-238.10873134648136
175,750.0,12.4581987388331,-93.77090063058345,26341,20.64462245169128,-0.43133451172875364,-347.90454143788213,173.95227071894107,-238.10873134648136
175,800.0,12.4581987388331,-93.77090063058345,26341,20.64462245169128,-0.43133451172875364,-347.90454143788213,173.95227071894107,-238.10873134648136
1 period std final_eq ret_pct n_trades win_rate sharpe max_dd_u max_dd_pct stable_score
2 275 800.0 63.55924228204109 -68.22037885897946 20470 20.058622374206156 -0.3140671553821636 -228.29076347059168 114.14538173529584 -163.3054901118021
3 275 750.0 63.55924228204109 -68.22037885897946 20470 20.058622374206156 -0.3140671553821636 -228.29076347059168 114.14538173529584 -163.3054901118021
4 275 150.0 63.55924228204109 -68.22037885897946 20470 20.058622374206156 -0.3140671553821636 -228.29076347059168 114.14538173529584 -163.3054901118021
5 275 200.0 63.55924228204109 -68.22037885897946 20470 20.058622374206156 -0.3140671553821636 -228.29076347059168 114.14538173529584 -163.3054901118021
6 275 250.0 63.55924228204109 -68.22037885897946 20470 20.058622374206156 -0.3140671553821636 -228.29076347059168 114.14538173529584 -163.3054901118021
7 275 300.0 63.55924228204109 -68.22037885897946 20470 20.058622374206156 -0.3140671553821636 -228.29076347059168 114.14538173529584 -163.3054901118021
8 275 350.0 63.55924228204109 -68.22037885897946 20470 20.058622374206156 -0.3140671553821636 -228.29076347059168 114.14538173529584 -163.3054901118021
9 275 400.0 63.55924228204109 -68.22037885897946 20470 20.058622374206156 -0.3140671553821636 -228.29076347059168 114.14538173529584 -163.3054901118021
10 275 450.0 63.55924228204109 -68.22037885897946 20470 20.058622374206156 -0.3140671553821636 -228.29076347059168 114.14538173529584 -163.3054901118021
11 275 500.0 63.55924228204109 -68.22037885897946 20470 20.058622374206156 -0.3140671553821636 -228.29076347059168 114.14538173529584 -163.3054901118021
12 275 550.0 63.55924228204109 -68.22037885897946 20470 20.058622374206156 -0.3140671553821636 -228.29076347059168 114.14538173529584 -163.3054901118021
13 275 600.0 63.55924228204109 -68.22037885897946 20470 20.058622374206156 -0.3140671553821636 -228.29076347059168 114.14538173529584 -163.3054901118021
14 275 650.0 63.55924228204109 -68.22037885897946 20470 20.058622374206156 -0.3140671553821636 -228.29076347059168 114.14538173529584 -163.3054901118021
15 275 700.0 63.55924228204109 -68.22037885897946 20470 20.058622374206156 -0.3140671553821636 -228.29076347059168 114.14538173529584 -163.3054901118021
16 275 100.0 63.55924228204109 -68.22037885897946 20470 20.058622374206156 -0.3140671553821636 -228.29076347059168 114.14538173529584 -163.3054901118021
17 300 150.0 45.636613027184836 -77.18169348640758 19146 20.531703750130575 -0.4725797195498165 -233.02158222956754 116.51079111478377 -176.0612830128324
18 300 800.0 45.636613027184836 -77.18169348640758 19146 20.531703750130575 -0.4725797195498165 -233.02158222956754 116.51079111478377 -176.0612830128324
19 300 750.0 45.636613027184836 -77.18169348640758 19146 20.531703750130575 -0.4725797195498165 -233.02158222956754 116.51079111478377 -176.0612830128324
20 300 700.0 45.636613027184836 -77.18169348640758 19146 20.531703750130575 -0.4725797195498165 -233.02158222956754 116.51079111478377 -176.0612830128324
21 300 650.0 45.636613027184836 -77.18169348640758 19146 20.531703750130575 -0.4725797195498165 -233.02158222956754 116.51079111478377 -176.0612830128324
22 300 600.0 45.636613027184836 -77.18169348640758 19146 20.531703750130575 -0.4725797195498165 -233.02158222956754 116.51079111478377 -176.0612830128324
23 300 550.0 45.636613027184836 -77.18169348640758 19146 20.531703750130575 -0.4725797195498165 -233.02158222956754 116.51079111478377 -176.0612830128324
24 300 100.0 45.636613027184836 -77.18169348640758 19146 20.531703750130575 -0.4725797195498165 -233.02158222956754 116.51079111478377 -176.0612830128324
25 300 450.0 45.636613027184836 -77.18169348640758 19146 20.531703750130575 -0.4725797195498165 -233.02158222956754 116.51079111478377 -176.0612830128324
26 300 400.0 45.636613027184836 -77.18169348640758 19146 20.531703750130575 -0.4725797195498165 -233.02158222956754 116.51079111478377 -176.0612830128324
27 300 350.0 45.636613027184836 -77.18169348640758 19146 20.531703750130575 -0.4725797195498165 -233.02158222956754 116.51079111478377 -176.0612830128324
28 300 300.0 45.636613027184836 -77.18169348640758 19146 20.531703750130575 -0.4725797195498165 -233.02158222956754 116.51079111478377 -176.0612830128324
29 300 250.0 45.636613027184836 -77.18169348640758 19146 20.531703750130575 -0.4725797195498165 -233.02158222956754 116.51079111478377 -176.0612830128324
30 300 500.0 45.636613027184836 -77.18169348640758 19146 20.531703750130575 -0.4725797195498165 -233.02158222956754 116.51079111478377 -176.0612830128324
31 300 200.0 45.636613027184836 -77.18169348640758 19146 20.531703750130575 -0.4725797195498165 -233.02158222956754 116.51079111478377 -176.0612830128324
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177
strategy/results/bb_full_grid_5m_20260226_224709.csv
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@@ -1,177 +0,0 @@
period,std,final_eq,ret_pct,n_trades,win_rate,sharpe,max_dd_u,max_dd_pct,stable_score
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290,482.0,243.61657139606652,21.80828569803326,19616,20.39661500815661,0.053326400551732156,-206.99839245306777,103.49919622653387,-60.35115447657306
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290,494.0,243.61657139606652,21.80828569803326,19616,20.39661500815661,0.053326400551732156,-206.99839245306777,103.49919622653387,-60.35115447657306
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300,492.0,45.636613027184836,-77.18169348640758,19146,20.531703750130575,-0.4725797195498165,-233.02158222956754,116.51079111478377,-176.0612830128324
300,488.0,45.636613027184836,-77.18169348640758,19146,20.531703750130575,-0.4725797195498165,-233.02158222956754,116.51079111478377,-176.0612830128324
300,486.0,45.636613027184836,-77.18169348640758,19146,20.531703750130575,-0.4725797195498165,-233.02158222956754,116.51079111478377,-176.0612830128324
300,484.0,45.636613027184836,-77.18169348640758,19146,20.531703750130575,-0.4725797195498165,-233.02158222956754,116.51079111478377,-176.0612830128324
300,482.0,45.636613027184836,-77.18169348640758,19146,20.531703750130575,-0.4725797195498165,-233.02158222956754,116.51079111478377,-176.0612830128324
300,480.0,45.636613027184836,-77.18169348640758,19146,20.531703750130575,-0.4725797195498165,-233.02158222956754,116.51079111478377,-176.0612830128324
300,510.0,45.636613027184836,-77.18169348640758,19146,20.531703750130575,-0.4725797195498165,-233.02158222956754,116.51079111478377,-176.0612830128324
280,482.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
280,510.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
280,484.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
280,486.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
280,488.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
280,490.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
280,492.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
280,494.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
280,496.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
280,498.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
280,500.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
280,502.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
280,504.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
280,506.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
280,508.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
280,480.0,65.63691589272076,-67.18154205363962,20060,20.004985044865403,-0.1793386285305311,-276.8334911466567,138.41674557332834,-180.06700205466868
284,510.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
284,482.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
284,484.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
284,486.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
284,488.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
284,490.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
284,492.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
284,494.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
284,496.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
284,498.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
284,500.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
284,502.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
284,504.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
284,506.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
284,508.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
284,480.0,4.392776873192187,-97.8036115634039,19958,19.646257139993985,-0.6921390391247239,-215.20797390381568,107.60398695190784,-192.19246959442688
1 period std final_eq ret_pct n_trades win_rate sharpe max_dd_u max_dd_pct stable_score
2 290 496.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
3 290 510.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
4 290 482.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
5 290 484.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
6 290 486.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
7 290 488.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
8 290 490.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
9 290 492.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
10 290 494.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
11 290 498.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
12 290 500.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
13 290 502.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
14 290 504.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
15 290 506.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
16 290 508.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
17 290 480.0 243.61657139606652 21.80828569803326 19616 20.39661500815661 0.053326400551732156 -206.99839245306777 103.49919622653387 -60.35115447657306
18 294 510.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
19 294 492.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
20 294 508.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
21 294 482.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
22 294 484.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
23 294 486.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
24 294 488.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
25 294 490.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
26 294 480.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
27 294 494.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
28 294 506.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
29 294 498.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
30 294 500.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
31 294 502.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
32 294 504.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
33 294 496.0 199.68691854748374 -0.15654072625812887 19426 20.369607742201172 -0.00044065189971063414 -202.09482205003653 101.04741102501826 -80.99975736906927
34 292 480.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
35 292 482.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
36 292 484.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
37 292 488.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
38 292 490.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
39 292 492.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
40 292 494.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
41 292 486.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
42 292 498.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
43 292 508.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
44 292 500.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
45 292 510.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
46 292 496.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
47 292 506.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
48 292 502.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
49 292 504.0 162.04741012047063 -18.976294939764685 19532 20.243702641818555 -0.06641787769617029 -211.29322924484535 105.64661462242269 -104.29060117005689
50 296 494.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
51 296 506.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
52 296 504.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
53 296 502.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
54 296 500.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
55 296 498.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
56 296 496.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
57 296 492.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
58 296 490.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
59 296 488.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
60 296 484.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
61 296 482.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
62 296 480.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
63 296 510.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
64 296 486.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
65 296 508.0 75.00472410848523 -62.497637945757376 19304 20.33257355988396 -0.2675450843656243 -202.54566627473156 101.27283313736577 -146.72644546803747
66 288 480.0 52.63249387410759 -73.68375306294621 19698 20.200020306630115 -0.2855012237698626 -205.4509978409444 102.7254989204722 -159.29016688456232
67 288 498.0 52.63249387410759 -73.68375306294621 19698 20.200020306630115 -0.2855012237698626 -205.4509978409444 102.7254989204722 -159.29016688456232
68 288 482.0 52.63249387410759 -73.68375306294621 19698 20.200020306630115 -0.2855012237698626 -205.4509978409444 102.7254989204722 -159.29016688456232
69 288 510.0 52.63249387410759 -73.68375306294621 19698 20.200020306630115 -0.2855012237698626 -205.4509978409444 102.7254989204722 -159.29016688456232
70 288 508.0 52.63249387410759 -73.68375306294621 19698 20.200020306630115 -0.2855012237698626 -205.4509978409444 102.7254989204722 -159.29016688456232
71 288 506.0 52.63249387410759 -73.68375306294621 19698 20.200020306630115 -0.2855012237698626 -205.4509978409444 102.7254989204722 -159.29016688456232
72 288 504.0 52.63249387410759 -73.68375306294621 19698 20.200020306630115 -0.2855012237698626 -205.4509978409444 102.7254989204722 -159.29016688456232
73 288 502.0 52.63249387410759 -73.68375306294621 19698 20.200020306630115 -0.2855012237698626 -205.4509978409444 102.7254989204722 -159.29016688456232
74 288 500.0 52.63249387410759 -73.68375306294621 19698 20.200020306630115 -0.2855012237698626 -205.4509978409444 102.7254989204722 -159.29016688456232
75 288 494.0 52.63249387410759 -73.68375306294621 19698 20.200020306630115 -0.2855012237698626 -205.4509978409444 102.7254989204722 -159.29016688456232
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period,std,period_step,std_step,final_eq,ret_pct,n_trades,win_rate,sharpe,dd
15,1.5,10,0.5,48.48092814523593,-75.75953592738203,39080,37.35926305015353,-0.13119006344299677,-697.4004561224623
15,2.0,10,0.5,36.80358364077569,-81.59820817961216,40087,34.42263077805772,-0.1511470195866686,-639.962755073432
15,2.5,10,0.5,15.801154047365497,-92.09942297631726,41183,31.27746885850958,-0.2043489750410225,-608.2984283591458
15,3.0,10,0.5,10.259192622590602,-94.8704036887047,42341,28.449965754233485,-0.2628767129294784,-456.8505810537002
15,3.5,10,0.5,10.780007201280425,-94.60999639935979,43317,26.490754207355078,-0.3519748652128244,-315.69577713194894
15,4.0,10,0.5,6.762275289834179,-96.61886235508291,43780,25.48423937871174,-0.44416507239126524,-220.0935343290423
25,1.5,10,0.5,15.185351901530389,-92.4073240492348,29514,35.12909127871519,-0.4186681901048435,-273.0478151000914
25,2.0,10,0.5,11.75114974910751,-94.12442512544625,30273,31.972384633171476,-0.4746326737292537,-221.26735265117904
25,2.5,10,0.5,22.991590869749515,-88.50420456512524,30921,29.355454222049737,-0.48801085648391873,-209.49673224901383
25,3.0,10,0.5,32.34220252441412,-83.82889873779294,31558,27.05177767919387,-0.3753596284992899,-198.35054814330795
25,3.5,10,0.5,21.650159567897365,-89.17492021605132,32117,25.503627362456022,-0.39733883257515135,-218.6979255650799
25,4.0,10,0.5,31.860026972198018,-84.06998651390099,32563,24.432638270429628,-0.29302419118789713,-239.82444497645355
35,1.5,10,0.5,4.101201077511393,-97.9493994612443,24993,33.06525827231625,-0.8217792504548787,-245.46414083107442
35,2.0,10,0.5,5.823800486102119,-97.08809975694894,25587,29.866729198421073,-0.7426470214087769,-286.4406699983145
35,2.5,10,0.5,5.192863521634847,-97.40356823918258,26051,27.265747955932596,-0.7730880094730899,-266.1512752758687
35,3.0,10,0.5,3.4579745963381434,-98.27101270183093,26599,25.33553893003496,-0.4636391572882053,-387.52773267113974
35,3.5,10,0.5,3.5341183807460554,-98.23294080962697,27034,24.058592883036177,-0.5140569048308203,-362.5140804337234
35,4.0,10,0.5,5.611416981128725,-97.19429150943564,27353,23.255218805981063,-0.3312688921086745,-564.2468214116803
45,1.5,10,0.5,14.632970646405713,-92.68351467679715,21687,32.23590169225803,-0.6509354804642926,-235.74612368411292
45,2.0,10,0.5,31.77948296599959,-84.1102585170002,22150,29.16027088036117,-0.4284165792843191,-269.6776110433831
45,2.5,10,0.5,172.40643379727345,-13.796783101363275,22525,26.81908990011099,-0.043299240462867164,-256.7844732672258
45,3.0,10,0.5,41.377840288991365,-79.31107985550432,22953,25.077331939180063,-0.34899879597984057,-296.6696402361111
45,3.5,10,0.5,74.54221936958507,-62.728890315207465,23241,24.052321328686375,-0.2535287445141998,-305.77109805667635
45,4.0,10,0.5,197.09642100514674,-1.4517894974266312,23514,23.394573445606873,-0.003849297905947261,-346.25462265446606
55,1.5,10,0.5,16.079473075595672,-91.96026346220216,19596,31.02163706878955,-0.6486187784866569,-231.27610776888795
55,2.0,10,0.5,66.87668346352854,-66.56165826823573,19950,28.25062656641604,-0.33475319699218586,-252.4384445697346
55,2.5,10,0.5,103.71180081815875,-48.144099590920625,20315,26.059561900073835,-0.23234055949460508,-230.74049378986336
55,3.0,10,0.5,30.706542292630328,-84.64672885368483,20620,24.539282250242483,-0.5332739163457259,-280.4316033186037
55,3.5,10,0.5,19.94144489188811,-90.02927755405594,20900,23.535885167464116,-0.4556365722046084,-337.09291756128977
55,4.0,10,0.5,35.10054549461695,-82.44972725269153,21094,22.88802503081445,-0.41982398626910356,-291.17676700223814
65,1.5,10,0.5,40.332952988339386,-79.8335235058303,17772,30.31172631105109,-0.37260054288227523,-282.40115704942934
65,2.0,10,0.5,50.70752436109092,-74.64623781945454,18112,27.49006183745583,-0.4263815652152113,-215.11807270889656
65,2.5,10,0.5,208.09642220895924,4.04821110447962,18381,25.55356074207062,0.013052853027704602,-225.3544321606581
65,3.0,10,0.5,69.17941178529537,-65.41029410735231,18603,24.27565446433371,-0.3475091632420777,-267.8026072891796
65,3.5,10,0.5,205.87085569261697,2.9354278463084853,18841,23.459476673212677,0.007794090142701672,-365.7703099142676
65,4.0,10,0.5,295.45309206039354,47.72654603019677,19007,22.812647971799862,0.07753257373956958,-359.300057037728
75,1.5,10,0.5,10.983713106190567,-94.50814344690471,16303,29.761393608538306,-0.7512444028811992,-213.15198872906865
75,2.0,10,0.5,13.482406589107267,-93.25879670544637,16620,27.081829121540313,-0.9839660957936669,-208.4588882425938
75,2.5,10,0.5,89.942884051582,-55.028557974209,16868,25.43277211287645,-0.36375113759430683,-211.31503916370698
75,3.0,10,0.5,52.241254269813616,-73.87937286509319,17087,24.28747000643764,-0.5981765475484465,-226.7492413952581
75,3.5,10,0.5,83.14852160988522,-58.42573919505739,17305,23.449869979774633,-0.3267893523857235,-230.606510358243
75,4.0,10,0.5,119.23474995074422,-40.38262502462789,17460,22.82359679266896,-0.20534903490050124,-207.9658889220549
85,1.5,10,0.5,7.349676583985419,-96.32516170800729,15179,28.89518413597734,-0.8318675275223718,-239.72913883031944
85,2.0,10,0.5,18.984025431376047,-90.50798728431198,15430,26.629941672067403,-0.7833690422200502,-235.6042347672767
85,2.5,10,0.5,57.167618532561264,-71.41619073371936,15664,24.86593462717058,-0.506936066912954,-235.03840225356828
85,3.0,10,0.5,35.58954831791428,-82.20522584104286,15844,23.54834637717748,-0.6040070996108049,-234.6785523683346
85,3.5,10,0.5,63.4069876968914,-68.2965061515543,16023,22.72982587530425,-0.3382080473039228,-295.9762241559409
85,4.0,10,0.5,88.47696456769705,-55.761517716151474,16178,22.147360613178392,-0.20193042953347687,-316.4817254821683
95,1.5,10,0.5,10.405821375357965,-94.79708931232102,14416,28.24639289678135,-0.7837973560897118,-270.90798093738533
95,2.0,10,0.5,13.815903557653941,-93.09204822117303,14665,25.741561541084213,-0.8765580107174691,-263.12113920477657
95,2.5,10,0.5,25.71688278154052,-87.14155860922975,14858,23.832278906986133,-0.6723450672405393,-274.7658311337973
95,3.0,10,0.5,19.356147709815595,-90.3219261450922,15049,22.652667951358893,-0.726365789425801,-269.1728308007388
95,3.5,10,0.5,25.893289102448367,-87.05335544877582,15185,22.081000987816925,-0.6885792508968731,-278.83350717325703
95,4.0,10,0.5,34.075610133322904,-82.96219493333855,15310,21.606792945787067,-0.541211763961438,-255.9863145165913
105,1.5,10,0.5,11.125302869126308,-94.43734856543685,13778,27.340688053418493,-0.6600706399042071,-269.35929952511646
105,2.0,10,0.5,10.200827786542048,-94.89958610672898,14010,24.753747323340473,-0.6866326886722063,-258.9303568592826
105,2.5,10,0.5,9.579821710127378,-95.2100891449363,14163,23.151874602838383,-0.5987791032180315,-274.5676511660392
105,3.0,10,0.5,4.3087765598937855,-97.84561172005311,14346,21.971281193364003,-0.6400093736324544,-278.89884430849224
105,3.5,10,0.5,13.027667066344636,-93.48616646682768,14469,21.528785679729076,-0.5524535568435962,-284.41705640185387
105,4.0,10,0.5,13.547757879797267,-93.22612106010136,14605,21.027045532351934,-0.3485713143577096,-412.93208491456176
115,1.5,10,0.5,29.66121970936215,-85.16939014531893,13296,27.030685920577618,-0.45172471596332026,-235.02860926589415
115,2.0,10,0.5,14.293240724692515,-92.85337963765375,13517,24.4432936302434,-0.568103750659441,-232.03742432596695
115,2.5,10,0.5,11.574164514147391,-94.21291774292631,13681,22.87113515093926,-0.4778439718297106,-259.1164560473074
115,3.0,10,0.5,9.287076704988312,-95.35646164750584,13830,21.800433839479393,-0.44032821158432417,-275.63595981438704
115,3.5,10,0.5,21.398944760292235,-89.30052761985388,13960,21.181948424068768,-0.287420801784064,-317.14370796930467
115,4.0,10,0.5,60.40509496475816,-69.79745251762091,14063,20.75659532105525,-0.09411868706769629,-829.2395563032217
15,1.5,20,1.0,48.48092814523593,-75.75953592738203,39080,37.35926305015353,-0.13119006344299677,-697.4004561224623
15,2.5,20,1.0,15.801154047365497,-92.09942297631726,41183,31.27746885850958,-0.2043489750410225,-608.2984283591458
15,3.5,20,1.0,10.780007201280425,-94.60999639935979,43317,26.490754207355078,-0.3519748652128244,-315.69577713194894
35,1.5,20,1.0,4.101201077511393,-97.9493994612443,24993,33.06525827231625,-0.8217792504548787,-245.46414083107442
35,2.5,20,1.0,5.192863521634847,-97.40356823918258,26051,27.265747955932596,-0.7730880094730899,-266.1512752758687
35,3.5,20,1.0,3.5341183807460554,-98.23294080962697,27034,24.058592883036177,-0.5140569048308203,-362.5140804337234
55,1.5,20,1.0,16.079473075595672,-91.96026346220216,19596,31.02163706878955,-0.6486187784866569,-231.27610776888795
55,2.5,20,1.0,103.71180081815875,-48.144099590920625,20315,26.059561900073835,-0.23234055949460508,-230.74049378986336
55,3.5,20,1.0,19.94144489188811,-90.02927755405594,20900,23.535885167464116,-0.4556365722046084,-337.09291756128977
75,1.5,20,1.0,10.983713106190567,-94.50814344690471,16303,29.761393608538306,-0.7512444028811992,-213.15198872906865
75,2.5,20,1.0,89.942884051582,-55.028557974209,16868,25.43277211287645,-0.36375113759430683,-211.31503916370698
75,3.5,20,1.0,83.14852160988522,-58.42573919505739,17305,23.449869979774633,-0.3267893523857235,-230.606510358243
95,1.5,20,1.0,10.405821375357965,-94.79708931232102,14416,28.24639289678135,-0.7837973560897118,-270.90798093738533
95,2.5,20,1.0,25.71688278154052,-87.14155860922975,14858,23.832278906986133,-0.6723450672405393,-274.7658311337973
95,3.5,20,1.0,25.893289102448367,-87.05335544877582,15185,22.081000987816925,-0.6885792508968731,-278.83350717325703
115,1.5,20,1.0,29.66121970936215,-85.16939014531893,13296,27.030685920577618,-0.45172471596332026,-235.02860926589415
115,2.5,20,1.0,11.574164514147391,-94.21291774292631,13681,22.87113515093926,-0.4778439718297106,-259.1164560473074
115,3.5,20,1.0,21.398944760292235,-89.30052761985388,13960,21.181948424068768,-0.287420801784064,-317.14370796930467
1 period std period_step std_step final_eq ret_pct n_trades win_rate sharpe dd
2 15 1.5 10 0.5 48.48092814523593 -75.75953592738203 39080 37.35926305015353 -0.13119006344299677 -697.4004561224623
3 15 2.0 10 0.5 36.80358364077569 -81.59820817961216 40087 34.42263077805772 -0.1511470195866686 -639.962755073432
4 15 2.5 10 0.5 15.801154047365497 -92.09942297631726 41183 31.27746885850958 -0.2043489750410225 -608.2984283591458
5 15 3.0 10 0.5 10.259192622590602 -94.8704036887047 42341 28.449965754233485 -0.2628767129294784 -456.8505810537002
6 15 3.5 10 0.5 10.780007201280425 -94.60999639935979 43317 26.490754207355078 -0.3519748652128244 -315.69577713194894
7 15 4.0 10 0.5 6.762275289834179 -96.61886235508291 43780 25.48423937871174 -0.44416507239126524 -220.0935343290423
8 25 1.5 10 0.5 15.185351901530389 -92.4073240492348 29514 35.12909127871519 -0.4186681901048435 -273.0478151000914
9 25 2.0 10 0.5 11.75114974910751 -94.12442512544625 30273 31.972384633171476 -0.4746326737292537 -221.26735265117904
10 25 2.5 10 0.5 22.991590869749515 -88.50420456512524 30921 29.355454222049737 -0.48801085648391873 -209.49673224901383
11 25 3.0 10 0.5 32.34220252441412 -83.82889873779294 31558 27.05177767919387 -0.3753596284992899 -198.35054814330795
12 25 3.5 10 0.5 21.650159567897365 -89.17492021605132 32117 25.503627362456022 -0.39733883257515135 -218.6979255650799
13 25 4.0 10 0.5 31.860026972198018 -84.06998651390099 32563 24.432638270429628 -0.29302419118789713 -239.82444497645355
14 35 1.5 10 0.5 4.101201077511393 -97.9493994612443 24993 33.06525827231625 -0.8217792504548787 -245.46414083107442
15 35 2.0 10 0.5 5.823800486102119 -97.08809975694894 25587 29.866729198421073 -0.7426470214087769 -286.4406699983145
16 35 2.5 10 0.5 5.192863521634847 -97.40356823918258 26051 27.265747955932596 -0.7730880094730899 -266.1512752758687
17 35 3.0 10 0.5 3.4579745963381434 -98.27101270183093 26599 25.33553893003496 -0.4636391572882053 -387.52773267113974
18 35 3.5 10 0.5 3.5341183807460554 -98.23294080962697 27034 24.058592883036177 -0.5140569048308203 -362.5140804337234
19 35 4.0 10 0.5 5.611416981128725 -97.19429150943564 27353 23.255218805981063 -0.3312688921086745 -564.2468214116803
20 45 1.5 10 0.5 14.632970646405713 -92.68351467679715 21687 32.23590169225803 -0.6509354804642926 -235.74612368411292
21 45 2.0 10 0.5 31.77948296599959 -84.1102585170002 22150 29.16027088036117 -0.4284165792843191 -269.6776110433831
22 45 2.5 10 0.5 172.40643379727345 -13.796783101363275 22525 26.81908990011099 -0.043299240462867164 -256.7844732672258
23 45 3.0 10 0.5 41.377840288991365 -79.31107985550432 22953 25.077331939180063 -0.34899879597984057 -296.6696402361111
24 45 3.5 10 0.5 74.54221936958507 -62.728890315207465 23241 24.052321328686375 -0.2535287445141998 -305.77109805667635
25 45 4.0 10 0.5 197.09642100514674 -1.4517894974266312 23514 23.394573445606873 -0.003849297905947261 -346.25462265446606
26 55 1.5 10 0.5 16.079473075595672 -91.96026346220216 19596 31.02163706878955 -0.6486187784866569 -231.27610776888795
27 55 2.0 10 0.5 66.87668346352854 -66.56165826823573 19950 28.25062656641604 -0.33475319699218586 -252.4384445697346
28 55 2.5 10 0.5 103.71180081815875 -48.144099590920625 20315 26.059561900073835 -0.23234055949460508 -230.74049378986336
29 55 3.0 10 0.5 30.706542292630328 -84.64672885368483 20620 24.539282250242483 -0.5332739163457259 -280.4316033186037
30 55 3.5 10 0.5 19.94144489188811 -90.02927755405594 20900 23.535885167464116 -0.4556365722046084 -337.09291756128977
31 55 4.0 10 0.5 35.10054549461695 -82.44972725269153 21094 22.88802503081445 -0.41982398626910356 -291.17676700223814
32 65 1.5 10 0.5 40.332952988339386 -79.8335235058303 17772 30.31172631105109 -0.37260054288227523 -282.40115704942934
33 65 2.0 10 0.5 50.70752436109092 -74.64623781945454 18112 27.49006183745583 -0.4263815652152113 -215.11807270889656
34 65 2.5 10 0.5 208.09642220895924 4.04821110447962 18381 25.55356074207062 0.013052853027704602 -225.3544321606581
35 65 3.0 10 0.5 69.17941178529537 -65.41029410735231 18603 24.27565446433371 -0.3475091632420777 -267.8026072891796
36 65 3.5 10 0.5 205.87085569261697 2.9354278463084853 18841 23.459476673212677 0.007794090142701672 -365.7703099142676
37 65 4.0 10 0.5 295.45309206039354 47.72654603019677 19007 22.812647971799862 0.07753257373956958 -359.300057037728
38 75 1.5 10 0.5 10.983713106190567 -94.50814344690471 16303 29.761393608538306 -0.7512444028811992 -213.15198872906865
39 75 2.0 10 0.5 13.482406589107267 -93.25879670544637 16620 27.081829121540313 -0.9839660957936669 -208.4588882425938
40 75 2.5 10 0.5 89.942884051582 -55.028557974209 16868 25.43277211287645 -0.36375113759430683 -211.31503916370698
41 75 3.0 10 0.5 52.241254269813616 -73.87937286509319 17087 24.28747000643764 -0.5981765475484465 -226.7492413952581
42 75 3.5 10 0.5 83.14852160988522 -58.42573919505739 17305 23.449869979774633 -0.3267893523857235 -230.606510358243
43 75 4.0 10 0.5 119.23474995074422 -40.38262502462789 17460 22.82359679266896 -0.20534903490050124 -207.9658889220549
44 85 1.5 10 0.5 7.349676583985419 -96.32516170800729 15179 28.89518413597734 -0.8318675275223718 -239.72913883031944
45 85 2.0 10 0.5 18.984025431376047 -90.50798728431198 15430 26.629941672067403 -0.7833690422200502 -235.6042347672767
46 85 2.5 10 0.5 57.167618532561264 -71.41619073371936 15664 24.86593462717058 -0.506936066912954 -235.03840225356828
47 85 3.0 10 0.5 35.58954831791428 -82.20522584104286 15844 23.54834637717748 -0.6040070996108049 -234.6785523683346
48 85 3.5 10 0.5 63.4069876968914 -68.2965061515543 16023 22.72982587530425 -0.3382080473039228 -295.9762241559409
49 85 4.0 10 0.5 88.47696456769705 -55.761517716151474 16178 22.147360613178392 -0.20193042953347687 -316.4817254821683
50 95 1.5 10 0.5 10.405821375357965 -94.79708931232102 14416 28.24639289678135 -0.7837973560897118 -270.90798093738533
51 95 2.0 10 0.5 13.815903557653941 -93.09204822117303 14665 25.741561541084213 -0.8765580107174691 -263.12113920477657
52 95 2.5 10 0.5 25.71688278154052 -87.14155860922975 14858 23.832278906986133 -0.6723450672405393 -274.7658311337973
53 95 3.0 10 0.5 19.356147709815595 -90.3219261450922 15049 22.652667951358893 -0.726365789425801 -269.1728308007388
54 95 3.5 10 0.5 25.893289102448367 -87.05335544877582 15185 22.081000987816925 -0.6885792508968731 -278.83350717325703
55 95 4.0 10 0.5 34.075610133322904 -82.96219493333855 15310 21.606792945787067 -0.541211763961438 -255.9863145165913
56 105 1.5 10 0.5 11.125302869126308 -94.43734856543685 13778 27.340688053418493 -0.6600706399042071 -269.35929952511646
57 105 2.0 10 0.5 10.200827786542048 -94.89958610672898 14010 24.753747323340473 -0.6866326886722063 -258.9303568592826
58 105 2.5 10 0.5 9.579821710127378 -95.2100891449363 14163 23.151874602838383 -0.5987791032180315 -274.5676511660392
59 105 3.0 10 0.5 4.3087765598937855 -97.84561172005311 14346 21.971281193364003 -0.6400093736324544 -278.89884430849224
60 105 3.5 10 0.5 13.027667066344636 -93.48616646682768 14469 21.528785679729076 -0.5524535568435962 -284.41705640185387
61 105 4.0 10 0.5 13.547757879797267 -93.22612106010136 14605 21.027045532351934 -0.3485713143577096 -412.93208491456176
62 115 1.5 10 0.5 29.66121970936215 -85.16939014531893 13296 27.030685920577618 -0.45172471596332026 -235.02860926589415
63 115 2.0 10 0.5 14.293240724692515 -92.85337963765375 13517 24.4432936302434 -0.568103750659441 -232.03742432596695
64 115 2.5 10 0.5 11.574164514147391 -94.21291774292631 13681 22.87113515093926 -0.4778439718297106 -259.1164560473074
65 115 3.0 10 0.5 9.287076704988312 -95.35646164750584 13830 21.800433839479393 -0.44032821158432417 -275.63595981438704
66 115 3.5 10 0.5 21.398944760292235 -89.30052761985388 13960 21.181948424068768 -0.287420801784064 -317.14370796930467
67 115 4.0 10 0.5 60.40509496475816 -69.79745251762091 14063 20.75659532105525 -0.09411868706769629 -829.2395563032217
68 15 1.5 20 1.0 48.48092814523593 -75.75953592738203 39080 37.35926305015353 -0.13119006344299677 -697.4004561224623
69 15 2.5 20 1.0 15.801154047365497 -92.09942297631726 41183 31.27746885850958 -0.2043489750410225 -608.2984283591458
70 15 3.5 20 1.0 10.780007201280425 -94.60999639935979 43317 26.490754207355078 -0.3519748652128244 -315.69577713194894
71 35 1.5 20 1.0 4.101201077511393 -97.9493994612443 24993 33.06525827231625 -0.8217792504548787 -245.46414083107442
72 35 2.5 20 1.0 5.192863521634847 -97.40356823918258 26051 27.265747955932596 -0.7730880094730899 -266.1512752758687
73 35 3.5 20 1.0 3.5341183807460554 -98.23294080962697 27034 24.058592883036177 -0.5140569048308203 -362.5140804337234
74 55 1.5 20 1.0 16.079473075595672 -91.96026346220216 19596 31.02163706878955 -0.6486187784866569 -231.27610776888795
75 55 2.5 20 1.0 103.71180081815875 -48.144099590920625 20315 26.059561900073835 -0.23234055949460508 -230.74049378986336
76 55 3.5 20 1.0 19.94144489188811 -90.02927755405594 20900 23.535885167464116 -0.4556365722046084 -337.09291756128977
77 75 1.5 20 1.0 10.983713106190567 -94.50814344690471 16303 29.761393608538306 -0.7512444028811992 -213.15198872906865
78 75 2.5 20 1.0 89.942884051582 -55.028557974209 16868 25.43277211287645 -0.36375113759430683 -211.31503916370698
79 75 3.5 20 1.0 83.14852160988522 -58.42573919505739 17305 23.449869979774633 -0.3267893523857235 -230.606510358243
80 95 1.5 20 1.0 10.405821375357965 -94.79708931232102 14416 28.24639289678135 -0.7837973560897118 -270.90798093738533
81 95 2.5 20 1.0 25.71688278154052 -87.14155860922975 14858 23.832278906986133 -0.6723450672405393 -274.7658311337973
82 95 3.5 20 1.0 25.893289102448367 -87.05335544877582 15185 22.081000987816925 -0.6885792508968731 -278.83350717325703
83 115 1.5 20 1.0 29.66121970936215 -85.16939014531893 13296 27.030685920577618 -0.45172471596332026 -235.02860926589415
84 115 2.5 20 1.0 11.574164514147391 -94.21291774292631 13681 22.87113515093926 -0.4778439718297106 -259.1164560473074
85 115 3.5 20 1.0 21.398944760292235 -89.30052761985388 13960 21.181948424068768 -0.287420801784064 -317.14370796930467

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period,std,period_step,std_step,final_eq,ret_pct,n_trades,win_rate,sharpe,dd
15,1.5,10,0.5,11.855192165034223,-94.07240391748289,19708,37.77146336513091,-0.6235222422995786,-284.85443596149724
15,2.0,10,0.5,8.753359275898845,-95.62332036205058,20195,35.246348105966824,-0.7569673183718936,-263.2971212724792
15,2.5,10,0.5,9.803525418208569,-95.09823729089571,20708,32.639559590496425,-0.4752350103234552,-277.4245022765702
15,3.0,10,0.5,3.412780495824038,-98.29360975208797,21275,29.83783783783784,-0.511480053076418,-338.8119559579532
15,3.5,10,0.5,6.198174773117584,-96.9009126134412,21716,27.73531037023393,-0.5128705717437164,-313.48715997416474
15,4.0,10,0.5,20.69226809284817,-89.65386595357592,21948,26.571897211591033,-0.32674764882357116,-403.7400501883454
25,1.5,10,0.5,5.836491805389524,-97.08175409730524,15020,35.22636484687084,-1.2249549689127937,-204.09205005290724
25,2.0,10,0.5,12.081211963616443,-93.95939401819177,15311,32.577885180589114,-0.8898463515682434,-222.54350054966164
25,2.5,10,0.5,19.914003433039493,-90.04299828348026,15659,30.161568427102626,-0.7895621638697325,-209.1994100018314
25,3.0,10,0.5,9.425303324148096,-95.28734833792595,15976,28.067100650976464,-0.7300825335867858,-243.96197406916642
25,3.5,10,0.5,5.855289614688857,-97.07235519265558,16253,26.55509752045776,-0.7085359518779542,-278.87154684168763
25,4.0,10,0.5,19.200810578463244,-90.39959471076838,16495,25.407699302819037,-0.5772741076812997,-259.23352233813335
35,1.5,10,0.5,28.21206634200495,-85.89396682899752,12085,34.73727761688043,-0.647009511262925,-223.61914840751774
35,2.0,10,0.5,55.70970922376068,-72.14514538811966,12306,31.569965870307165,-0.39673167475158405,-248.8668646132019
35,2.5,10,0.5,104.49489503334085,-47.75255248332957,12520,29.249201277955272,-0.235045001015176,-230.00122971261297
35,3.0,10,0.5,56.342667499287515,-71.82866625035624,12752,27.40746549560853,-0.3682997075298054,-252.50663799518358
35,3.5,10,0.5,107.45577769881035,-46.272111150594824,12984,26.070548367221196,-0.11749439894681797,-338.48701280893147
35,4.0,10,0.5,247.34481984104798,23.67240992052399,13141,25.34053724982878,0.056546089336753404,-264.63887018550304
45,1.5,10,0.5,13.15646879250955,-93.42176560374523,10385,34.10688493018777,-0.8149857539481573,-271.2863404459294
45,2.0,10,0.5,11.80829464190141,-94.0958526790493,10598,31.307793923381773,-0.8912043997401211,-297.47872205043245
45,2.5,10,0.5,19.14157030465441,-90.4292148476728,10763,28.96032704636254,-0.6910660598831703,-311.4102276700665
45,3.0,10,0.5,8.655841919185013,-95.6720790404075,10963,27.091124692146312,-0.7225121606690603,-326.33298667933167
45,3.5,10,0.5,11.91144901552033,-94.04427549223983,11138,26.08188184593284,-0.4970305469345722,-405.3189086446707
45,4.0,10,0.5,9.249110224038603,-95.3754448879807,11280,25.407801418439718,-0.41890741502953877,-461.95310334212525
55,1.5,10,0.5,18.500960550013694,-90.74951972499315,9480,32.07805907172996,-0.8630311046085801,-217.10439532674513
55,2.0,10,0.5,17.84513535296389,-91.07743232351805,9674,29.0262559437668,-0.8346686137946728,-233.79233332465478
55,2.5,10,0.5,11.28795717324628,-94.35602141337687,9829,26.899989826025028,-0.7157999288001704,-250.0470782402587
55,3.0,10,0.5,6.758810067617017,-96.62059496619149,9991,25.3628265438895,-0.7901176121561565,-250.49463359988766
55,3.5,10,0.5,25.48546333282715,-87.25726833358642,10108,24.39651760981401,-0.4341398096630764,-238.0657728760076
55,4.0,10,0.5,29.947033814854976,-85.0264830925725,10226,23.714062194406416,-0.22621028615725514,-308.2508768025071
65,1.5,10,0.5,70.01498211022908,-64.99250894488546,8797,31.41980220529726,-0.2472823034078909,-360.22920891906733
65,2.0,10,0.5,43.99985728425265,-78.00007135787368,8982,28.635047873524826,-0.31226067485305287,-344.43297562483747
65,2.5,10,0.5,11.141230088157057,-94.42938495592148,9139,26.326731589889484,-0.5877699195318181,-211.49453353955298
65,3.0,10,0.5,23.243485816159232,-88.37825709192039,9249,25.14866472051033,-0.4199885865099326,-225.22292758760577
65,3.5,10,0.5,30.046303723946345,-84.97684813802682,9351,24.371724949203294,-0.19507372955886226,-583.9077968841493
65,4.0,10,0.5,72.17368768595833,-63.913156157020836,9438,23.924560288196652,-0.1043229443150086,-677.8833873149147
75,1.5,10,0.5,28.878912457463993,-85.560543771268,8286,30.762732319575186,-0.712313061605688,-183.61532124127612
75,2.0,10,0.5,14.421291378727464,-92.78935431063627,8483,27.77319344571496,-0.8044560280829893,-201.50595876109264
75,2.5,10,0.5,4.806399583771505,-97.59680020811425,8610,26.10917537746806,-0.9313204289980628,-210.20790025893479
75,3.0,10,0.5,25.508786019142804,-87.2456069904286,8727,24.876819067262517,-0.49409805140782975,-215.2630427100224
75,3.5,10,0.5,16.653002578165868,-91.67349871091707,8830,23.997734994337485,-0.38357581879982244,-240.9197266621369
75,4.0,10,0.5,66.21022764054645,-66.89488617972677,8918,23.716079838528817,-0.1250596640250747,-698.6447378168509
85,1.5,10,0.5,14.951030134591296,-92.52448493270435,7845,29.547482472912684,-0.8953650772108298,-197.64150282305923
85,2.0,10,0.5,3.498277467815169,-98.25086126609241,8019,26.66167851353037,-1.0458276339206454,-214.15579835858927
85,2.5,10,0.5,1.7737078631867869,-99.1131460684066,8156,25.0,-1.0282285179369723,-213.61753481478925
85,3.0,10,0.5,8.719403599603398,-95.6402982001983,8248,24.114936954413192,-0.8211787209745792,-211.61074718573
85,3.5,10,0.5,8.943987615516464,-95.52800619224176,8328,23.499039385206533,-0.7549418677035037,-215.98611912681693
85,4.0,10,0.5,23.1770265836258,-88.4114867081871,8401,23.021068920366623,-0.3729888932052693,-212.96678953288284
95,1.5,10,0.5,24.927710059813517,-87.53614497009325,7408,29.306155507559396,-0.9520827117838521,-190.41061079110813
95,2.0,10,0.5,16.06239432079733,-91.96880283960134,7527,26.69058057659094,-0.9398549359933428,-198.34239467123092
95,2.5,10,0.5,21.973918801798945,-89.01304059910052,7633,25.153936853137694,-0.7146688539676412,-194.44238080585185
95,3.0,10,0.5,45.828446580797696,-77.08577670960115,7709,24.361136334154885,-0.43326307968690586,-186.11539161963253
95,3.5,10,0.5,60.57066438340541,-69.71466780829729,7806,23.7253394824494,-0.27195008259729253,-247.0975110088031
95,4.0,10,0.5,143.79458015304226,-28.10270992347887,7872,23.310467479674795,-0.05263662190109353,-598.1389379467043
105,1.5,10,0.5,16.454388894063115,-91.77280555296844,6991,28.92290087255042,-0.8170686782691946,-199.83483128223048
105,2.0,10,0.5,17.491042243515682,-91.25447887824215,7107,26.72013507809202,-0.6527938090965348,-198.51731255242447
105,2.5,10,0.5,13.7537652656679,-93.12311736716605,7193,25.079938829417493,-0.5618187399818152,-219.0288186814003
105,3.0,10,0.5,26.65257798217648,-86.67371100891177,7272,24.284928492849282,-0.31235378013631904,-405.153180534651
105,3.5,10,0.5,22.934969797555294,-88.53251510122236,7337,23.72904456862478,-0.3120317109901497,-344.1527116262127
105,4.0,10,0.5,52.74918908477296,-73.62540545761352,7389,23.318446339152796,-0.13089661662245716,-768.1307536755826
115,1.5,10,0.5,6.321018890365941,-96.83949055481703,6643,28.631642330272467,-1.0099053237536952,-206.58136634538567
115,2.0,10,0.5,8.586175448038853,-95.70691227598057,6751,26.307213746111685,-0.7835254269427628,-220.54728033133756
115,2.5,10,0.5,10.376641254511714,-94.81167937274414,6839,24.681971048398886,-0.6364432358639216,-218.41178219528035
115,3.0,10,0.5,9.20546544012515,-95.39726727993742,6921,23.81158792082069,-0.594131818951341,-207.12155382705376
115,3.5,10,0.5,7.630256305421915,-96.18487184728905,6966,23.16968130921619,-0.5844894984911244,-221.95436668671815
115,4.0,10,0.5,7.8321546288246875,-96.08392268558765,7019,22.838011112694115,-0.44419049676595773,-229.18136807602502
15,1.5,20,1.0,11.855192165034223,-94.07240391748289,19708,37.77146336513091,-0.6235222422995786,-284.85443596149724
15,2.5,20,1.0,9.803525418208569,-95.09823729089571,20708,32.639559590496425,-0.4752350103234552,-277.4245022765702
15,3.5,20,1.0,6.198174773117584,-96.9009126134412,21716,27.73531037023393,-0.5128705717437164,-313.48715997416474
35,1.5,20,1.0,28.21206634200495,-85.89396682899752,12085,34.73727761688043,-0.647009511262925,-223.61914840751774
35,2.5,20,1.0,104.49489503334085,-47.75255248332957,12520,29.249201277955272,-0.235045001015176,-230.00122971261297
35,3.5,20,1.0,107.45577769881035,-46.272111150594824,12984,26.070548367221196,-0.11749439894681797,-338.48701280893147
55,1.5,20,1.0,18.500960550013694,-90.74951972499315,9480,32.07805907172996,-0.8630311046085801,-217.10439532674513
55,2.5,20,1.0,11.28795717324628,-94.35602141337687,9829,26.899989826025028,-0.7157999288001704,-250.0470782402587
55,3.5,20,1.0,25.48546333282715,-87.25726833358642,10108,24.39651760981401,-0.4341398096630764,-238.0657728760076
75,1.5,20,1.0,28.878912457463993,-85.560543771268,8286,30.762732319575186,-0.712313061605688,-183.61532124127612
75,2.5,20,1.0,4.806399583771505,-97.59680020811425,8610,26.10917537746806,-0.9313204289980628,-210.20790025893479
75,3.5,20,1.0,16.653002578165868,-91.67349871091707,8830,23.997734994337485,-0.38357581879982244,-240.9197266621369
95,1.5,20,1.0,24.927710059813517,-87.53614497009325,7408,29.306155507559396,-0.9520827117838521,-190.41061079110813
95,2.5,20,1.0,21.973918801798945,-89.01304059910052,7633,25.153936853137694,-0.7146688539676412,-194.44238080585185
95,3.5,20,1.0,60.57066438340541,-69.71466780829729,7806,23.7253394824494,-0.27195008259729253,-247.0975110088031
115,1.5,20,1.0,6.321018890365941,-96.83949055481703,6643,28.631642330272467,-1.0099053237536952,-206.58136634538567
115,2.5,20,1.0,10.376641254511714,-94.81167937274414,6839,24.681971048398886,-0.6364432358639216,-218.41178219528035
115,3.5,20,1.0,7.630256305421915,-96.18487184728905,6966,23.16968130921619,-0.5844894984911244,-221.95436668671815
1 period std period_step std_step final_eq ret_pct n_trades win_rate sharpe dd
2 15 1.5 10 0.5 11.855192165034223 -94.07240391748289 19708 37.77146336513091 -0.6235222422995786 -284.85443596149724
3 15 2.0 10 0.5 8.753359275898845 -95.62332036205058 20195 35.246348105966824 -0.7569673183718936 -263.2971212724792
4 15 2.5 10 0.5 9.803525418208569 -95.09823729089571 20708 32.639559590496425 -0.4752350103234552 -277.4245022765702
5 15 3.0 10 0.5 3.412780495824038 -98.29360975208797 21275 29.83783783783784 -0.511480053076418 -338.8119559579532
6 15 3.5 10 0.5 6.198174773117584 -96.9009126134412 21716 27.73531037023393 -0.5128705717437164 -313.48715997416474
7 15 4.0 10 0.5 20.69226809284817 -89.65386595357592 21948 26.571897211591033 -0.32674764882357116 -403.7400501883454
8 25 1.5 10 0.5 5.836491805389524 -97.08175409730524 15020 35.22636484687084 -1.2249549689127937 -204.09205005290724
9 25 2.0 10 0.5 12.081211963616443 -93.95939401819177 15311 32.577885180589114 -0.8898463515682434 -222.54350054966164
10 25 2.5 10 0.5 19.914003433039493 -90.04299828348026 15659 30.161568427102626 -0.7895621638697325 -209.1994100018314
11 25 3.0 10 0.5 9.425303324148096 -95.28734833792595 15976 28.067100650976464 -0.7300825335867858 -243.96197406916642
12 25 3.5 10 0.5 5.855289614688857 -97.07235519265558 16253 26.55509752045776 -0.7085359518779542 -278.87154684168763
13 25 4.0 10 0.5 19.200810578463244 -90.39959471076838 16495 25.407699302819037 -0.5772741076812997 -259.23352233813335
14 35 1.5 10 0.5 28.21206634200495 -85.89396682899752 12085 34.73727761688043 -0.647009511262925 -223.61914840751774
15 35 2.0 10 0.5 55.70970922376068 -72.14514538811966 12306 31.569965870307165 -0.39673167475158405 -248.8668646132019
16 35 2.5 10 0.5 104.49489503334085 -47.75255248332957 12520 29.249201277955272 -0.235045001015176 -230.00122971261297
17 35 3.0 10 0.5 56.342667499287515 -71.82866625035624 12752 27.40746549560853 -0.3682997075298054 -252.50663799518358
18 35 3.5 10 0.5 107.45577769881035 -46.272111150594824 12984 26.070548367221196 -0.11749439894681797 -338.48701280893147
19 35 4.0 10 0.5 247.34481984104798 23.67240992052399 13141 25.34053724982878 0.056546089336753404 -264.63887018550304
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period,std,period_step,std_step,final_eq,ret_pct,n_trades,win_rate,sharpe,dd
15,1.5,10,0.5,0.00047035550350871735,-99.99976482224825,111588,34.390794709108505,-1.0318566813340773,-202.38278172487549
15,2.0,10,0.5,0.0003682458531207367,-99.99981587707344,114983,31.99951297148274,-1.0517447664263284,-207.00926699542296
15,2.5,10,0.5,0.00013517790996149244,-99.99993241104502,118944,28.41841538875437,-1.066843300280861,-202.97365018191613
15,3.0,10,0.5,2.7372158257389396e-05,-99.99998631392087,123018,25.341819896275343,-1.0375855233149691,-203.97454793219083
15,3.5,10,0.5,7.669217720507588e-06,-99.99999616539114,125514,23.555141259142406,-1.0295116405913771,-199.9999926321606
15,4.0,10,0.5,1.3373229853368135e-06,-99.9999993313385,126581,22.765659933165324,-1.0295288560138371,-199.99999869386636
25,1.5,10,0.5,0.0017630495668879283,-99.99911847521656,84946,32.794952087208344,-1.293729889070109,-207.35144513711467
25,2.0,10,0.5,0.0021262699573547064,-99.99893686502132,87416,29.61471584149355,-1.0769254917914826,-207.3230697431621
25,2.5,10,0.5,0.019232972624929533,-99.99038351368753,89753,26.640892226443686,-0.7661744780665724,-282.9426200919278
25,3.0,10,0.5,0.008324747423078871,-99.99583762628846,92114,24.08537247323968,-0.7701299195822682,-299.3619517674745
25,3.5,10,0.5,0.0021990833038556443,-99.99890045834807,93887,22.321514160639918,-0.8594065630854474,-249.20948309430867
25,4.0,10,0.5,0.0006738628522136209,-99.9996630685739,95030,21.376407450278858,-0.7576562230433436,-291.98051861072224
35,1.5,10,0.5,0.02089403178818467,-99.98955298410591,72581,31.327757953183337,-1.3309409260309297,-202.064386268866
35,2.0,10,0.5,0.04911133191480452,-99.97544433404259,74592,27.870280995280993,-1.1788018107980864,-208.19318228650735
35,2.5,10,0.5,0.11339157723591194,-99.94330421138204,76304,25.05897462780457,-0.9891952503585197,-207.81448051914438
35,3.0,10,0.5,0.11968128814263472,-99.94015935592869,77841,22.939068100358423,-0.8139159656385352,-221.5376495451823
35,3.5,10,0.5,0.01984485951274019,-99.99007757024363,79196,21.3861811202586,-1.060687386107305,-230.0395662353739
35,4.0,10,0.5,0.012363126554740892,-99.99381843672263,80118,20.540952095658906,-0.9081750231226203,-210.33305195479676
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45,2.0,10,0.5,0.08115056060726777,-99.95942471969637,65616,26.502682272616436,-0.9579345891598869,-236.4862054858405
45,2.5,10,0.5,0.13538296683662002,-99.93230851658168,66915,23.782410520809982,-0.7075702450197628,-271.08910187835227
45,3.0,10,0.5,0.09969712703152057,-99.95015143648423,68156,21.86307881917953,-0.6451318177267918,-295.9747288571139
45,3.5,10,0.5,0.09210686086215344,-99.95394656956893,69136,20.51174496644295,-0.45887396866107066,-429.20953221378915
45,4.0,10,0.5,0.08145389297753457,-99.95927305351124,69869,19.75554251527859,-0.5082958377978372,-369.88002808773837
55,1.5,10,0.5,0.055918483619185166,-99.9720407581904,57008,29.073112545607632,-1.135641198208353,-205.86923282780617
55,2.0,10,0.5,0.09137017136829197,-99.95431491431586,58485,25.582628024279728,-1.0618048081366185,-210.73187077440718
55,2.5,10,0.5,0.15140570229310388,-99.92429714885346,59613,23.10402093503095,-0.9409011616127569,-214.74837340120598
55,3.0,10,0.5,0.19074157711726422,-99.90462921144137,60601,21.458391775713274,-0.7210538774678307,-228.38551279326268
55,3.5,10,0.5,0.266445127868473,-99.86677743606576,61404,20.28043775649795,-0.564524044727798,-243.9096107161547
55,4.0,10,0.5,0.26774779880824356,-99.86612610059588,62052,19.62064075291691,-0.48626128008345376,-293.3629257317168
65,1.5,10,0.5,0.04907015568532271,-99.97546492215734,52899,28.14230892833513,-1.1576453239098663,-199.95103322806455
65,2.0,10,0.5,0.09845537769869178,-99.95077231115066,54133,24.896089261633385,-0.9612794470061556,-201.98589389095932
65,2.5,10,0.5,0.14242537277814582,-99.92878731361093,55056,22.54068584713746,-1.0481822765622575,-217.92289691106637
65,3.0,10,0.5,0.21445168112609417,-99.89277415943695,55869,20.963324920796865,-0.9157350431731428,-217.07298744964024
65,3.5,10,0.5,0.11086043832824247,-99.94456978083588,56546,19.916528136384535,-0.889029811935134,-208.98004563743567
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75,2.5,10,0.5,0.6840327479120798,-99.65798362604396,51222,22.05302409121081,-0.8751293718771636,-251.58583670556357
75,3.0,10,0.5,1.0493683175491069,-99.47531584122545,51928,20.643968571868744,-0.8506313317219855,-240.16427918824203
75,3.5,10,0.5,0.3002907430136643,-99.84985462849316,52522,19.66604470507597,-0.9150694199211542,-228.47263285477916
75,4.0,10,0.5,0.5645103894047913,-99.7177448052976,52944,19.082426715019643,-0.5813832054508865,-339.28451518453875
85,1.5,10,0.5,0.02561939348725843,-99.98719030325637,46669,26.347254065868135,-1.3394559452187687,-199.97440286154722
85,2.0,10,0.5,0.027562363422181876,-99.9862188182889,47656,23.30871243914722,-1.6035376159684314,-199.97246157942988
85,2.5,10,0.5,0.06710236812371269,-99.96644881593815,48349,21.363420132784547,-1.3159475743389093,-200.5606978611736
85,3.0,10,0.5,0.06029185809930138,-99.96985407095035,48950,20.036772216547497,-1.1509308662867805,-202.6717296280356
85,3.5,10,0.5,0.054195810082062756,-99.97290209495897,49451,19.18869183636327,-0.9659646629970992,-203.09667435496505
85,4.0,10,0.5,0.0936191651022354,-99.95319041744888,49870,18.600360938439945,-0.6387572048825219,-267.1308034697755
95,1.5,10,0.5,0.0978095706248613,-99.95109521468757,44232,26.064839934888766,-1.6362452282909372,-203.01129606801078
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105,1.5,10,0.5,0.3107871173165156,-99.84460644134174,42457,25.289116046823846,-1.4384875040917713,-207.99320092200065
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105,2.5,10,0.5,0.6519741878688033,-99.6740129060656,43819,20.49111116182478,-1.3838762571726289,-211.58787241883724
105,3.0,10,0.5,0.6272088870954825,-99.68639555645225,44317,19.258975111131168,-1.1669183306576214,-214.76037206815255
105,3.5,10,0.5,0.48414610299791855,-99.75792694850104,44756,18.587451961748144,-1.097312680815389,-218.08694275448883
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115,1.5,10,0.5,0.2519762013578816,-99.87401189932106,40603,24.907026574391054,-1.2068532641982856,-203.55972685901094
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115,2.5,10,0.5,0.5104894066647994,-99.7447552966676,41895,20.179018976011456,-1.2283831092133597,-207.03468168503625
115,3.0,10,0.5,0.3267094948301192,-99.83664525258494,42350,19.053128689492326,-0.9353309323210536,-214.927636848525
115,3.5,10,0.5,0.4562186733246713,-99.77189066333766,42727,18.37713857748028,-0.8387344659201758,-212.4165991865744
115,4.0,10,0.5,0.804703304477972,-99.59764834776101,42999,17.967859717667853,-0.6328910463396727,-230.00767242563782
15,1.5,20,1.0,0.00047035550350871735,-99.99976482224825,111588,34.390794709108505,-1.0318566813340773,-202.38278172487549
15,2.5,20,1.0,0.00013517790996149244,-99.99993241104502,118944,28.41841538875437,-1.066843300280861,-202.97365018191613
15,3.5,20,1.0,7.669217720507588e-06,-99.99999616539114,125514,23.555141259142406,-1.0295116405913771,-199.9999926321606
35,1.5,20,1.0,0.02089403178818467,-99.98955298410591,72581,31.327757953183337,-1.3309409260309297,-202.064386268866
35,2.5,20,1.0,0.11339157723591194,-99.94330421138204,76304,25.05897462780457,-0.9891952503585197,-207.81448051914438
35,3.5,20,1.0,0.01984485951274019,-99.99007757024363,79196,21.3861811202586,-1.060687386107305,-230.0395662353739
55,1.5,20,1.0,0.055918483619185166,-99.9720407581904,57008,29.073112545607632,-1.135641198208353,-205.86923282780617
55,2.5,20,1.0,0.15140570229310388,-99.92429714885346,59613,23.10402093503095,-0.9409011616127569,-214.74837340120598
55,3.5,20,1.0,0.266445127868473,-99.86677743606576,61404,20.28043775649795,-0.564524044727798,-243.9096107161547
75,1.5,20,1.0,0.4210843943579031,-99.78945780282105,49258,27.441227820861585,-0.8547303664345759,-251.85239328153438
75,2.5,20,1.0,0.6840327479120798,-99.65798362604396,51222,22.05302409121081,-0.8751293718771636,-251.58583670556357
75,3.5,20,1.0,0.3002907430136643,-99.84985462849316,52522,19.66604470507597,-0.9150694199211542,-228.47263285477916
95,1.5,20,1.0,0.0978095706248613,-99.95109521468757,44232,26.064839934888766,-1.6362452282909372,-203.01129606801078
95,2.5,20,1.0,0.13353975402701052,-99.9332301229865,45683,21.053783683208195,-1.2834053444224973,-205.97722633464866
95,3.5,20,1.0,0.08490721133074189,-99.95754639433463,46731,19.066572510753033,-0.9476179854957475,-209.6450016389805
115,1.5,20,1.0,0.2519762013578816,-99.87401189932106,40603,24.907026574391054,-1.2068532641982856,-203.55972685901094
115,2.5,20,1.0,0.5104894066647994,-99.7447552966676,41895,20.179018976011456,-1.2283831092133597,-207.03468168503625
115,3.5,20,1.0,0.4562186733246713,-99.77189066333766,42727,18.37713857748028,-0.8387344659201758,-212.4165991865744
1 period std period_step std_step final_eq ret_pct n_trades win_rate sharpe dd
2 15 1.5 10 0.5 0.00047035550350871735 -99.99976482224825 111588 34.390794709108505 -1.0318566813340773 -202.38278172487549
3 15 2.0 10 0.5 0.0003682458531207367 -99.99981587707344 114983 31.99951297148274 -1.0517447664263284 -207.00926699542296
4 15 2.5 10 0.5 0.00013517790996149244 -99.99993241104502 118944 28.41841538875437 -1.066843300280861 -202.97365018191613
5 15 3.0 10 0.5 2.7372158257389396e-05 -99.99998631392087 123018 25.341819896275343 -1.0375855233149691 -203.97454793219083
6 15 3.5 10 0.5 7.669217720507588e-06 -99.99999616539114 125514 23.555141259142406 -1.0295116405913771 -199.9999926321606
7 15 4.0 10 0.5 1.3373229853368135e-06 -99.9999993313385 126581 22.765659933165324 -1.0295288560138371 -199.99999869386636
8 25 1.5 10 0.5 0.0017630495668879283 -99.99911847521656 84946 32.794952087208344 -1.293729889070109 -207.35144513711467
9 25 2.0 10 0.5 0.0021262699573547064 -99.99893686502132 87416 29.61471584149355 -1.0769254917914826 -207.3230697431621
10 25 2.5 10 0.5 0.019232972624929533 -99.99038351368753 89753 26.640892226443686 -0.7661744780665724 -282.9426200919278
11 25 3.0 10 0.5 0.008324747423078871 -99.99583762628846 92114 24.08537247323968 -0.7701299195822682 -299.3619517674745
12 25 3.5 10 0.5 0.0021990833038556443 -99.99890045834807 93887 22.321514160639918 -0.8594065630854474 -249.20948309430867
13 25 4.0 10 0.5 0.0006738628522136209 -99.9996630685739 95030 21.376407450278858 -0.7576562230433436 -291.98051861072224
14 35 1.5 10 0.5 0.02089403178818467 -99.98955298410591 72581 31.327757953183337 -1.3309409260309297 -202.064386268866
15 35 2.0 10 0.5 0.04911133191480452 -99.97544433404259 74592 27.870280995280993 -1.1788018107980864 -208.19318228650735
16 35 2.5 10 0.5 0.11339157723591194 -99.94330421138204 76304 25.05897462780457 -0.9891952503585197 -207.81448051914438
17 35 3.0 10 0.5 0.11968128814263472 -99.94015935592869 77841 22.939068100358423 -0.8139159656385352 -221.5376495451823
18 35 3.5 10 0.5 0.01984485951274019 -99.99007757024363 79196 21.3861811202586 -1.060687386107305 -230.0395662353739
19 35 4.0 10 0.5 0.012363126554740892 -99.99381843672263 80118 20.540952095658906 -0.9081750231226203 -210.33305195479676
20 45 1.5 10 0.5 0.06804612639104632 -99.96597693680448 63854 30.02474394712939 -1.129416637835604 -199.9327591253644
21 45 2.0 10 0.5 0.08115056060726777 -99.95942471969637 65616 26.502682272616436 -0.9579345891598869 -236.4862054858405
22 45 2.5 10 0.5 0.13538296683662002 -99.93230851658168 66915 23.782410520809982 -0.7075702450197628 -271.08910187835227
23 45 3.0 10 0.5 0.09969712703152057 -99.95015143648423 68156 21.86307881917953 -0.6451318177267918 -295.9747288571139
24 45 3.5 10 0.5 0.09210686086215344 -99.95394656956893 69136 20.51174496644295 -0.45887396866107066 -429.20953221378915
25 45 4.0 10 0.5 0.08145389297753457 -99.95927305351124 69869 19.75554251527859 -0.5082958377978372 -369.88002808773837
26 55 1.5 10 0.5 0.055918483619185166 -99.9720407581904 57008 29.073112545607632 -1.135641198208353 -205.86923282780617
27 55 2.0 10 0.5 0.09137017136829197 -99.95431491431586 58485 25.582628024279728 -1.0618048081366185 -210.73187077440718
28 55 2.5 10 0.5 0.15140570229310388 -99.92429714885346 59613 23.10402093503095 -0.9409011616127569 -214.74837340120598
29 55 3.0 10 0.5 0.19074157711726422 -99.90462921144137 60601 21.458391775713274 -0.7210538774678307 -228.38551279326268
30 55 3.5 10 0.5 0.266445127868473 -99.86677743606576 61404 20.28043775649795 -0.564524044727798 -243.9096107161547
31 55 4.0 10 0.5 0.26774779880824356 -99.86612610059588 62052 19.62064075291691 -0.48626128008345376 -293.3629257317168
32 65 1.5 10 0.5 0.04907015568532271 -99.97546492215734 52899 28.14230892833513 -1.1576453239098663 -199.95103322806455
33 65 2.0 10 0.5 0.09845537769869178 -99.95077231115066 54133 24.896089261633385 -0.9612794470061556 -201.98589389095932
34 65 2.5 10 0.5 0.14242537277814582 -99.92878731361093 55056 22.54068584713746 -1.0481822765622575 -217.92289691106637
35 65 3.0 10 0.5 0.21445168112609417 -99.89277415943695 55869 20.963324920796865 -0.9157350431731428 -217.07298744964024
36 65 3.5 10 0.5 0.11086043832824247 -99.94456978083588 56546 19.916528136384535 -0.889029811935134 -208.98004563743567
37 65 4.0 10 0.5 0.13368760731622228 -99.93315619634188 57073 19.287579065407463 -0.6873579796541707 -224.09354725742426
38 75 1.5 10 0.5 0.4210843943579031 -99.78945780282105 49258 27.441227820861585 -0.8547303664345759 -251.85239328153438
39 75 2.0 10 0.5 0.4517921443623139 -99.77410392781884 50455 24.164106629670005 -0.6584921622071749 -294.1643492057131
40 75 2.5 10 0.5 0.6840327479120798 -99.65798362604396 51222 22.05302409121081 -0.8751293718771636 -251.58583670556357
41 75 3.0 10 0.5 1.0493683175491069 -99.47531584122545 51928 20.643968571868744 -0.8506313317219855 -240.16427918824203
42 75 3.5 10 0.5 0.3002907430136643 -99.84985462849316 52522 19.66604470507597 -0.9150694199211542 -228.47263285477916
43 75 4.0 10 0.5 0.5645103894047913 -99.7177448052976 52944 19.082426715019643 -0.5813832054508865 -339.28451518453875
44 85 1.5 10 0.5 0.02561939348725843 -99.98719030325637 46669 26.347254065868135 -1.3394559452187687 -199.97440286154722
45 85 2.0 10 0.5 0.027562363422181876 -99.9862188182889 47656 23.30871243914722 -1.6035376159684314 -199.97246157942988
46 85 2.5 10 0.5 0.06710236812371269 -99.96644881593815 48349 21.363420132784547 -1.3159475743389093 -200.5606978611736
47 85 3.0 10 0.5 0.06029185809930138 -99.96985407095035 48950 20.036772216547497 -1.1509308662867805 -202.6717296280356
48 85 3.5 10 0.5 0.054195810082062756 -99.97290209495897 49451 19.18869183636327 -0.9659646629970992 -203.09667435496505
49 85 4.0 10 0.5 0.0936191651022354 -99.95319041744888 49870 18.600360938439945 -0.6387572048825219 -267.1308034697755
50 95 1.5 10 0.5 0.0978095706248613 -99.95109521468757 44232 26.064839934888766 -1.6362452282909372 -203.01129606801078
51 95 2.0 10 0.5 0.09155064140685701 -99.95422467929657 45102 22.967939337501665 -1.4580521919101679 -199.97896885909833
52 95 2.5 10 0.5 0.13353975402701052 -99.9332301229865 45683 21.053783683208195 -1.2834053444224973 -205.97722633464866
53 95 3.0 10 0.5 0.11922415974801172 -99.940387920126 46243 19.793266007828212 -1.3127594636083004 -209.93002471500728
54 95 3.5 10 0.5 0.08490721133074189 -99.95754639433463 46731 19.066572510753033 -0.9476179854957475 -209.6450016389805
55 95 4.0 10 0.5 0.12790328947294202 -99.93604835526352 47067 18.550151911105445 -0.751921932535252 -215.34297603401436
56 105 1.5 10 0.5 0.3107871173165156 -99.84460644134174 42457 25.289116046823846 -1.4384875040917713 -207.99320092200065
57 105 2.0 10 0.5 0.4348322314181436 -99.78258388429093 43239 22.33862947801753 -1.3783320940275678 -207.42263371527645
58 105 2.5 10 0.5 0.6519741878688033 -99.6740129060656 43819 20.49111116182478 -1.3838762571726289 -211.58787241883724
59 105 3.0 10 0.5 0.6272088870954825 -99.68639555645225 44317 19.258975111131168 -1.1669183306576214 -214.76037206815255
60 105 3.5 10 0.5 0.48414610299791855 -99.75792694850104 44756 18.587451961748144 -1.097312680815389 -218.08694275448883
61 105 4.0 10 0.5 0.9238007100788466 -99.53809964496058 45088 18.071327182398864 -0.766833644617431 -228.08378402449856
62 115 1.5 10 0.5 0.2519762013578816 -99.87401189932106 40603 24.907026574391054 -1.2068532641982856 -203.55972685901094
63 115 2.0 10 0.5 0.24640178809971616 -99.87679910595014 41362 21.938010734490597 -1.1265561819825711 -205.1211003357915
64 115 2.5 10 0.5 0.5104894066647994 -99.7447552966676 41895 20.179018976011456 -1.2283831092133597 -207.03468168503625
65 115 3.0 10 0.5 0.3267094948301192 -99.83664525258494 42350 19.053128689492326 -0.9353309323210536 -214.927636848525
66 115 3.5 10 0.5 0.4562186733246713 -99.77189066333766 42727 18.37713857748028 -0.8387344659201758 -212.4165991865744
67 115 4.0 10 0.5 0.804703304477972 -99.59764834776101 42999 17.967859717667853 -0.6328910463396727 -230.00767242563782
68 15 1.5 20 1.0 0.00047035550350871735 -99.99976482224825 111588 34.390794709108505 -1.0318566813340773 -202.38278172487549
69 15 2.5 20 1.0 0.00013517790996149244 -99.99993241104502 118944 28.41841538875437 -1.066843300280861 -202.97365018191613
70 15 3.5 20 1.0 7.669217720507588e-06 -99.99999616539114 125514 23.555141259142406 -1.0295116405913771 -199.9999926321606
71 35 1.5 20 1.0 0.02089403178818467 -99.98955298410591 72581 31.327757953183337 -1.3309409260309297 -202.064386268866
72 35 2.5 20 1.0 0.11339157723591194 -99.94330421138204 76304 25.05897462780457 -0.9891952503585197 -207.81448051914438
73 35 3.5 20 1.0 0.01984485951274019 -99.99007757024363 79196 21.3861811202586 -1.060687386107305 -230.0395662353739
74 55 1.5 20 1.0 0.055918483619185166 -99.9720407581904 57008 29.073112545607632 -1.135641198208353 -205.86923282780617
75 55 2.5 20 1.0 0.15140570229310388 -99.92429714885346 59613 23.10402093503095 -0.9409011616127569 -214.74837340120598
76 55 3.5 20 1.0 0.266445127868473 -99.86677743606576 61404 20.28043775649795 -0.564524044727798 -243.9096107161547
77 75 1.5 20 1.0 0.4210843943579031 -99.78945780282105 49258 27.441227820861585 -0.8547303664345759 -251.85239328153438
78 75 2.5 20 1.0 0.6840327479120798 -99.65798362604396 51222 22.05302409121081 -0.8751293718771636 -251.58583670556357
79 75 3.5 20 1.0 0.3002907430136643 -99.84985462849316 52522 19.66604470507597 -0.9150694199211542 -228.47263285477916
80 95 1.5 20 1.0 0.0978095706248613 -99.95109521468757 44232 26.064839934888766 -1.6362452282909372 -203.01129606801078
81 95 2.5 20 1.0 0.13353975402701052 -99.9332301229865 45683 21.053783683208195 -1.2834053444224973 -205.97722633464866
82 95 3.5 20 1.0 0.08490721133074189 -99.95754639433463 46731 19.066572510753033 -0.9476179854957475 -209.6450016389805
83 115 1.5 20 1.0 0.2519762013578816 -99.87401189932106 40603 24.907026574391054 -1.2068532641982856 -203.55972685901094
84 115 2.5 20 1.0 0.5104894066647994 -99.7447552966676 41895 20.179018976011456 -1.2283831092133597 -207.03468168503625
85 115 3.5 20 1.0 0.4562186733246713 -99.77189066333766 42727 18.37713857748028 -0.8387344659201758 -212.4165991865744

87702
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period,std,final_eq,ret_pct,n_trades,win_rate,sharpe,max_dd_u,max_dd_pct,stable_score
20,2.0,0.0042263950973378305,-99.99788680245133,87701,33.69858952577508,-0.8399134626089371,-265.2264490104231,132.61322450521155,-216.16742795792783
1 period std final_eq ret_pct n_trades win_rate sharpe max_dd_u max_dd_pct stable_score
2 20 2.0 0.0042263950973378305 -99.99788680245133 87701 33.69858952577508 -0.8399134626089371 -265.2264490104231 132.61322450521155 -216.16742795792783

101
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@@ -1,101 +0,0 @@
period,std,final_eq,ret_pct,n_trades,win_rate,sharpe,max_dd_u,max_dd_pct,stable_score
141,1.0,0.3725631525960213,-99.81371842370199,30049,32.88628573330227,-1.1419908723322958,-239.4883338177086,119.7441669088543,-209.312942418773
141,3.0,0.8935583586833639,-99.55322082065832,30102,22.692844329280444,-1.0449970179309893,-226.953389102229,113.4766945511145,-202.8745406767218
141,5.0,2.5724707153438344,-98.71376464232809,30090,21.06679960119641,-0.5885453783225604,-297.2986371543968,148.6493185771984,-224.69576404395752
141,7.0,3.4685697259990484,-98.26571513700047,30092,20.72643892064336,-0.48007699971208917,-328.79731669397,164.398658346985,-235.54556581113354
141,9.0,3.580904571423253,-98.20954771428838,30091,20.66730916220797,-0.49161721726796526,-306.39410158641846,153.19705079320923,-226.66659495607135
141,11.0,3.9783828811208326,-98.01080855943958,30091,20.657339403808447,-0.48652196781648543,-307.6960797832785,153.84803989163925,-226.92750408654882
141,13.0,3.9305433487109616,-98.03472832564452,30091,20.65401615100861,-0.48196282506779964,-311.2585673086382,155.6292836543191,-228.3217091499134
141,15.0,4.237395487302333,-97.88130225634883,30091,20.65401615100861,-0.48117309142726505,-311.1693851846781,155.58469259233905,-228.12313342734726
141,17.0,4.1877548957065605,-97.90612255214673,30091,20.65401615100861,-0.4813053584937705,-311.19057123284057,155.59528561642028,-228.15801534720822
141,19.0,4.1489686043404,-97.9255156978298,30091,20.65401615100861,-0.4814041115675331,-311.207124787311,155.6035623936555,-228.1852149515646
181,1.0,1.54541089852939,-99.22729455073531,25902,31.850822330321982,-1.199437232610771,-208.37766382491262,104.18883191245631,-196.9716068720296
181,3.0,1.9965918755290404,-99.00170406223548,25928,22.39663684048133,-1.0619028735115128,-233.36576433437043,116.68288216718521,-205.0908442781218
181,5.0,8.071401651585806,-95.9642991742071,25919,20.965315019869593,-0.6710765949697802,-240.5263670750946,120.2631835375473,-200.22776514388232
181,7.0,13.682622486061769,-93.15868875696911,25914,20.71853052404106,-0.4523005061492697,-325.41220228547587,162.70610114273794,-228.7511757449507
181,9.0,13.060274116014678,-93.46986294199266,25913,20.66144406282561,-0.45908377193167316,-323.2236062913858,161.6118031456929,-228.26831072172706
181,11.0,13.68249600732716,-93.15875199633642,25913,20.646007795315093,-0.4571038290855216,-324.09815757538547,162.04907878769274,-228.28326097551687
181,13.0,13.695219818316474,-93.15239009084176,25913,20.646007795315093,-0.455292429728791,-325.93245317752525,162.96622658876262,-228.98888051859734
181,15.0,13.67098609342226,-93.16450695328886,25913,20.646007795315093,-0.4553571498005006,-325.93961655899017,162.96980827949508,-229.00463937449092
181,17.0,13.426859791565604,-93.28657010421719,25913,20.646007795315093,-0.45602310778001637,-326.0117792097359,163.00588960486795,-229.16355908147176
181,19.0,13.426859791565604,-93.28657010421719,25913,20.646007795315093,-0.45602310778001637,-326.0117792097359,163.00588960486795,-229.16355908147176
121,1.0,0.6316466226049364,-99.68417668869753,33004,33.78378378378378,-1.052222338701853,-206.42730524680505,103.21365262340252,-194.8817668518418
121,3.0,0.6213813515500181,-99.689309324225,33066,23.099256033387768,-0.9034975192511878,-214.48595875045243,107.24297937522623,-196.32566305542025
121,5.0,1.9136436948828712,-99.04317815255857,33056,21.484753146176185,-0.5982068102544683,-211.51121036008402,105.75560518004201,-190.82614401964582
121,7.0,1.0735613492988372,-99.46321932535058,33046,21.103915753797736,-0.4968590955505304,-244.79461181542908,122.39730590771454,-203.34337319812857
121,9.0,1.1663959453321875,-99.4168020273339,33046,21.040367971917934,-0.491645833390951,-247.9707541823954,123.9853770911977,-204.5048537009835
121,11.0,1.2183278724323423,-99.39083606378382,33046,21.031289717363673,-0.4933546135117468,-247.94934993910346,123.97467496955173,-204.49083140156617
121,13.0,1.2425260804680303,-99.37873695976599,33046,21.028263632512257,-0.4874890223174935,-251.45344300781414,125.72672150390707,-205.80998243070158
121,15.0,1.2856620112079782,-99.357168994396,33046,21.028263632512257,-0.4873534021489805,-251.41190743169045,125.70595371584523,-205.77017279285997
121,17.0,1.2515397754677258,-99.37423011226613,33046,21.028263632512257,-0.4874574412622979,-251.4447637233199,125.72238186165995,-205.80162489674169
121,19.0,1.2605473760065111,-99.36972631199674,33046,21.028263632512257,-0.4868456824111407,-251.43609030718045,125.71804515359023,-205.78631062380262
101,1.0,1.3812151149048713,-99.30939244254756,36046,34.84436553293014,-1.0513559683073899,-211.596726954287,105.79836347714348,-196.56435484395104
101,3.0,1.3107125459371607,-99.34464372703142,36135,23.616991836169916,-0.8487417539167138,-219.7768214710855,109.88841073554273,-197.4402733624662
101,5.0,6.3255050108003585,-96.83724749459982,36121,21.726973228869635,-0.4279048943481762,-310.0263705615421,155.01318528077104,-225.98265445139478
101,7.0,2.898681370453997,-98.550659314773,36119,21.346105927628116,-0.5105396352220445,-266.04726570364727,133.02363285182363,-211.09604121889646
101,9.0,2.337590220963109,-98.83120488951845,36119,21.276890279354358,-0.4946838636498477,-266.9034571984829,133.45172859924145,-211.52879413270978
101,11.0,2.248555784854723,-98.87572210757264,36119,21.257509897837704,-0.49917680860743774,-266.9359298883164,133.4679649441582,-211.64021576618848
101,13.0,2.498694325239585,-98.7506528373802,36119,21.254741271906752,-0.49039344526005274,-271.61493372988264,135.80746686494132,-213.28134767245388
101,15.0,2.4446207313192856,-98.77768963434036,36119,21.254741271906752,-0.49053481242017677,-271.6323210497848,135.8161605248924,-213.31703580329642
101,17.0,2.46021630053702,-98.7698918497315,36119,21.254741271906752,-0.4895899161479696,-271.6273063072573,135.81365315362865,-213.29589336641004
101,19.0,2.46021630053702,-98.7698918497315,36119,21.254741271906752,-0.4895899161479696,-271.6273063072573,135.81365315362865,-213.29589336641004
81,1.0,0.38789258931728443,-99.80605370534136,40428,35.93054318788958,-1.3592208304573201,-203.78168899414376,101.89084449707188,-197.6293792684867
81,3.0,2.35707122036968,-98.82146438981516,40594,24.326255111592847,-1.0223276542135724,-209.41404598106254,104.70702299053127,-194.85501463280306
81,5.0,2.738798055863725,-98.63060097206814,40574,22.06092571597575,-0.612968955468472,-226.175262272745,113.08763113637251,-196.4563333467878
81,7.0,1.6379035690835946,-99.1810482154582,40564,21.721230647865102,-0.6189674329263939,-232.6156018912471,116.30780094562357,-199.65489816707378
81,9.0,1.5795652645096294,-99.21021736774519,40559,21.664735323849207,-0.6049640177737161,-233.1884392554897,116.59421962774485,-199.74516128322568
81,11.0,1.4844472673119984,-99.257776366344,40559,21.652407603737764,-0.6068398317520862,-233.2567175159563,116.62835875797815,-199.84254135375159
81,13.0,1.6060637766271089,-99.19696811168645,40558,21.650475861728882,-0.598769408587746,-233.1961706190891,116.59808530954454,-199.66066926237505
81,15.0,1.5941017930091896,-99.2029491034954,40558,21.650475861728882,-0.5978375540333052,-233.20318933464523,116.6015946673226,-199.65827548575314
81,17.0,1.5919727231579546,-99.20401363842102,40558,21.650475861728882,-0.597844544661025,-233.20443857023713,116.60221928511857,-199.65992360244817
81,19.0,1.5919727231579546,-99.20401363842102,40558,21.650475861728882,-0.597844544661025,-233.20443857023713,116.60221928511857,-199.65992360244817
61,1.0,0.18588972434247641,-99.90705513782876,46603,37.351672639100485,-1.271435744311454,-199.8161496463976,99.9080748231988,-195.09074392812525
61,3.0,1.2411584337304506,-99.37942078313478,46919,25.1049681365758,-1.002261040309346,-198.90841062139575,99.45420531069787,-190.96991751540523
61,5.0,1.2648197669081618,-99.36759011654591,46875,22.5664,-0.7628705030782208,-199.20933748136585,99.60466874068292,-188.2057711460309
61,7.0,0.5589298789988087,-99.7205350605006,46863,22.19448178733756,-0.7542253286061638,-199.61902667123155,99.80951333561578,-188.61884967226717
61,9.0,0.6392894290548907,-99.68035528547256,46855,22.149183651691388,-0.7325411704462591,-202.84398590545504,101.4219929527275,-189.6084436930097
61,11.0,0.6976965475421673,-99.65115172622892,46853,22.137323116982905,-0.7221597770072495,-205.6043321758009,102.80216608790045,-190.55880192063626
61,13.0,0.6606675247637739,-99.66966623761812,46852,22.131392469905233,-0.7288789587814761,-205.63860970568084,102.8193048528404,-190.67165762526815
61,15.0,0.6921328687861904,-99.6539335656069,46852,22.131392469905233,-0.7250593802377309,-205.60948243768982,102.80474121884491,-190.5984391035356
61,17.0,0.6711409318082459,-99.66442953409587,46852,22.131392469905233,-0.7467447191672053,-201.21777452725777,100.60888726362889,-189.11247597500545
61,19.0,0.6711409318082459,-99.66442953409587,46852,22.131392469905233,-0.7467447191672053,-201.21777452725777,100.60888726362889,-189.11247597500545
41,1.0,0.015212549931630836,-99.99239372503419,58232,38.022049732106055,-0.9675643564696258,-201.18916338481642,100.5945816924082,-192.07883135659625
41,3.0,0.05802476361942974,-99.97098761819028,58921,25.78367644812546,-0.8141023181668979,-221.8357360102845,110.91786800514225,-198.47450984030687
41,5.0,0.018204245993222193,-99.99089787700339,58828,22.76806962670837,-0.7747373783069786,-220.02688832306336,110.01344416153167,-197.29850174591246
41,7.0,0.018803260658613055,-99.99059836967069,58800,22.43877551020408,-0.7516083628047471,-213.19677775293198,106.598388876466,-194.28860982450044
41,9.0,0.019308294015552056,-99.99034585299222,58793,22.393822393822393,-0.7274306410639146,-220.4007030959921,110.20035154799605,-196.87979478415605
41,11.0,0.01806066289798151,-99.990969668551,58793,22.387018862789787,-0.728322811635155,-220.38697020477568,110.19348510238784,-196.88563149008314
41,13.0,0.01727499608890596,-99.99136250195555,58794,22.378133823179237,-0.7291549517475395,-220.38774928750541,110.1938746437527,-196.8963216379282
41,15.0,0.017696144371817355,-99.99115192781409,58794,22.378133823179237,-0.7285582364048866,-220.38733166854774,110.19366583427387,-196.88878343209183
41,17.0,0.017696144371817355,-99.99115192781409,58794,22.378133823179237,-0.7285582364048866,-220.38733166854774,110.19366583427387,-196.88878343209183
41,19.0,0.017696144371817355,-99.99115192781409,58794,22.378133823179237,-0.7285582364048866,-220.38733166854774,110.19366583427387,-196.88878343209183
161,1.0,0.18507916598399116,-99.90746041700801,27807,32.272449383248826,-1.3626921206535092,-208.8205005280496,104.4102502640248,-199.78796607606995
161,3.0,0.23998103803793114,-99.88000948098103,27834,22.53359200977222,-1.3100826718988585,-210.56993164514623,105.28496582257311,-199.82897420182582
161,5.0,0.5855126564436772,-99.70724367177816,27825,21.02785265049416,-0.92014274909802,-209.8497421239102,104.92487106195509,-194.68885351051847
161,7.0,0.47603821222660997,-99.7619808938867,27824,20.741086831512366,-0.8540499785557677,-222.3354842636233,111.16774213181164,-198.94477434200525
161,9.0,0.5528348080519955,-99.72358259597401,27824,20.68717653824037,-0.8742066357356877,-222.36440030194234,111.18220015097118,-199.15982234557922
161,11.0,0.5547483846886698,-99.72262580765566,27823,20.677137619954713,-0.8745006016030721,-222.3500000989885,111.17500004949426,-199.15663306648793
161,13.0,0.5642197176946141,-99.7178901411527,27823,20.67354347122884,-0.8685846686060622,-222.34258574695468,111.17129287347734,-199.0779404632073
161,15.0,0.5973160858428005,-99.7013419570786,27823,20.67354347122884,-0.8684350979936863,-222.3166772371032,111.1583386185516,-199.0492340278441
161,17.0,0.5931270699461915,-99.70343646502691,27823,20.67354347122884,-0.8684544122038876,-222.31995648391177,111.1599782419559,-199.0528720050383
161,19.0,0.5869955515964692,-99.70650222420177,27823,20.67354347122884,-0.8684828298480397,-222.32475636113264,111.16237818056631,-199.0581987268313
21,1.0,0.0012545307129279897,-99.99937273464353,83039,38.97686629174243,-1.2206218969570914,-214.4358469291487,107.21792346457435,-200.4211742697881
21,3.0,0.003141325327724193,-99.99842933733613,85276,27.505980580702662,-0.611161009432778,-288.68540178116535,144.34270089058268,-222.80652216299563
21,5.0,0.0004631489929338503,-99.99976842550353,84980,24.166862791245,-0.5990293997070005,-257.5217756663234,128.7608878331617,-210.19683148851692
21,7.0,0.0005214659574309736,-99.99973926702128,84944,23.981682049350162,-0.5113764484639733,-295.2633541495073,147.63167707475364,-224.24159830839187
21,9.0,0.0004554326656295686,-99.99977228366718,84945,23.968450173641767,-0.5117181252434727,-295.0636443124399,147.53182215621996,-224.16584751156483
21,11.0,0.0004365337751968878,-99.9997817331124,84945,23.960209547354168,-0.512010867309432,-295.0636628918621,147.53183144593106,-224.16937729757046
21,13.0,0.0004331898842896685,-99.99978340505785,84946,23.95757304640595,-0.5120799195015522,-295.06366617922765,147.53183308961383,-224.17020891076754
21,15.0,0.0004331388233741457,-99.99978343058831,84946,23.95757304640595,-0.512079919638494,-295.06366622942545,147.53183311471273,-224.17020895802042
21,17.0,0.0004331388233741457,-99.99978343058831,84946,23.95757304640595,-0.512079919638494,-295.06366622942545,147.53183311471273,-224.17020895802042
21,19.0,0.00043289925579133006,-99.9997835503721,84947,23.957291016751622,-0.5120799202809959,-295.06366646494337,147.53183323247168,-224.1702091797214
1,1.0,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,3.0,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,5.0,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,7.0,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,9.0,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,11.0,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,13.0,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,15.0,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,17.0,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1,19.0,7.332091932035972e-15,-100.0,314852,34.19479628523877,-1.9254884054263868,-199.9,99.95,-203.06586086511663
1 period std final_eq ret_pct n_trades win_rate sharpe max_dd_u max_dd_pct stable_score
2 141 1.0 0.3725631525960213 -99.81371842370199 30049 32.88628573330227 -1.1419908723322958 -239.4883338177086 119.7441669088543 -209.312942418773
3 141 3.0 0.8935583586833639 -99.55322082065832 30102 22.692844329280444 -1.0449970179309893 -226.953389102229 113.4766945511145 -202.8745406767218
4 141 5.0 2.5724707153438344 -98.71376464232809 30090 21.06679960119641 -0.5885453783225604 -297.2986371543968 148.6493185771984 -224.69576404395752
5 141 7.0 3.4685697259990484 -98.26571513700047 30092 20.72643892064336 -0.48007699971208917 -328.79731669397 164.398658346985 -235.54556581113354
6 141 9.0 3.580904571423253 -98.20954771428838 30091 20.66730916220797 -0.49161721726796526 -306.39410158641846 153.19705079320923 -226.66659495607135
7 141 11.0 3.9783828811208326 -98.01080855943958 30091 20.657339403808447 -0.48652196781648543 -307.6960797832785 153.84803989163925 -226.92750408654882
8 141 13.0 3.9305433487109616 -98.03472832564452 30091 20.65401615100861 -0.48196282506779964 -311.2585673086382 155.6292836543191 -228.3217091499134
9 141 15.0 4.237395487302333 -97.88130225634883 30091 20.65401615100861 -0.48117309142726505 -311.1693851846781 155.58469259233905 -228.12313342734726
10 141 17.0 4.1877548957065605 -97.90612255214673 30091 20.65401615100861 -0.4813053584937705 -311.19057123284057 155.59528561642028 -228.15801534720822
11 141 19.0 4.1489686043404 -97.9255156978298 30091 20.65401615100861 -0.4814041115675331 -311.207124787311 155.6035623936555 -228.1852149515646
12 181 1.0 1.54541089852939 -99.22729455073531 25902 31.850822330321982 -1.199437232610771 -208.37766382491262 104.18883191245631 -196.9716068720296
13 181 3.0 1.9965918755290404 -99.00170406223548 25928 22.39663684048133 -1.0619028735115128 -233.36576433437043 116.68288216718521 -205.0908442781218
14 181 5.0 8.071401651585806 -95.9642991742071 25919 20.965315019869593 -0.6710765949697802 -240.5263670750946 120.2631835375473 -200.22776514388232
15 181 7.0 13.682622486061769 -93.15868875696911 25914 20.71853052404106 -0.4523005061492697 -325.41220228547587 162.70610114273794 -228.7511757449507
16 181 9.0 13.060274116014678 -93.46986294199266 25913 20.66144406282561 -0.45908377193167316 -323.2236062913858 161.6118031456929 -228.26831072172706
17 181 11.0 13.68249600732716 -93.15875199633642 25913 20.646007795315093 -0.4571038290855216 -324.09815757538547 162.04907878769274 -228.28326097551687
18 181 13.0 13.695219818316474 -93.15239009084176 25913 20.646007795315093 -0.455292429728791 -325.93245317752525 162.96622658876262 -228.98888051859734
19 181 15.0 13.67098609342226 -93.16450695328886 25913 20.646007795315093 -0.4553571498005006 -325.93961655899017 162.96980827949508 -229.00463937449092
20 181 17.0 13.426859791565604 -93.28657010421719 25913 20.646007795315093 -0.45602310778001637 -326.0117792097359 163.00588960486795 -229.16355908147176
21 181 19.0 13.426859791565604 -93.28657010421719 25913 20.646007795315093 -0.45602310778001637 -326.0117792097359 163.00588960486795 -229.16355908147176
22 121 1.0 0.6316466226049364 -99.68417668869753 33004 33.78378378378378 -1.052222338701853 -206.42730524680505 103.21365262340252 -194.8817668518418
23 121 3.0 0.6213813515500181 -99.689309324225 33066 23.099256033387768 -0.9034975192511878 -214.48595875045243 107.24297937522623 -196.32566305542025
24 121 5.0 1.9136436948828712 -99.04317815255857 33056 21.484753146176185 -0.5982068102544683 -211.51121036008402 105.75560518004201 -190.82614401964582
25 121 7.0 1.0735613492988372 -99.46321932535058 33046 21.103915753797736 -0.4968590955505304 -244.79461181542908 122.39730590771454 -203.34337319812857
26 121 9.0 1.1663959453321875 -99.4168020273339 33046 21.040367971917934 -0.491645833390951 -247.9707541823954 123.9853770911977 -204.5048537009835
27 121 11.0 1.2183278724323423 -99.39083606378382 33046 21.031289717363673 -0.4933546135117468 -247.94934993910346 123.97467496955173 -204.49083140156617
28 121 13.0 1.2425260804680303 -99.37873695976599 33046 21.028263632512257 -0.4874890223174935 -251.45344300781414 125.72672150390707 -205.80998243070158
29 121 15.0 1.2856620112079782 -99.357168994396 33046 21.028263632512257 -0.4873534021489805 -251.41190743169045 125.70595371584523 -205.77017279285997
30 121 17.0 1.2515397754677258 -99.37423011226613 33046 21.028263632512257 -0.4874574412622979 -251.4447637233199 125.72238186165995 -205.80162489674169
31 121 19.0 1.2605473760065111 -99.36972631199674 33046 21.028263632512257 -0.4868456824111407 -251.43609030718045 125.71804515359023 -205.78631062380262
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65 41 7.0 0.018803260658613055 -99.99059836967069 58800 22.43877551020408 -0.7516083628047471 -213.19677775293198 106.598388876466 -194.28860982450044
66 41 9.0 0.019308294015552056 -99.99034585299222 58793 22.393822393822393 -0.7274306410639146 -220.4007030959921 110.20035154799605 -196.87979478415605
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68 41 13.0 0.01727499608890596 -99.99136250195555 58794 22.378133823179237 -0.7291549517475395 -220.38774928750541 110.1938746437527 -196.8963216379282
69 41 15.0 0.017696144371817355 -99.99115192781409 58794 22.378133823179237 -0.7285582364048866 -220.38733166854774 110.19366583427387 -196.88878343209183
70 41 17.0 0.017696144371817355 -99.99115192781409 58794 22.378133823179237 -0.7285582364048866 -220.38733166854774 110.19366583427387 -196.88878343209183
71 41 19.0 0.017696144371817355 -99.99115192781409 58794 22.378133823179237 -0.7285582364048866 -220.38733166854774 110.19366583427387 -196.88878343209183
72 161 1.0 0.18507916598399116 -99.90746041700801 27807 32.272449383248826 -1.3626921206535092 -208.8205005280496 104.4102502640248 -199.78796607606995
73 161 3.0 0.23998103803793114 -99.88000948098103 27834 22.53359200977222 -1.3100826718988585 -210.56993164514623 105.28496582257311 -199.82897420182582
74 161 5.0 0.5855126564436772 -99.70724367177816 27825 21.02785265049416 -0.92014274909802 -209.8497421239102 104.92487106195509 -194.68885351051847
75 161 7.0 0.47603821222660997 -99.7619808938867 27824 20.741086831512366 -0.8540499785557677 -222.3354842636233 111.16774213181164 -198.94477434200525
76 161 9.0 0.5528348080519955 -99.72358259597401 27824 20.68717653824037 -0.8742066357356877 -222.36440030194234 111.18220015097118 -199.15982234557922
77 161 11.0 0.5547483846886698 -99.72262580765566 27823 20.677137619954713 -0.8745006016030721 -222.3500000989885 111.17500004949426 -199.15663306648793
78 161 13.0 0.5642197176946141 -99.7178901411527 27823 20.67354347122884 -0.8685846686060622 -222.34258574695468 111.17129287347734 -199.0779404632073
79 161 15.0 0.5973160858428005 -99.7013419570786 27823 20.67354347122884 -0.8684350979936863 -222.3166772371032 111.1583386185516 -199.0492340278441
80 161 17.0 0.5931270699461915 -99.70343646502691 27823 20.67354347122884 -0.8684544122038876 -222.31995648391177 111.1599782419559 -199.0528720050383
81 161 19.0 0.5869955515964692 -99.70650222420177 27823 20.67354347122884 -0.8684828298480397 -222.32475636113264 111.16237818056631 -199.0581987268313
82 21 1.0 0.0012545307129279897 -99.99937273464353 83039 38.97686629174243 -1.2206218969570914 -214.4358469291487 107.21792346457435 -200.4211742697881
83 21 3.0 0.003141325327724193 -99.99842933733613 85276 27.505980580702662 -0.611161009432778 -288.68540178116535 144.34270089058268 -222.80652216299563
84 21 5.0 0.0004631489929338503 -99.99976842550353 84980 24.166862791245 -0.5990293997070005 -257.5217756663234 128.7608878331617 -210.19683148851692
85 21 7.0 0.0005214659574309736 -99.99973926702128 84944 23.981682049350162 -0.5113764484639733 -295.2633541495073 147.63167707475364 -224.24159830839187
86 21 9.0 0.0004554326656295686 -99.99977228366718 84945 23.968450173641767 -0.5117181252434727 -295.0636443124399 147.53182215621996 -224.16584751156483
87 21 11.0 0.0004365337751968878 -99.9997817331124 84945 23.960209547354168 -0.512010867309432 -295.0636628918621 147.53183144593106 -224.16937729757046
88 21 13.0 0.0004331898842896685 -99.99978340505785 84946 23.95757304640595 -0.5120799195015522 -295.06366617922765 147.53183308961383 -224.17020891076754
89 21 15.0 0.0004331388233741457 -99.99978343058831 84946 23.95757304640595 -0.512079919638494 -295.06366622942545 147.53183311471273 -224.17020895802042
90 21 17.0 0.0004331388233741457 -99.99978343058831 84946 23.95757304640595 -0.512079919638494 -295.06366622942545 147.53183311471273 -224.17020895802042
91 21 19.0 0.00043289925579133006 -99.9997835503721 84947 23.957291016751622 -0.5120799202809959 -295.06366646494337 147.53183323247168 -224.1702091797214
92 1 1.0 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
93 1 3.0 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
94 1 5.0 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
95 1 7.0 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
96 1 9.0 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
97 1 11.0 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
98 1 13.0 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
99 1 15.0 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
100 1 17.0 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663
101 1 19.0 7.332091932035972e-15 -100.0 314852 34.19479628523877 -1.9254884054263868 -199.9 99.95 -203.06586086511663

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strategy/results/bb_midline_hier_search_5m_20260226_192735.csv
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7
strategy/results/bb_midline_hier_search_5m_20260226_192735_yearly.csv
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@@ -1,7 +0,0 @@
year,year_end_equity,year_return_pct
2020,54.050106322289274,-72.97494683885536
2021,94.65774624824195,75.12962080743739
2022,200.96942517386574,112.31165238903867
2023,141.40095297115792,-29.64056455412212
2024,192.12283166096822,35.87095958268133
2025,243.61657139606652,26.802509254062695
1 year year_end_equity year_return_pct
2 2020 54.050106322289274 -72.97494683885536
3 2021 94.65774624824195 75.12962080743739
4 2022 200.96942517386574 112.31165238903867
5 2023 141.40095297115792 -29.64056455412212
6 2024 192.12283166096822 35.87095958268133
7 2025 243.61657139606652 26.802509254062695

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strategy/results/bb_midline_param_sweep.csv
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@@ -1,33 +0,0 @@
period,std,final_eq,ret_pct,n_trades,win_rate,sharpe,dd
10,1.5,0.05342370290338611,-99.9732881485483,65278,36.77808756395723,-1.5827730890254355,-200.11090187995964
10,2.0,0.01988664648055615,-99.99005667675972,69456,37.21348767565077,-1.375492398950596,-200.74729198772366
10,2.5,0.004545923852164151,-99.99772703807392,74138,37.50033720898864,-1.2284719936927497,-201.33569201183727
10,3.0,0.0009123838658130715,-99.99954380806709,76146,37.615895779161086,-1.6590563717126823,-202.30746831252432
20,1.5,0.3782564097262717,-99.81087179513686,47456,37.41992582602832,-1.773963308419704,-204.25419944748978
20,2.0,0.10116775050634881,-99.94941612474683,49854,37.58374453403939,-1.6215139967794863,-207.24649370462032
20,2.5,0.0420847966759596,-99.97895760166202,51496,38.30588783594842,-1.8732479488082094,-202.88341805372127
20,3.0,0.03714319731018452,-99.98142840134491,52399,39.06563102349281,-1.7117920644966065,-201.30652434882285
30,1.5,1.4879534896660909,-99.25602325516695,39618,37.46529355343531,-1.0597237275155744,-241.7741860257891
30,2.0,0.5206306817787774,-99.73968465911061,41418,37.77343184122845,-1.2705250571479818,-204.3989052518405
30,2.5,0.08578933249176414,-99.95710533375411,42437,38.86938284987158,-1.4392780573843966,-208.66548353770028
30,3.0,0.06761980742073989,-99.96619009628964,42819,39.68331815315631,-1.24928580482028,-216.1250835959552
50,1.5,1.8088706722788732,-99.09556466386056,31443,37.334223833603666,-1.4000644569639418,-202.32460954374
50,2.0,1.028036476208118,-99.48598176189594,32460,38.44423906346272,-1.2716921237889525,-200.83851685504973
50,2.5,0.11541810526712795,-99.94229094736643,32939,39.5306475606424,-1.203251914380095,-200.27701570363715
50,3.0,0.1351670804614327,-99.93241645976929,33085,40.54405319631253,-1.0704484513755306,-200.26052595255356
80,1.5,4.150889743129027,-97.92455512843549,25302,38.02861433878745,-1.6665676083991399,-202.37237995021502
80,2.0,5.992075618322869,-97.00396219083856,25933,39.2704276404581,-1.2873048988895401,-222.45880619181088
80,2.5,2.3099195066108917,-98.84504024669455,26064,40.29696132596685,-1.2180479656844296,-242.45465429040132
80,3.0,2.334754168161419,-98.83262291591929,26050,41.051823416506714,-1.0505864510447513,-269.054135429288
100,1.5,6.04121936461079,-96.97939031769461,22786,38.181339418941455,-1.4175370742512177,-196.92102711681954
100,2.0,5.423127145984888,-97.28843642700755,23191,39.51101720495019,-1.2153007501059072,-231.1710791247756
100,2.5,2.610862380586256,-98.69456880970687,23301,40.483241062615335,-1.1094891363618633,-259.30276840213105
100,3.0,5.716308119598341,-97.14184594020082,23299,41.37945834585175,-0.7610979701772258,-288.02851880803166
150,1.5,6.121437150276749,-96.93928142486162,18407,38.88194708534797,-1.002800854613305,-241.71726284052002
150,2.0,1.1970630383174046,-99.4014684808413,18595,40.112933584296854,-1.1681812761761492,-224.23744764512085
150,2.5,0.5963506942019386,-99.70182465289903,18625,41.261744966442954,-0.8184845317905666,-263.25717616484644
150,3.0,0.6302294940489122,-99.68488525297555,18578,42.00129185057595,-0.8539049301221916,-284.17449718087295
200,1.5,11.879732518914734,-94.06013374054264,15536,39.624098867147275,-0.966813276208611,-190.0398595048643
200,2.0,7.102255587565593,-96.4488722062172,15612,40.949269792467334,-1.0272918700545648,-195.52766791647502
200,2.5,7.996108371386221,-96.0019458143069,15618,42.00281726213343,-0.8066524896070545,-212.59859294532936
200,3.0,12.272570900469248,-93.86371454976538,15593,42.57038414673251,-0.6873433542160784,-197.05958687744663
1 period std final_eq ret_pct n_trades win_rate sharpe dd
2 10 1.5 0.05342370290338611 -99.9732881485483 65278 36.77808756395723 -1.5827730890254355 -200.11090187995964
3 10 2.0 0.01988664648055615 -99.99005667675972 69456 37.21348767565077 -1.375492398950596 -200.74729198772366
4 10 2.5 0.004545923852164151 -99.99772703807392 74138 37.50033720898864 -1.2284719936927497 -201.33569201183727
5 10 3.0 0.0009123838658130715 -99.99954380806709 76146 37.615895779161086 -1.6590563717126823 -202.30746831252432
6 20 1.5 0.3782564097262717 -99.81087179513686 47456 37.41992582602832 -1.773963308419704 -204.25419944748978
7 20 2.0 0.10116775050634881 -99.94941612474683 49854 37.58374453403939 -1.6215139967794863 -207.24649370462032
8 20 2.5 0.0420847966759596 -99.97895760166202 51496 38.30588783594842 -1.8732479488082094 -202.88341805372127
9 20 3.0 0.03714319731018452 -99.98142840134491 52399 39.06563102349281 -1.7117920644966065 -201.30652434882285
10 30 1.5 1.4879534896660909 -99.25602325516695 39618 37.46529355343531 -1.0597237275155744 -241.7741860257891
11 30 2.0 0.5206306817787774 -99.73968465911061 41418 37.77343184122845 -1.2705250571479818 -204.3989052518405
12 30 2.5 0.08578933249176414 -99.95710533375411 42437 38.86938284987158 -1.4392780573843966 -208.66548353770028
13 30 3.0 0.06761980742073989 -99.96619009628964 42819 39.68331815315631 -1.24928580482028 -216.1250835959552
14 50 1.5 1.8088706722788732 -99.09556466386056 31443 37.334223833603666 -1.4000644569639418 -202.32460954374
15 50 2.0 1.028036476208118 -99.48598176189594 32460 38.44423906346272 -1.2716921237889525 -200.83851685504973
16 50 2.5 0.11541810526712795 -99.94229094736643 32939 39.5306475606424 -1.203251914380095 -200.27701570363715
17 50 3.0 0.1351670804614327 -99.93241645976929 33085 40.54405319631253 -1.0704484513755306 -200.26052595255356
18 80 1.5 4.150889743129027 -97.92455512843549 25302 38.02861433878745 -1.6665676083991399 -202.37237995021502
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31 200 2.0 7.102255587565593 -96.4488722062172 15612 40.949269792467334 -1.0272918700545648 -195.52766791647502
32 200 2.5 7.996108371386221 -96.0019458143069 15618 42.00281726213343 -0.8066524896070545 -212.59859294532936
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period,std,final_eq,ret_pct,n_trades,win_rate,sharpe,max_dd_u,max_dd_pct,stable_score
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901,350.5,563.79,181.9,10270,19.56,0.0934,-1726.97,863.49,-507.77
901,400.5,563.79,181.9,10270,19.56,0.0934,-1726.97,863.49,-507.77
901,450.5,563.79,181.9,10270,19.56,0.0934,-1726.97,863.49,-507.77
901,500.5,563.79,181.9,10270,19.56,0.0934,-1726.97,863.49,-507.77
901,550.5,563.79,181.9,10270,19.56,0.0934,-1726.97,863.49,-507.77
901,600.5,563.79,181.9,10270,19.56,0.0934,-1726.97,863.49,-507.77
901,650.5,563.79,181.9,10270,19.56,0.0934,-1726.97,863.49,-507.77
901,700.5,563.79,181.9,10270,19.56,0.0934,-1726.97,863.49,-507.77
901,750.5,563.79,181.9,10270,19.56,0.0934,-1726.97,863.49,-507.77
901,800.5,563.79,181.9,10270,19.56,0.0934,-1726.97,863.49,-507.77
901,850.5,563.79,181.9,10270,19.56,0.0934,-1726.97,863.49,-507.77
901,900.5,563.79,181.9,10270,19.56,0.0934,-1726.97,863.49,-507.77
901,950.5,563.79,181.9,10270,19.56,0.0934,-1726.97,863.49,-507.77
1 period std final_eq ret_pct n_trades win_rate sharpe max_dd_u max_dd_pct stable_score
2 251 0.5 1.26 -99.37 21094 37.48 -1.1405 -199.63 99.82 -192.91
3 251 50.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
4 251 100.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
5 251 150.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
6 251 200.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
7 251 250.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
8 251 300.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
9 251 350.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
10 251 400.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
11 251 450.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
12 251 500.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
13 251 550.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
14 251 600.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
15 251 650.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
16 251 700.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
17 251 750.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
18 251 800.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
19 251 850.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
20 251 900.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
21 251 950.5 11.52 -94.24 21200 20.43 -0.4469 -243.29 121.65 -196.92
22 201 0.5 0.9 -99.55 24082 38.36 -1.1894 -201.26 100.63 -194.33
23 201 50.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
24 201 100.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
25 201 150.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
26 201 200.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
27 201 250.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
28 201 300.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
29 201 350.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
30 201 400.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
31 201 450.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
32 201 500.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
33 201 550.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
34 201 600.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
35 201 650.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
36 201 700.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
37 201 750.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
38 201 800.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
39 201 850.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
40 201 900.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
41 201 950.5 9.72 -95.14 24243 20.74 -0.8051 -220.74 110.37 -193.1
42 101 0.5 2.93 -98.53 35484 40.7 -1.0208 -200.53 100.27 -191.0
43 101 50.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
44 101 100.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
45 101 150.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
46 101 200.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
47 101 250.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
48 101 300.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
49 101 350.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
50 101 400.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
51 101 450.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
52 101 500.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
53 101 550.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
54 101 600.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
55 101 650.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
56 101 700.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
57 101 750.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
58 101 800.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
59 101 850.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
60 101 900.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
61 101 950.5 2.46 -98.77 36119 21.25 -0.4896 -271.63 135.81 -213.3
62 151 0.5 0.45 -99.78 28965 38.93 -1.1185 -203.05 101.52 -194.42
63 151 50.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
64 151 100.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
65 151 150.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
66 151 200.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
67 151 250.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
68 151 300.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
69 151 350.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
70 151 400.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
71 151 450.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
72 151 500.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
73 151 550.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
74 151 600.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
75 151 650.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
76 151 700.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
77 151 750.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
78 151 800.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
79 151 850.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
80 151 900.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
81 151 950.5 1.03 -99.48 29301 20.34 -0.5489 -298.09 149.05 -225.31
82 51 0.5 0.26 -99.87 50009 41.39 -1.3186 -200.91 100.46 -196.05
83 51 50.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
84 51 100.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
85 51 150.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
86 51 200.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
87 51 250.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
88 51 300.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
89 51 350.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
90 51 400.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
91 51 450.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
92 51 500.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
93 51 550.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
94 51 600.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
95 51 650.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
96 51 700.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
97 51 750.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
98 51 800.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
99 51 850.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
100 51 900.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
101 51 950.5 0.1 -99.95 51874 22.13 -0.8994 -213.73 106.87 -196.24
102 1 0.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
103 1 50.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
104 1 100.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
105 1 150.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
106 1 200.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
107 1 250.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
108 1 300.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
109 1 350.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
110 1 400.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
111 1 450.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
112 1 500.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
113 1 550.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
114 1 600.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
115 1 650.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
116 1 700.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
117 1 750.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
118 1 800.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
119 1 850.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
120 1 900.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
121 1 950.5 0.0 -100.0 314852 34.19 -1.9255 -199.9 99.95 -203.07
122 301 0.5 3.29 -98.36 18999 36.79 -0.9796 -210.35 105.17 -194.25
123 301 50.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
124 301 100.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
125 301 150.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
126 301 200.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
127 301 250.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
128 301 300.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
129 301 350.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
130 301 400.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
131 301 450.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
132 301 500.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
133 301 550.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
134 301 600.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
135 301 650.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
136 301 700.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
137 301 750.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
138 301 800.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
139 301 850.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
140 301 900.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
141 301 950.5 52.84 -73.58 19058 20.5 -0.3757 -226.55 113.28 -168.71
142 351 0.5 3.36 -98.32 17810 36.27 -1.221 -197.11 98.55 -191.81
143 351 50.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
144 351 100.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
145 351 150.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
146 351 200.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
147 351 250.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
148 351 300.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
149 351 350.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
150 351 400.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
151 351 450.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
152 351 500.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
153 351 550.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
154 351 600.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
155 351 650.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
156 351 700.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
157 351 750.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
158 351 800.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
159 351 850.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
160 351 900.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
161 351 950.5 9.61 -95.2 17857 19.48 -0.6357 -200.97 100.48 -183.21
162 401 0.5 3.01 -98.5 17114 34.98 -1.2456 -197.42 98.71 -192.41
163 401 50.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
164 401 100.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
165 401 150.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
166 401 200.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
167 401 250.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
168 401 300.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
169 401 350.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
170 401 400.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
171 401 450.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
172 401 500.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
173 401 550.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
174 401 600.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
175 401 650.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
176 401 700.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
177 401 750.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
178 401 800.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
179 401 850.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
180 401 900.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
181 401 950.5 6.01 -97.0 17153 19.41 -0.5546 -218.38 109.19 -191.01
182 451 0.5 2.33 -98.84 16156 34.27 -1.124 -199.45 99.73 -192.1
183 451 50.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
184 451 100.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
185 451 150.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
186 451 200.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
187 451 250.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
188 451 300.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
189 451 350.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
190 451 400.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
191 451 450.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
192 451 500.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
193 451 550.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
194 451 600.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
195 451 650.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
196 451 700.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
197 451 750.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
198 451 800.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
199 451 850.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
200 451 900.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
201 451 950.5 3.18 -98.41 16171 19.46 -0.5248 -231.2 115.6 -197.19
202 501 0.5 1.9 -99.05 15372 33.37 -0.8822 -198.31 99.16 -188.96
203 501 50.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
204 501 100.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
205 501 150.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
206 501 200.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
207 501 250.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
208 501 300.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
209 501 350.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
210 501 400.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
211 501 450.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
212 501 500.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
213 501 550.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
214 501 600.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
215 501 650.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
216 501 700.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
217 501 750.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
218 501 800.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
219 501 850.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
220 501 900.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
221 501 950.5 2.07 -98.97 15381 19.29 -0.5183 -256.66 128.33 -207.85
222 551 0.5 4.52 -97.74 14150 33.36 -0.9378 -198.07 99.03 -188.22
223 551 50.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
224 551 100.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
225 551 150.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
226 551 200.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
227 551 250.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
228 551 300.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
229 551 350.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
230 551 400.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
231 551 450.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
232 551 500.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
233 551 550.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
234 551 600.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
235 551 650.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
236 551 700.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
237 551 750.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
238 551 800.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
239 551 850.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
240 551 900.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
241 551 950.5 11.44 -94.28 14159 19.62 -0.5181 -224.63 112.31 -190.35
242 601 0.5 3.15 -98.43 13683 32.54 -0.7494 -247.88 123.94 -206.57
243 601 50.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
244 601 100.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
245 601 150.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
246 601 200.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
247 601 250.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
248 601 300.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
249 601 350.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
250 601 400.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
251 601 450.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
252 601 500.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
253 601 550.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
254 601 600.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
255 601 650.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
256 601 700.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
257 601 750.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
258 601 800.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
259 601 850.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
260 601 900.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
261 601 950.5 3.06 -98.47 13680 19.18 -0.1846 -553.04 276.52 -321.9
262 651 0.5 8.02 -95.99 13136 32.14 -0.8123 -232.77 116.39 -198.85
263 651 50.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
264 651 100.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
265 651 150.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
266 651 200.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
267 651 250.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
268 651 300.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
269 651 350.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
270 651 400.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
271 651 450.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
272 651 500.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
273 651 550.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
274 651 600.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
275 651 650.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
276 651 700.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
277 651 750.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
278 651 800.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
279 651 850.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
280 651 900.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
281 651 950.5 48.8 -75.6 13142 19.53 -0.1411 -535.69 267.85 -291.57
282 701 0.5 1.6 -99.2 12485 31.23 -0.8874 -198.89 99.45 -189.4
283 701 50.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
284 701 100.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
285 701 150.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
286 701 200.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
287 701 250.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
288 701 300.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
289 701 350.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
290 701 400.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
291 701 450.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
292 701 500.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
293 701 550.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
294 701 600.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
295 701 650.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
296 701 700.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
297 701 750.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
298 701 800.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
299 701 850.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
300 701 900.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
301 701 950.5 14.5 -92.75 12501 19.6 -0.1569 -643.67 321.83 -352.1
302 801 0.5 10.35 -94.83 11410 31.07 -0.8074 -208.99 104.49 -188.11
303 801 50.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
304 801 100.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
305 801 150.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
306 801 200.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
307 801 250.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
308 801 300.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
309 801 350.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
310 801 400.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
311 801 450.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
312 801 500.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
313 801 550.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
314 801 600.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
315 801 650.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
316 801 700.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
317 801 750.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
318 801 800.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
319 801 850.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
320 801 900.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
321 801 950.5 118.75 -40.62 11418 19.45 -0.0405 -1080.04 540.02 -473.13
322 751 0.5 6.79 -96.61 11769 31.7 -0.8974 -208.02 104.01 -190.58
323 751 50.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
324 751 100.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
325 751 150.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
326 751 200.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
327 751 250.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
328 751 300.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
329 751 350.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
330 751 400.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
331 751 450.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
332 751 500.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
333 751 550.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
334 751 600.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
335 751 650.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
336 751 700.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
337 751 750.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
338 751 800.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
339 751 850.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
340 751 900.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
341 751 950.5 127.53 -36.23 11780 19.9 -0.1076 -345.62 172.81 -175.77
342 851 0.5 13.14 -93.43 10724 31.9 -0.8458 -197.01 98.5 -182.38
343 851 50.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
344 851 100.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
345 851 150.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
346 851 200.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
347 851 250.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
348 851 300.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
349 851 350.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
350 851 400.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
351 851 450.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
352 851 500.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
353 851 550.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
354 851 600.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
355 851 650.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
356 851 700.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
357 851 750.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
358 851 800.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
359 851 850.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
360 851 900.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
361 851 950.5 316.6 58.3 10734 20.19 0.0626 -1385.53 692.77 -495.16
362 951 0.5 2.59 -98.71 9665 30.17 -0.804 -230.09 115.04 -200.39
363 951 50.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
364 951 100.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
365 951 150.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
366 951 200.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
367 951 250.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
368 951 300.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
369 951 350.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
370 951 400.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
371 951 450.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
372 951 500.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
373 951 550.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
374 951 600.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
375 951 650.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
376 951 700.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
377 951 750.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
378 951 800.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
379 951 850.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
380 951 900.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
381 951 950.5 79.31 -60.35 9672 19.24 -0.1026 -623.51 311.76 -310.98
382 901 0.5 13.45 -93.27 10264 30.67 -0.8953 -189.6 94.8 -179.86
383 901 50.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
384 901 100.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
385 901 150.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
386 901 200.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
387 901 250.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
388 901 300.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
389 901 350.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
390 901 400.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
391 901 450.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
392 901 500.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
393 901 550.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
394 901 600.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
395 901 650.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
396 901 700.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
397 901 750.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
398 901 800.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
399 901 850.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
400 901 900.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77
401 901 950.5 563.79 181.9 10270 19.56 0.0934 -1726.97 863.49 -507.77

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"""
BB(10, 2.5) 均值回归策略回测 — 15分钟K线 | 2020-2025 | 200U | 万五手续费 | 90%返佣次日8点到账
"""
import sys, time
sys.stdout.reconfigure(line_buffering=True)
sys.path.insert(0, str(__import__("pathlib").Path(__file__).resolve().parents[1]))
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from pathlib import Path
from strategy.bb_backtest import BBConfig, run_bb_backtest
from strategy.data_loader import load_klines
out_dir = Path(__file__).resolve().parent / "results"
out_dir.mkdir(parents=True, exist_ok=True)
# ============================================================
# 加载 15 分钟 K 线数据 (2020-2025)
# ============================================================
YEARS = list(range(2020, 2026))
data = {}
print("加载 15 分钟 K 线数据 (2020-2025)...")
t0 = time.time()
for y in YEARS:
end_year = y + 1 if y < 2025 else 2026 # 2025 数据到 2026-01-01 前
df = load_klines('15m', f'{y}-01-01', f'{end_year}-01-01')
data[y] = df
print(f" {y}: {len(df):>7,} 条 ({df.index[0]} ~ {df.index[-1]})")
print(f"数据加载完成 ({time.time()-t0:.1f}s)\n")
# ============================================================
# 配置: 200U | 万五手续费 | 90%返佣次日8点到账
# ============================================================
cfg = BBConfig(
bb_period=10,
bb_std=2.5,
leverage=50,
initial_capital=200.0,
margin_pct=0.01, # 1% 权益/单
max_daily_loss=50.0, # 固定值备用
max_daily_loss_pct=0.05, # 日亏损上限 = 当日起始权益的 5%
fee_rate=0.0005, # 万五 (0.05%) 开平仓各万五
rebate_rate=0.0,
rebate_pct=0.90, # 90% 手续费返佣
rebate_hour_utc=0, # UTC 0点 = 北京时间早上8点到账
# 强平
liq_enabled=True,
maint_margin_rate=0.005, # 0.5% 维持保证金率
# 滑点
slippage_pct=0.0005, # 0.05% 滑点
# 市场容量限制
max_notional=500000.0, # 单笔最大名义价值 50万U
# 加仓
pyramid_enabled=True,
pyramid_step=0.01, # 递增加仓
pyramid_max=3,
)
# ============================================================
# 运行回测 (滚仓: 200U 起，逐年累加不复位)
# ============================================================
df_full = pd.concat([data[y] for y in YEARS])
r_full = run_bb_backtest(df_full, cfg)
d_full = r_full.daily_stats
eq_full = d_full["equity"].astype(float)
pnl_full = d_full["pnl"].astype(float)
eq_curve = r_full.equity_curve["equity"].dropna()
final_eq = float(eq_full.iloc[-1])
ret_pct = (final_eq - 200) / 200 * 100
dd_full = float((eq_full - eq_full.cummax()).min())
sharpe_full = float(pnl_full.mean() / pnl_full.std()) * np.sqrt(365) if pnl_full.std() > 0 else 0
win_full = sum(1 for t in r_full.trades if t.net_pnl > 0) / max(len(r_full.trades), 1) * 100
print("=" * 100)
print(" BB(10, 2.5) 15分钟K线 | 200U 起滚仓 | 万五 fee | 90% 返佣 | 含强平+滑点+容量限制")
print("=" * 100)
liq_count = sum(1 for t in r_full.trades if t.fee == 0.0 and t.net_pnl < 0)
print(f" 初始本金: 200U | 最终权益: {final_eq:,.0f}U | 收益率: {ret_pct:+,.1f}%")
print(f" 交易次数: {len(r_full.trades)} | 胜率: {win_full:.1f}% | 强平次数: {liq_count} | 最大回撤: {dd_full:+,.0f}U")
print(f" 总手续费: {r_full.total_fee:,.0f} | 总返佣: {r_full.total_rebate:,.0f} | Sharpe: {sharpe_full:.2f}")
print("-" * 100)
print(" 年末权益:")
for y in YEARS:
mask = eq_curve.index.year == y
if mask.any():
yr_eq = float(eq_curve.loc[mask].iloc[-1])
print(f" {y} 年末: {yr_eq:,.0f}U")
print("=" * 100)
# ============================================================
# 生成权益曲线图 (对数坐标，滚仓复利)
# ============================================================
fig, ax = plt.subplots(1, 1, figsize=(14, 6), dpi=120)
eq_ser = eq_curve
days = (eq_ser.index - eq_ser.index[0]).total_seconds() / 86400
ax.semilogy(days, eq_ser.values.clip(min=1), color="#2563eb", linewidth=1.2)
ax.axhline(y=200, color="gray", linestyle="--", alpha=0.6)
ax.set_title("BB(10, 2.5) 15min | 200U | 0.05% fee 90% rebate | 2020-2025", fontsize=12, fontweight="bold")
ax.set_xlabel("Days")
ax.set_ylabel("Equity (USDT, log scale)")
ax.grid(True, alpha=0.3)
chart_path = out_dir / "bb_15m_200u_2020_2025.png"
plt.savefig(chart_path, bbox_inches="tight")
print(f"\n图表已保存: {chart_path}")

323
strategy/run_bb_15m_2020_2025_conservative.py
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@@ -1,323 +0,0 @@
"""
Conservative BB backtest for bb_trade-style logic.
Assumptions:
1) Use 15m OHLC from 2020-01-01 to 2026-01-01 (exclusive).
2) Bollinger band is computed from CLOSED bars (shifted by 1 bar).
3) If bar i touches band, execute on bar i+1 open (no same-bar fill).
4) Fee: 0.05% each side; rebate: 90% of daily fee, credited next day at 08:00 UTC+8 (UTC 00:00).
5) Position sizing: 1% equity open, then +1%/+2%/+3% add ladder (max 3 adds).
6) Leverage 50x; optional liquidation model enabled.
"""
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
import sys
import numpy as np
import pandas as pd
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from strategy.data_loader import load_klines
@dataclass
class ConservativeConfig:
bb_period: int = 10
bb_std: float = 2.5
leverage: float = 50.0
initial_capital: float = 200.0
margin_pct: float = 0.01
pyramid_step: float = 0.01
pyramid_max: int = 3
fee_rate: float = 0.0005
rebate_pct: float = 0.90
rebate_hour_utc: int = 0
slippage_pct: float = 0.0005
liq_enabled: bool = True
maint_margin_rate: float = 0.005
max_daily_loss: float = 50.0
def bollinger_prev_close(close: pd.Series, period: int, n_std: float) -> tuple[np.ndarray, np.ndarray]:
mid = close.rolling(period).mean().shift(1)
std = close.rolling(period).std(ddof=0).shift(1)
upper = (mid + n_std * std).to_numpy(dtype=float)
lower = (mid - n_std * std).to_numpy(dtype=float)
return upper, lower
def run_conservative(df: pd.DataFrame, cfg: ConservativeConfig):
arr_open = df["open"].to_numpy(dtype=float)
arr_high = df["high"].to_numpy(dtype=float)
arr_low = df["low"].to_numpy(dtype=float)
arr_close = df["close"].to_numpy(dtype=float)
idx = df.index
n = len(df)
upper, lower = bollinger_prev_close(df["close"].astype(float), cfg.bb_period, cfg.bb_std)
balance = cfg.initial_capital
position = 0 # +1 long, -1 short
entry_price = 0.0
entry_qty = 0.0
entry_margin = 0.0
pyramid_count = 0
total_fee = 0.0
total_rebate = 0.0
trades = 0
win_trades = 0
liq_count = 0
day_fees = {}
current_day = None
day_start_equity = cfg.initial_capital
day_pnl = 0.0
day_stopped = False
rebate_applied_today = False
equity_arr = np.full(n, np.nan)
position_arr = np.zeros(n)
def unrealised(price: float) -> float:
if position == 0:
return 0.0
if position == 1:
return entry_qty * (price - entry_price)
return entry_qty * (entry_price - price)
def apply_open_slippage(side: int, price: float) -> float:
# Buy higher, sell lower
return price * (1 + cfg.slippage_pct) if side == 1 else price * (1 - cfg.slippage_pct)
def apply_close_slippage(side: int, price: float) -> float:
# Close long = sell lower; close short = buy higher
return price * (1 - cfg.slippage_pct) if side == 1 else price * (1 + cfg.slippage_pct)
def add_fee(ts: pd.Timestamp, fee: float):
nonlocal total_fee
total_fee += fee
d = ts.date()
day_fees[d] = day_fees.get(d, 0.0) + fee
def close_position(exec_price: float, exec_idx: int):
nonlocal balance, position, entry_price, entry_qty, entry_margin, pyramid_count
nonlocal trades, win_trades, day_pnl
if position == 0:
return
px = apply_close_slippage(position, exec_price)
gross = entry_qty * (px - entry_price) if position == 1 else entry_qty * (entry_price - px)
notional = entry_qty * px
fee = notional * cfg.fee_rate
net = gross - fee
balance += net
add_fee(idx[exec_idx], fee)
day_pnl += net
trades += 1
if net > 0:
win_trades += 1
position = 0
entry_price = 0.0
entry_qty = 0.0
entry_margin = 0.0
pyramid_count = 0
def open_position(side: int, exec_price: float, exec_idx: int, is_add: bool = False):
nonlocal balance, position, entry_price, entry_qty, entry_margin, pyramid_count
nonlocal day_pnl
px = apply_open_slippage(side, exec_price)
eq = balance + unrealised(px)
if is_add:
margin = eq * (cfg.margin_pct + cfg.pyramid_step * (pyramid_count + 1))
else:
margin = eq * cfg.margin_pct
margin = min(margin, balance * 0.95)
if margin <= 0:
return
notional = margin * cfg.leverage
qty = notional / px
fee = notional * cfg.fee_rate
balance -= fee
add_fee(idx[exec_idx], fee)
day_pnl -= fee
if is_add and position != 0:
old_notional = entry_qty * entry_price
new_notional = qty * px
entry_qty += qty
entry_price = (old_notional + new_notional) / entry_qty
entry_margin += margin
pyramid_count += 1
else:
position = side
entry_price = px
entry_qty = qty
entry_margin = margin
pyramid_count = 0
for i in range(n - 1):
ts = idx[i]
bar_day = ts.date()
if bar_day != current_day:
current_day = bar_day
day_start_equity = balance + unrealised(arr_close[i])
day_pnl = 0.0
day_stopped = False
rebate_applied_today = False
if (not rebate_applied_today) and ts.hour >= cfg.rebate_hour_utc:
prev_day = bar_day - pd.Timedelta(days=1)
prev_fee = day_fees.get(prev_day, 0.0)
if prev_fee > 0:
rebate = prev_fee * cfg.rebate_pct
balance += rebate
total_rebate += rebate
rebate_applied_today = True
if not day_stopped:
if day_pnl + unrealised(arr_close[i]) <= -cfg.max_daily_loss:
close_position(arr_open[i + 1], i + 1)
day_stopped = True
if cfg.liq_enabled and position != 0 and entry_margin > 0:
liq_threshold = 1.0 / cfg.leverage * (1 - cfg.maint_margin_rate)
if position == 1:
liq_price = entry_price * (1 - liq_threshold)
if arr_low[i] <= liq_price:
balance -= entry_margin
day_pnl -= entry_margin
position = 0
entry_price = 0.0
entry_qty = 0.0
entry_margin = 0.0
pyramid_count = 0
trades += 1
liq_count += 1
elif position == -1:
liq_price = entry_price * (1 + liq_threshold)
if arr_high[i] >= liq_price:
balance -= entry_margin
day_pnl -= entry_margin
position = 0
entry_price = 0.0
entry_qty = 0.0
entry_margin = 0.0
pyramid_count = 0
trades += 1
liq_count += 1
if day_stopped or np.isnan(upper[i]) or np.isnan(lower[i]):
equity_arr[i] = balance + unrealised(arr_close[i])
position_arr[i] = position
if balance <= 0:
balance = 0.0
equity_arr[i:] = 0.0
position_arr[i:] = 0
break
continue
touched_upper = arr_high[i] >= upper[i]
touched_lower = arr_low[i] <= lower[i]
exec_px = arr_open[i + 1]
if touched_upper and touched_lower:
pass
elif touched_upper:
if position == 1:
close_position(exec_px, i + 1)
if position == 0:
open_position(-1, exec_px, i + 1, is_add=False)
elif position == -1 and pyramid_count < cfg.pyramid_max:
open_position(-1, exec_px, i + 1, is_add=True)
elif touched_lower:
if position == -1:
close_position(exec_px, i + 1)
if position == 0:
open_position(1, exec_px, i + 1, is_add=False)
elif position == 1 and pyramid_count < cfg.pyramid_max:
open_position(1, exec_px, i + 1, is_add=True)
equity_arr[i] = balance + unrealised(arr_close[i])
position_arr[i] = position
if balance <= 0:
balance = 0.0
equity_arr[i:] = 0.0
position_arr[i:] = 0
break
if position != 0 and balance > 0:
close_position(arr_close[-1], n - 1)
equity_arr[-1] = balance
position_arr[-1] = 0
eq_df = pd.DataFrame({"equity": equity_arr, "position": position_arr}, index=idx)
daily = eq_df["equity"].resample("1D").last().dropna().to_frame("equity")
daily["pnl"] = daily["equity"].diff().fillna(0.0)
return {
"equity_curve": eq_df,
"daily": daily,
"final_equity": float(daily["equity"].iloc[-1]),
"trade_count": int(trades),
"win_rate": float(win_trades / max(trades, 1)),
"liq_count": int(liq_count),
"total_fee": float(total_fee),
"total_rebate": float(total_rebate),
}
def main():
df = load_klines("15m", "2020-01-01", "2026-01-01")
cfg = ConservativeConfig()
result = run_conservative(df, cfg)
daily = result["daily"]
eq = daily["equity"].astype(float)
pnl = daily["pnl"].astype(float)
final_eq = float(eq.iloc[-1])
ret_pct = (final_eq - cfg.initial_capital) / cfg.initial_capital * 100
max_dd = float((eq - eq.cummax()).min()) if len(eq) else 0.0
sharpe = float(pnl.mean() / pnl.std() * np.sqrt(365)) if pnl.std() > 0 else 0.0
print("=" * 110)
print("Conservative BB(10,2.5) | 15m | 2020-2025 | 200U | fee 0.05% each side | 90% rebate next day 08:00")
print("=" * 110)
print(f"Final equity: {final_eq:.8f} U")
print(f"Return: {ret_pct:+.2f}%")
print(f"Trades: {result['trade_count']}")
print(f"Win rate: {result['win_rate']*100:.2f}%")
print(f"Liquidations: {result['liq_count']}")
print(f"Max drawdown: {max_dd:.2f} U")
print(f"Total fee: {result['total_fee']:.8f}")
print(f"Total rebate: {result['total_rebate']:.8f}")
print(f"Sharpe: {sharpe:.4f}")
print("-" * 110)
for year in range(2020, 2026):
m = daily.index.year == year
if m.any():
print(f"{year} year-end equity: {float(daily.loc[m, 'equity'].iloc[-1]):.8f} U")
print("=" * 110)
out_dir = Path(__file__).resolve().parent / "results"
out_dir.mkdir(parents=True, exist_ok=True)
out_csv = out_dir / "bb_15m_2020_2025_conservative_daily.csv"
daily.to_csv(out_csv)
print(f"Saved daily equity: {out_csv}")
if __name__ == "__main__":
main()

147
strategy/run_bb_2020_2025.py
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@@ -1,147 +0,0 @@
"""
BB(10, 2.5) 均值回归策略回测 — 2020-2025 逐年
完全复现 bb_trade.py 的参数: BB(10,2.5) | 50x | 1%权益/单 | 1000U初始资金
测试两种手续费场景:
A) 0.06% taker (无返佣)
B) 0.025% maker + 返佣 (模拟浏览器下单)
"""
import sys, time
sys.stdout.reconfigure(line_buffering=True)
sys.path.insert(0, str(__import__("pathlib").Path(__file__).resolve().parents[1]))
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from pathlib import Path
from strategy.bb_backtest import BBConfig, run_bb_backtest
from strategy.data_loader import load_klines
out_dir = Path(__file__).resolve().parent / "results"
out_dir.mkdir(parents=True, exist_ok=True)
# ============================================================
# 加载数据
# ============================================================
YEARS = list(range(2020, 2027))
data = {}
print("加载 5 分钟 K 线数据...")
t0 = time.time()
for y in YEARS:
df = load_klines('5m', f'{y}-01-01', f'{y+1}-01-01')
data[y] = df
print(f" {y}: {len(df):>7,} 条 ({df.index[0]} ~ {df.index[-1]})")
print(f"数据加载完成 ({time.time()-t0:.1f}s)\n")
# ============================================================
# 配置 (完全匹配 bb_trade.py)
# ============================================================
BASE_KWARGS = dict(
bb_period=10,
bb_std=2.5,
leverage=50,
initial_capital=200.0,
margin_pct=0.01, # 1% 权益/单
max_daily_loss=50.0,
fee_rate=0.0005, # 万五 (0.05%) 每侧
rebate_rate=0.0,
rebate_pct=0.90, # 90% 手续费次日返还
rebate_hour_utc=0, # UTC 0点 = 北京时间早上8点
)
configs = {
"A) 原版(不加仓)": BBConfig(**BASE_KWARGS, pyramid_enabled=False),
"B) 衰减加仓 decay=0.99 max=10": BBConfig(**BASE_KWARGS, pyramid_enabled=True, pyramid_decay=0.99, pyramid_max=10),
"C) 衰减加仓 decay=0.99 max=3": BBConfig(**BASE_KWARGS, pyramid_enabled=True, pyramid_decay=0.99, pyramid_max=3),
"D) 递增加仓 +1%/次 max=3": BBConfig(**BASE_KWARGS, pyramid_enabled=True, pyramid_step=0.01, pyramid_max=3),
"E) 递增加仓 +1%/次 max=10": BBConfig(**BASE_KWARGS, pyramid_enabled=True, pyramid_step=0.01, pyramid_max=10),
}
# ============================================================
# 运行回测
# ============================================================
all_results = {}
for label, cfg in configs.items():
print("=" * 100)
print(f" {label}")
print(f" BB({cfg.bb_period}, {cfg.bb_std}) | {cfg.leverage}x | margin_pct={cfg.margin_pct:.0%} | fee={cfg.fee_rate:.4%}")
print("=" * 100)
print(f" {'年份':>6s} {'最终权益':>10s} {'收益率':>8s} {'日均PnL':>8s} {'交易次数':>8s} {'胜率':>6s} "
f"{'最大回撤':>10s} {'总手续费':>10s} {'总返佣':>10s} {'Sharpe':>7s}")
print("-" * 100)
year_results = {}
for y in YEARS:
r = run_bb_backtest(data[y], cfg)
d = r.daily_stats
pnl = d["pnl"].astype(float)
eq = d["equity"].astype(float)
dd = float((eq - eq.cummax()).min())
final_eq = float(eq.iloc[-1])
ret_pct = (final_eq - cfg.initial_capital) / cfg.initial_capital * 100
n_trades = len(r.trades)
win_rate = sum(1 for t in r.trades if t.net_pnl > 0) / max(n_trades, 1) * 100
avg_daily = float(pnl.mean())
sharpe = float(pnl.mean() / pnl.std()) * np.sqrt(365) if pnl.std() > 0 else 0
year_results[y] = r
print(f" {y:>6d} {final_eq:>10.1f} {ret_pct:>+7.1f}% {avg_daily:>+7.2f}U "
f"{n_trades:>8d} {win_rate:>5.1f}% {dd:>+10.1f} "
f"{r.total_fee:>10.1f} {r.total_rebate:>10.1f} {sharpe:>7.2f}")
all_results[label] = year_results
print()
# ============================================================
# 生成图表
# ============================================================
fig, axes = plt.subplots(len(configs), 1, figsize=(18, 6 * len(configs)), dpi=120)
if len(configs) == 1:
axes = [axes]
colors = plt.cm.tab10(np.linspace(0, 1, len(YEARS)))
for ax, (label, year_results) in zip(axes, all_results.items()):
for i, y in enumerate(YEARS):
r = year_results[y]
eq = r.equity_curve["equity"].dropna()
# 归一化到天数 (x轴)
days = (eq.index - eq.index[0]).total_seconds() / 86400
ax.plot(days, eq.values, label=f"{y}", color=colors[i], linewidth=0.8)
ax.set_title(f"BB(10, 2.5) 50x 1%权益 — {label}", fontsize=13, fontweight="bold")
ax.set_xlabel("天数")
ax.set_ylabel("权益 (USDT)")
ax.axhline(y=1000, color="gray", linestyle="--", alpha=0.5)
ax.legend(loc="upper left", fontsize=9)
ax.grid(True, alpha=0.3)
plt.tight_layout()
chart_path = out_dir / "bb_trade_2020_2025_report.png"
plt.savefig(chart_path, bbox_inches="tight")
print(f"\n图表已保存: {chart_path}")
# ============================================================
# 汇总表
# ============================================================
print("\n" + "=" * 80)
print(" 汇总: 各场景各年度日均 PnL (U/day)")
print("=" * 80)
header = f" {'场景':<35s}" + "".join(f"{y:>10d}" for y in YEARS)
print(header)
print("-" * 80)
for label, year_results in all_results.items():
vals = []
for y in YEARS:
r = year_results[y]
avg = float(r.daily_stats["pnl"].astype(float).mean())
vals.append(avg)
row = f" {label:<35s}" + "".join(f"{v:>+10.2f}" for v in vals)
print(row)
print("=" * 80)

69
strategy/run_bb_20260223_now.py
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@@ -1,69 +0,0 @@
"""
BB(10, 2.5) 均值回归策略回测 — 2026/02/23 ~ 现在，本金 100U
完全复现 bb_trade.py 的参数: BB(10,2.5) | 50x | 1%权益/单 | 递增加仓 +1%/次 max=3
"""
import sys
sys.path.insert(0, str(__import__("pathlib").Path(__file__).resolve().parents[1]))
import numpy as np
from pathlib import Path
from datetime import datetime, timedelta
from strategy.bb_backtest import BBConfig, run_bb_backtest
from strategy.data_loader import load_klines
# 加载 2026-02-23 至今天的数据 (end_date 不包含，用明天确保含今天)
today = datetime.now().strftime("%Y-%m-%d")
END_DATE = (datetime.now() + timedelta(days=1)).strftime("%Y-%m-%d")
df = load_klines('5m', '2026-02-23', END_DATE)
print(f"数据: 2026-02-23 ~ {today}, 共 {len(df):,} 根 5 分钟 K 线")
# 配置：完全匹配 bb_trade.py D方案
cfg = BBConfig(
bb_period=10,
bb_std=2.5,
leverage=50,
initial_capital=100.0, # 本金 100U
margin_pct=0.01, # 1% 权益/单
pyramid_enabled=True,
pyramid_step=0.01, # 递增加仓 +1%/次
pyramid_max=3,
max_daily_loss=50.0,
fee_rate=0.0005,
rebate_rate=0.0,
rebate_pct=0.90,
rebate_hour_utc=0,
)
r = run_bb_backtest(df, cfg)
# 结果
d = r.daily_stats
pnl = d["pnl"].astype(float)
eq = d["equity"].astype(float)
final = float(eq.iloc[-1])
dd = float((eq - eq.cummax()).min())
ret_pct = (final - cfg.initial_capital) / cfg.initial_capital * 100
nt = len(r.trades)
wr = sum(1 for t in r.trades if t.net_pnl > 0) / max(nt, 1) * 100
print("\n" + "=" * 60)
print(" 回测结果 (BB 均值回归 | 2026/02/23 ~ 现在 | 本金 100U)")
print("=" * 60)
print(f" 最终权益: {final:,.2f} U")
print(f" 收益: {final - cfg.initial_capital:+,.2f} U ({ret_pct:+.1f}%)")
print(f" 最大回撤: {dd:+,.2f} U")
print(f" 交易次数: {nt}")
print(f" 胜率: {wr:.1f}%")
print(f" 总手续费: {r.total_fee:.2f} U")
print(f" 总返佣: {r.total_rebate:.2f} U")
print("=" * 60)
# 打印每日收益
if len(d) > 1:
print("\n每日收益:")
for idx, row in d.iterrows():
day_str = idx.strftime("%Y-%m-%d") if hasattr(idx, 'strftime') else str(idx)[:10]
p = row["pnl"]
e = row["equity"]
print(f" {day_str}: PnL {p:+.2f} U, 权益 {e:.2f} U")

67
strategy/run_bb_202602_detail.py
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@@ -1,67 +0,0 @@
"""
bb_trade.py 策略回测 — 2026年2月，输出详细交易明细
200U 本金 | 1% 仓位/单 | 万五手续费 | 90% 返佣次日8点到账
按北京时间加载数据 (与交易所/网页显示一致)
"""
import sys
sys.path.insert(0, str(__import__("pathlib").Path(__file__).resolve().parents[1]))
import pandas as pd
from pathlib import Path
from strategy.bb_backtest import BBConfig, run_bb_backtest
from strategy.data_loader import load_klines
out_dir = Path(__file__).resolve().parent / "results"
out_dir.mkdir(parents=True, exist_ok=True)
# 按北京时间加载 2026-02-01 00:00 ~ 2026-03-01 00:00
df = load_klines('5m', '2026-02-01', '2026-03-01', tz='Asia/Shanghai')
cfg = BBConfig(
bb_period=10, bb_std=2.5, leverage=50, initial_capital=200.0,
margin_pct=0.01, max_daily_loss=50.0, fee_rate=0.0005,
rebate_pct=0.90, rebate_hour_utc=0, pyramid_enabled=False, # 不加仓
pyramid_step=0.01, pyramid_max=3, slippage_pct=0.0, liq_enabled=True,
cross_margin=True, # 全仓：仅权益<=0 时爆仓
fill_at_close=True, # 真实成交：检测到信号后在 K 线收盘价成交
)
r = run_bb_backtest(df, cfg)
# 数据库/回测使用 UTC，转为北京时间输出
def to_beijing(ts):
if hasattr(ts, 'tz_localize'):
return ts.tz_localize('UTC').tz_convert('Asia/Shanghai').strftime('%Y-%m-%d %H:%M:%S')
return str(ts)[:19]
# 构建交易明细 DataFrame
rows = []
for i, t in enumerate(r.trades, 1):
rows.append({
"序号": i,
"方向": "做多" if t.side == "long" else "做空",
"开仓时间": to_beijing(t.entry_time),
"平仓时间": to_beijing(t.exit_time),
"开仓价": round(t.entry_price, 2),
"平仓价": round(t.exit_price, 2),
"保证金": round(t.margin, 2),
"杠杆": t.leverage,
"数量": round(t.qty, 4),
"毛盈亏": round(t.gross_pnl, 2),
"手续费": round(t.fee, 2),
"净盈亏": round(t.net_pnl, 2),
})
trade_df = pd.DataFrame(rows)
# 保存 CSV
csv_path = out_dir / "bb_202602_trade_detail.csv"
trade_df.to_csv(csv_path, index=False, encoding="utf-8-sig")
print(f"交易明细已保存: {csv_path}")
# 打印到控制台
print("\n" + "=" * 150)
print(f" 交易明细 (共 {len(r.trades)} 笔)")
print("=" * 150)
print(trade_df.to_string(index=False))

381
strategy/run_bb_5m_practical_upgrade.py
View File

@@ -1,381 +0,0 @@
"""
Practical upgraded BB backtest on 5m ETH data (2020-2025).
Execution model (conservative):
1) BB uses closed bars only (shift by 1 bar).
2) Signal on bar i, execution at bar i+1 open.
3) Fee = 0.05% each side, rebate = 90% next day at UTC 00:00 (UTC+8 08:00).
4) Daily loss stop, liquidation, and slippage are enabled.
Compares:
- Baseline: BB(30, 3.0), 1x, no trend filter.
- Practical: BB(30, 3.2), 1x, EMA trend filter, BB width filter, cooldown.
"""
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
import sys
import numpy as np
import pandas as pd
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from strategy.data_loader import load_klines
@dataclass
class PracticalConfig:
bb_period: int = 30
bb_std: float = 3.2
leverage: float = 1.0
initial_capital: float = 200.0
margin_pct: float = 0.01
fee_rate: float = 0.0005
rebate_pct: float = 0.90
rebate_hour_utc: int = 0
slippage_pct: float = 0.0005
liq_enabled: bool = True
maint_margin_rate: float = 0.005
max_daily_loss: float = 50.0
# Practical risk controls
trend_ema_period: int = 288 # 288 * 5m = 24h EMA
min_bandwidth: float = 0.01 # (upper-lower)/mid minimum
cooldown_bars: int = 6 # 6 * 5m = 30 minutes
def run_practical_backtest(df: pd.DataFrame, cfg: PracticalConfig):
arr_open = df["open"].to_numpy(dtype=float)
arr_high = df["high"].to_numpy(dtype=float)
arr_low = df["low"].to_numpy(dtype=float)
arr_close = df["close"].to_numpy(dtype=float)
idx = df.index
n = len(df)
close_s = df["close"].astype(float)
mid = close_s.rolling(cfg.bb_period).mean().shift(1)
std = close_s.rolling(cfg.bb_period).std(ddof=0).shift(1)
upper_s = mid + cfg.bb_std * std
lower_s = mid - cfg.bb_std * std
bandwidth_s = (upper_s - lower_s) / mid
upper = upper_s.to_numpy(dtype=float)
lower = lower_s.to_numpy(dtype=float)
bandwidth = bandwidth_s.to_numpy(dtype=float)
if cfg.trend_ema_period > 0:
trend_ema = close_s.ewm(span=cfg.trend_ema_period, adjust=False).mean().shift(1).to_numpy(dtype=float)
else:
trend_ema = np.full(n, np.nan)
balance = cfg.initial_capital
position = 0 # +1 long, -1 short
entry_price = 0.0
entry_qty = 0.0
entry_margin = 0.0
trades = 0
win_trades = 0
liq_count = 0
total_fee = 0.0
total_rebate = 0.0
current_day = None
day_pnl = 0.0
day_stopped = False
rebate_applied_today = False
next_trade_bar = 0
day_fees = {}
equity_arr = np.full(n, np.nan)
position_arr = np.zeros(n)
def unrealised(price: float) -> float:
if position == 0:
return 0.0
if position == 1:
return entry_qty * (price - entry_price)
return entry_qty * (entry_price - price)
def apply_open_slippage(side: int, price: float) -> float:
return price * (1 + cfg.slippage_pct) if side == 1 else price * (1 - cfg.slippage_pct)
def apply_close_slippage(side: int, price: float) -> float:
return price * (1 - cfg.slippage_pct) if side == 1 else price * (1 + cfg.slippage_pct)
def add_fee(ts: pd.Timestamp, fee: float):
nonlocal total_fee
total_fee += fee
d = ts.date()
day_fees[d] = day_fees.get(d, 0.0) + fee
def close_position(exec_price: float, exec_idx: int):
nonlocal balance, position, entry_price, entry_qty, entry_margin
nonlocal trades, win_trades, day_pnl
if position == 0:
return
px = apply_close_slippage(position, exec_price)
gross = entry_qty * (px - entry_price) if position == 1 else entry_qty * (entry_price - px)
notional = entry_qty * px
fee = notional * cfg.fee_rate
net = gross - fee
balance += net
add_fee(idx[exec_idx], fee)
day_pnl += net
trades += 1
if net > 0:
win_trades += 1
position = 0
entry_price = 0.0
entry_qty = 0.0
entry_margin = 0.0
def open_position(side: int, exec_price: float, exec_idx: int):
nonlocal balance, position, entry_price, entry_qty, entry_margin
nonlocal day_pnl
px = apply_open_slippage(side, exec_price)
eq = balance + unrealised(px)
margin = min(eq * cfg.margin_pct, balance * 0.95)
if margin <= 0:
return
notional = margin * cfg.leverage
qty = notional / px
fee = notional * cfg.fee_rate
balance -= fee
add_fee(idx[exec_idx], fee)
day_pnl -= fee
position = side
entry_price = px
entry_qty = qty
entry_margin = margin
for i in range(n - 1):
ts = idx[i]
bar_day = ts.date()
if bar_day != current_day:
current_day = bar_day
day_pnl = 0.0
day_stopped = False
rebate_applied_today = False
# Fee rebate settlement (next day 08:00 UTC+8 = UTC 00:00)
if (not rebate_applied_today) and ts.hour >= cfg.rebate_hour_utc:
prev_day = bar_day - pd.Timedelta(days=1)
prev_fee = day_fees.get(prev_day, 0.0)
if prev_fee > 0:
rebate = prev_fee * cfg.rebate_pct
balance += rebate
total_rebate += rebate
rebate_applied_today = True
# Daily loss stop
if not day_stopped and (day_pnl + unrealised(arr_close[i]) <= -cfg.max_daily_loss):
close_position(arr_open[i + 1], i + 1)
day_stopped = True
# Liquidation check
if cfg.liq_enabled and position != 0 and entry_margin > 0:
liq_threshold = 1.0 / cfg.leverage * (1 - cfg.maint_margin_rate)
if position == 1:
liq_price = entry_price * (1 - liq_threshold)
if arr_low[i] <= liq_price:
balance -= entry_margin
day_pnl -= entry_margin
position = 0
entry_price = 0.0
entry_qty = 0.0
entry_margin = 0.0
trades += 1
liq_count += 1
else:
liq_price = entry_price * (1 + liq_threshold)
if arr_high[i] >= liq_price:
balance -= entry_margin
day_pnl -= entry_margin
position = 0
entry_price = 0.0
entry_qty = 0.0
entry_margin = 0.0
trades += 1
liq_count += 1
if (
day_stopped
or i < next_trade_bar
or np.isnan(upper[i])
or np.isnan(lower[i])
or np.isnan(bandwidth[i])
):
equity_arr[i] = balance + unrealised(arr_close[i])
position_arr[i] = position
if balance <= 0:
balance = 0.0
equity_arr[i:] = 0.0
position_arr[i:] = 0
break
continue
if cfg.trend_ema_period > 0 and np.isnan(trend_ema[i]):
equity_arr[i] = balance + unrealised(arr_close[i])
position_arr[i] = position
continue
if bandwidth[i] < cfg.min_bandwidth:
equity_arr[i] = balance + unrealised(arr_close[i])
position_arr[i] = position
continue
touched_upper = arr_high[i] >= upper[i]
touched_lower = arr_low[i] <= lower[i]
exec_px = arr_open[i + 1]
if cfg.trend_ema_period > 0:
long_ok = arr_close[i] > trend_ema[i]
short_ok = arr_close[i] < trend_ema[i]
else:
long_ok = True
short_ok = True
if touched_upper and touched_lower:
pass
elif touched_upper:
if position == 1:
close_position(exec_px, i + 1)
next_trade_bar = max(next_trade_bar, i + 1 + cfg.cooldown_bars)
if position == 0 and short_ok:
open_position(-1, exec_px, i + 1)
next_trade_bar = max(next_trade_bar, i + 1 + cfg.cooldown_bars)
elif touched_lower:
if position == -1:
close_position(exec_px, i + 1)
next_trade_bar = max(next_trade_bar, i + 1 + cfg.cooldown_bars)
if position == 0 and long_ok:
open_position(1, exec_px, i + 1)
next_trade_bar = max(next_trade_bar, i + 1 + cfg.cooldown_bars)
equity_arr[i] = balance + unrealised(arr_close[i])
position_arr[i] = position
if balance <= 0:
balance = 0.0
equity_arr[i:] = 0.0
position_arr[i:] = 0
break
if position != 0 and balance > 0:
close_position(arr_close[-1], n - 1)
equity_arr[-1] = balance
position_arr[-1] = 0
eq_df = pd.DataFrame({"equity": equity_arr, "position": position_arr}, index=idx)
daily = eq_df["equity"].resample("1D").last().dropna().to_frame("equity")
daily["pnl"] = daily["equity"].diff().fillna(0.0)
return {
"equity_curve": eq_df,
"daily": daily,
"final_equity": float(daily["equity"].iloc[-1]),
"trade_count": int(trades),
"win_rate": float(win_trades / max(trades, 1)),
"liq_count": int(liq_count),
"total_fee": float(total_fee),
"total_rebate": float(total_rebate),
}
def summarize(label: str, result: dict, initial_capital: float):
daily = result["daily"]
eq = daily["equity"].astype(float)
pnl = daily["pnl"].astype(float)
final_eq = float(eq.iloc[-1])
ret_pct = (final_eq - initial_capital) / initial_capital * 100
max_dd = float((eq - eq.cummax()).min()) if len(eq) else 0.0
sharpe = float(pnl.mean() / pnl.std() * np.sqrt(365)) if pnl.std() > 0 else 0.0
print(f"[{label}]")
print(f" Final equity: {final_eq:.6f} U")
print(f" Return: {ret_pct:+.4f}%")
print(f" Trades: {result['trade_count']} | Win rate: {result['win_rate']*100:.2f}% | Liquidations: {result['liq_count']}")
print(f" Max drawdown: {max_dd:.6f} U | Sharpe: {sharpe:.4f}")
print(f" Total fee: {result['total_fee']:.6f} | Total rebate: {result['total_rebate']:.6f}")
print(" Year-end equity:")
for year in range(2020, 2026):
m = daily.index.year == year
if m.any():
print(f" {year}: {float(daily.loc[m, 'equity'].iloc[-1]):.6f} U")
print()
return {
"label": label,
"final_equity": final_eq,
"return_pct": ret_pct,
"trade_count": result["trade_count"],
"win_rate_pct": result["win_rate"] * 100,
"liq_count": result["liq_count"],
"max_drawdown": max_dd,
"total_fee": result["total_fee"],
"total_rebate": result["total_rebate"],
"sharpe": sharpe,
}
def main():
df = load_klines("5m", "2020-01-01", "2026-01-01")
baseline_cfg = PracticalConfig(
bb_period=30,
bb_std=3.0,
leverage=1.0,
trend_ema_period=0,
min_bandwidth=0.0,
cooldown_bars=0,
)
practical_cfg = PracticalConfig(
bb_period=30,
bb_std=3.2,
leverage=1.0,
trend_ema_period=288,
min_bandwidth=0.01,
cooldown_bars=6,
)
print("=" * 118)
print("Practical Upgrade Backtest | 5m | 2020-2025 | capital=200U | fee=0.05% each side | rebate=90% next day 08:00")
print("=" * 118)
print("Execution: signal at bar i, fill at bar i+1 open (conservative)")
print()
baseline = run_practical_backtest(df, baseline_cfg)
practical = run_practical_backtest(df, practical_cfg)
summary_rows = []
summary_rows.append(summarize("Baseline (BB30/3.0, no filters)", baseline, baseline_cfg.initial_capital))
summary_rows.append(summarize("Practical (BB30/3.2 + EMA + BW + cooldown)", practical, practical_cfg.initial_capital))
out_dir = Path(__file__).resolve().parent / "results"
out_dir.mkdir(parents=True, exist_ok=True)
baseline_daily = baseline["daily"].rename(columns={"equity": "baseline_equity", "pnl": "baseline_pnl"})
practical_daily = practical["daily"].rename(columns={"equity": "practical_equity", "pnl": "practical_pnl"})
daily_compare = baseline_daily.join(practical_daily, how="outer")
daily_compare.to_csv(out_dir / "bb_5m_practical_upgrade_daily.csv")
pd.DataFrame(summary_rows).to_csv(out_dir / "bb_5m_practical_upgrade_summary.csv", index=False)
print(f"Saved: {out_dir / 'bb_5m_practical_upgrade_daily.csv'}")
print(f"Saved: {out_dir / 'bb_5m_practical_upgrade_summary.csv'}")
if __name__ == "__main__":
main()

199
strategy/run_bb_backtest.py
View File

@@ -1,199 +0,0 @@
"""Run Bollinger Band mean-reversion backtest on ETH 2023+2024.
Preloads data once, then sweeps parameters in-memory for speed.
"""
import sys, time
sys.stdout.reconfigure(line_buffering=True)
sys.path.insert(0, str(__import__("pathlib").Path(__file__).resolve().parents[1]))
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from pathlib import Path
from collections import defaultdict
from strategy.bb_backtest import BBConfig, run_bb_backtest
from strategy.data_loader import KlineSource, load_klines
from datetime import datetime, timezone
root = Path(__file__).resolve().parents[1]
src = KlineSource(db_path=root / "models" / "database.db", table_name="bitmart_eth_5m")
out_dir = root / "strategy" / "results"
out_dir.mkdir(parents=True, exist_ok=True)
t0 = time.time()
# Preload data once
print("Loading data...")
df_23 = load_klines(src, datetime(2023,1,1,tzinfo=timezone.utc),
datetime(2023,12,31,23,59,tzinfo=timezone.utc))
df_24 = load_klines(src, datetime(2024,1,1,tzinfo=timezone.utc),
datetime(2024,12,31,23,59,tzinfo=timezone.utc))
data = {2023: df_23, 2024: df_24}
print(f"Loaded: 2023={len(df_23)} bars, 2024={len(df_24)} bars ({time.time()-t0:.1f}s)")
# ================================================================
# Sweep
# ================================================================
print("\n" + "=" * 120)
print(" Bollinger Band Mean-Reversion — ETH 5min | 1000U capital")
print(" touch upper BB -> short, touch lower BB -> long (flip)")
print("=" * 120)
results = []
def test(label, cfg):
"""Run on both years, print summary, store results."""
row = {"label": label, "cfg": cfg}
for year in [2023, 2024]:
r = run_bb_backtest(data[year], cfg)
d = r.daily_stats
pnl = d["pnl"].astype(float)
eq = d["equity"].astype(float)
dd = float((eq - eq.cummax()).min())
final = float(eq.iloc[-1])
nt = len(r.trades)
wr = sum(1 for t in r.trades if t.net_pnl > 0) / max(nt, 1) * 100
nf = r.total_fee - r.total_rebate
row[f"a{year}"] = float(pnl.mean())
row[f"d{year}"] = dd
row[f"r{year}"] = r
row[f"n{year}"] = nt
row[f"w{year}"] = wr
row[f"f{year}"] = nf
row[f"eq{year}"] = final
mn = min(row["a2023"], row["a2024"])
avg = (row["a2023"] + row["a2024"]) / 2
mark = " <<<" if mn >= 20 else (" **" if mn >= 10 else "")
print(f" {label:52s} 23:{row['a2023']:+6.1f} 24:{row['a2024']:+6.1f} "
f"avg:{avg:+5.1f} n23:{row['n2023']:3d} n24:{row['n2024']:3d} "
f"dd:{min(row['d2023'],row['d2024']):+7.0f}{mark}")
row["mn"] = mn; row["avg"] = avg
results.append(row)
# [1] BB period
print("\n[1] Period sweep")
for p in [10, 15, 20, 30, 40]:
test(f"BB({p},2.0) 80u 100x", BBConfig(bb_period=p, bb_std=2.0, margin_per_trade=80, leverage=100))
# [2] BB std
print("\n[2] Std sweep")
for s in [1.5, 1.8, 2.0, 2.5, 3.0]:
test(f"BB(20,{s}) 80u 100x", BBConfig(bb_period=20, bb_std=s, margin_per_trade=80, leverage=100))
# [3] Margin
print("\n[3] Margin sweep")
for m in [40, 60, 80, 100, 120]:
test(f"BB(20,2.0) {m}u 100x", BBConfig(bb_period=20, bb_std=2.0, margin_per_trade=m, leverage=100))
# [4] SL
print("\n[4] Stop-loss sweep")
for sl in [0.0, 0.01, 0.02, 0.03, 0.05]:
test(f"BB(20,2.0) 80u SL={sl:.0%}", BBConfig(bb_period=20, bb_std=2.0, margin_per_trade=80, leverage=100, stop_loss_pct=sl))
# [5] MDL
print("\n[5] Max daily loss")
for mdl in [50, 100, 150, 200]:
test(f"BB(20,2.0) 80u mdl={mdl}", BBConfig(bb_period=20, bb_std=2.0, margin_per_trade=80, leverage=100, max_daily_loss=mdl))
# [6] Combined fine-tune
print("\n[6] Fine-tune")
for p in [15, 20, 30]:
for s in [1.5, 2.0, 2.5]:
for m in [80, 100]:
test(f"BB({p},{s}) {m}u mdl=150",
BBConfig(bb_period=p, bb_std=s, margin_per_trade=m, leverage=100, max_daily_loss=150))
# ================================================================
# Ranking
# ================================================================
results.sort(key=lambda x: x["mn"], reverse=True)
print(f"\n{'='*120}")
print(f" TOP 10 — ranked by min(daily_avg_2023, daily_avg_2024)")
print(f"{'='*120}")
for i, r in enumerate(results[:10]):
print(f" {i+1:2d}. {r['label']:50s} 23:{r['a2023']:+6.1f} 24:{r['a2024']:+6.1f} "
f"min:{r['mn']:+6.1f} dd:{min(r['d2023'],r['d2024']):+7.0f} "
f"wr23:{r['w2023']:.0f}% wr24:{r['w2024']:.0f}%")
# ================================================================
# Detailed report for best
# ================================================================
best = results[0]
print(f"\n{'#'*70}")
print(f" BEST: {best['label']}")
print(f"{'#'*70}")
for year in [2023, 2024]:
r = best[f"r{year}"]
cfg = best["cfg"]
d = r.daily_stats
pnl = d["pnl"].astype(float)
eq = d["equity"].astype(float)
dd = (eq - eq.cummax()).min()
final = float(eq.iloc[-1])
nt = len(r.trades)
wr = sum(1 for t in r.trades if t.net_pnl > 0) / max(nt, 1)
nf = r.total_fee - r.total_rebate
loss_streak = max_ls = 0
for v in pnl.values:
if v < 0: loss_streak += 1; max_ls = max(max_ls, loss_streak)
else: loss_streak = 0
print(f"\n --- {year} ---")
print(f" Final equity : {final:,.2f} U ({final-cfg.initial_capital:+,.2f}, "
f"{(final-cfg.initial_capital)/cfg.initial_capital*100:+.1f}%)")
print(f" Max drawdown : {dd:,.2f} U")
print(f" Avg daily PnL : {pnl.mean():+,.2f} U")
print(f" Median daily PnL : {pnl.median():+,.2f} U")
print(f" Best/worst day : {pnl.max():+,.2f} / {pnl.min():+,.2f}")
print(f" Profitable days : {(pnl>0).sum()}/{len(pnl)} ({(pnl>0).mean():.1%})")
print(f" Days >= 20U : {(pnl>=20).sum()}")
print(f" Max loss streak : {max_ls} days")
print(f" Trades : {nt} (win rate {wr:.1%})")
print(f" Net fees : {nf:,.0f} U")
sharpe = pnl.mean() / max(pnl.std(), 1e-10) * np.sqrt(365)
print(f" Sharpe (annual) : {sharpe:.2f}")
# ================================================================
# Chart
# ================================================================
fig, axes = plt.subplots(3, 2, figsize=(18, 12),
gridspec_kw={"height_ratios": [3, 1.5, 1]})
for col, year in enumerate([2023, 2024]):
r = best[f"r{year}"]
cfg = best["cfg"]
d = r.daily_stats
eq = d["equity"].astype(float)
pnl = d["pnl"].astype(float)
dd = eq - eq.cummax()
axes[0, col].plot(eq.index, eq.values, linewidth=1.2, color="#1f77b4")
axes[0, col].axhline(cfg.initial_capital, color="gray", ls="--", lw=0.5)
axes[0, col].set_title(f"BB Strategy Equity — {year}\n"
f"BB({cfg.bb_period},{cfg.bb_std}) {cfg.margin_per_trade}u {cfg.leverage:.0f}x",
fontsize=11)
axes[0, col].set_ylabel("Equity (U)")
axes[0, col].grid(True, alpha=0.3)
colors = ["#2ca02c" if v >= 0 else "#d62728" for v in pnl.values]
axes[1, col].bar(pnl.index, pnl.values, color=colors, width=0.8)
axes[1, col].axhline(20, color="orange", ls="--", lw=1, label="20U target")
axes[1, col].axhline(0, color="gray", lw=0.5)
axes[1, col].set_ylabel("Daily PnL (U)")
axes[1, col].legend(fontsize=8)
axes[1, col].grid(True, alpha=0.3)
axes[2, col].fill_between(dd.index, dd.values, 0, color="#d62728", alpha=0.4)
axes[2, col].set_ylabel("Drawdown (U)")
axes[2, col].grid(True, alpha=0.3)
fig.tight_layout()
fig.savefig(out_dir / "bb_strategy_report.png", dpi=150)
plt.close(fig)
print(f"\nChart: {out_dir / 'bb_strategy_report.png'}")
print(f"Total time: {time.time()-t0:.0f}s")

197
strategy/run_bb_trade_backtest.py
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@@ -1,197 +0,0 @@
"""
回测 bb_trade.py 的 D方案策略
BB(10, 2.5) | 5分钟 | ETH | 50x | 递增加仓+1%/次 max=3
200U 本金 | 每次开仓 1% 权益 | 开平仓手续费万五 | 返佣90%次日早8点到账
"""
import sys, time
sys.stdout.reconfigure(line_buffering=True)
sys.path.insert(0, str(__import__("pathlib").Path(__file__).resolve().parents[1]))
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from pathlib import Path
from strategy.bb_backtest import BBConfig, run_bb_backtest
from strategy.data_loader import load_klines
out_dir = Path(__file__).resolve().parent / "results"
out_dir.mkdir(parents=True, exist_ok=True)
# ============================================================
# 加载数据 2020-2025
# ============================================================
YEARS = list(range(2020, 2026))
print("加载 5 分钟 K 线数据 (2020-2025)...")
t0 = time.time()
data = {}
for y in YEARS:
df = load_klines('5m', f'{y}-01-01', f'{y+1}-01-01')
data[y] = df
print(f" {y}: {len(df):>7,} 条 ({df.index[0]} ~ {df.index[-1]})")
# 合并全量数据用于连续回测
df_all = pd.concat([data[y] for y in YEARS])
df_all = df_all[~df_all.index.duplicated(keep='first')].sort_index()
print(f" 合计: {len(df_all):>7,} 条 ({df_all.index[0]} ~ {df_all.index[-1]})")
print(f"数据加载完成 ({time.time()-t0:.1f}s)\n")
# ============================================================
# 配置 — 完全匹配 bb_trade.py D方案
# ============================================================
cfg = BBConfig(
bb_period=10,
bb_std=2.5,
leverage=50,
initial_capital=200.0,
margin_pct=0.01, # 1% 权益/单
max_daily_loss=50.0,
fee_rate=0.0005, # 万五 (0.05%) 每侧 (开+平各收一次)
rebate_rate=0.0, # 无即时返佣
rebate_pct=0.90, # 90% 手续费次日返还
rebate_hour_utc=0, # UTC 0点 = 北京时间早上8点
pyramid_enabled=True,
pyramid_step=0.01, # 递增加仓 +1%/次
pyramid_max=3, # 最多加仓3次
slippage_pct=0.0, # 回测不加滑点 (实盘浏览器市价单有滑点)
liq_enabled=True,
stop_loss_pct=0.0,
)
# ============================================================
# 1) 逐年回测 (每年独立 200U 起步)
# ============================================================
print("=" * 100)
print(" 【逐年独立回测】每年独立 200U 本金")
print(f" BB({cfg.bb_period}, {cfg.bb_std}) | {cfg.leverage}x | 开仓={cfg.margin_pct:.0%}权益 | "
f"手续费={cfg.fee_rate:.4%}/侧 | 返佣={cfg.rebate_pct:.0%}次日8点")
print("=" * 100)
print(f" {'年份':>6s} {'最终权益':>10s} {'收益率':>8s} {'日均PnL':>8s} {'交易次数':>8s} {'胜率':>6s} "
f"{'最大回撤':>10s} {'回撤%':>8s} {'总手续费':>10s} {'总返佣':>10s} {'净手续费':>10s} {'Sharpe':>7s}")
print("-" * 130)
year_results = {}
for y in YEARS:
r = run_bb_backtest(data[y], cfg)
year_results[y] = r
d = r.daily_stats
pnl = d["pnl"].astype(float)
eq = d["equity"].astype(float)
peak = eq.cummax()
dd = float((eq - peak).min())
dd_pct = dd / float(peak[eq - peak == dd].iloc[0]) * 100 if dd < 0 else 0
final_eq = float(eq.iloc[-1])
ret_pct = (final_eq - cfg.initial_capital) / cfg.initial_capital * 100
n_trades = len(r.trades)
win_rate = sum(1 for t in r.trades if t.net_pnl > 0) / max(n_trades, 1) * 100
avg_daily = float(pnl.mean())
sharpe = float(pnl.mean() / pnl.std()) * np.sqrt(365) if pnl.std() > 0 else 0
net_fee = r.total_fee - r.total_rebate
print(f" {y:>6d} {final_eq:>10.1f} {ret_pct:>+7.1f}% {avg_daily:>+7.2f}U "
f"{n_trades:>8d} {win_rate:>5.1f}% {dd:>+10.1f} {dd_pct:>+7.1f}% "
f"{r.total_fee:>10.1f} {r.total_rebate:>10.1f} {net_fee:>10.1f} {sharpe:>7.2f}")
print()
# ============================================================
# 2) 连续回测 (2020-2025 一次性跑，资金连续滚动)
# ============================================================
print("=" * 100)
print(" 【连续回测】2020-2025 资金滚动，200U 起步")
print("=" * 100)
r_all = run_bb_backtest(df_all, cfg)
d_all = r_all.daily_stats
pnl_all = d_all["pnl"].astype(float)
eq_all = d_all["equity"].astype(float)
peak_all = eq_all.cummax()
dd_all = float((eq_all - peak_all).min())
dd_pct_all = dd_all / float(peak_all[eq_all - peak_all == dd_all].iloc[0]) * 100 if dd_all < 0 else 0
final_eq_all = float(eq_all.iloc[-1])
ret_pct_all = (final_eq_all - cfg.initial_capital) / cfg.initial_capital * 100
n_trades_all = len(r_all.trades)
win_rate_all = sum(1 for t in r_all.trades if t.net_pnl > 0) / max(n_trades_all, 1) * 100
avg_daily_all = float(pnl_all.mean())
sharpe_all = float(pnl_all.mean() / pnl_all.std()) * np.sqrt(365) if pnl_all.std() > 0 else 0
net_fee_all = r_all.total_fee - r_all.total_rebate
print(f" 初始资金: {cfg.initial_capital:.0f} U")
print(f" 最终权益: {final_eq_all:.1f} U")
print(f" 总收益率: {ret_pct_all:+.1f}%")
print(f" 日均 PnL: {avg_daily_all:+.2f} U")
print(f" 交易次数: {n_trades_all}")
print(f" 胜率: {win_rate_all:.1f}%")
print(f" 最大回撤: {dd_all:+.1f} U ({dd_pct_all:+.1f}%)")
print(f" 总手续费: {r_all.total_fee:.1f} U")
print(f" 总返佣: {r_all.total_rebate:.1f} U")
print(f" 净手续费: {net_fee_all:.1f} U")
print(f" Sharpe: {sharpe_all:.2f}")
print()
# 按年统计连续回测中的表现
print(" 连续回测逐年切片:")
print(f" {'年份':>6s} {'年初权益':>10s} {'年末权益':>10s} {'年收益':>10s} {'年收益率':>8s}")
print("-" * 60)
for y in YEARS:
mask = (eq_all.index >= f'{y}-01-01') & (eq_all.index < f'{y+1}-01-01')
if mask.sum() == 0:
continue
eq_year = eq_all[mask]
start_eq = float(eq_year.iloc[0])
end_eq = float(eq_year.iloc[-1])
yr_ret = end_eq - start_eq
yr_pct = yr_ret / start_eq * 100
print(f" {y:>6d} {start_eq:>10.1f} {end_eq:>10.1f} {yr_ret:>+10.1f} {yr_pct:>+7.1f}%")
print()
# ============================================================
# 图表
# ============================================================
fig, axes = plt.subplots(3, 1, figsize=(18, 18), dpi=120)
# 图1: 逐年独立回测权益曲线
ax1 = axes[0]
colors = plt.cm.tab10(np.linspace(0, 1, len(YEARS)))
for i, y in enumerate(YEARS):
r = year_results[y]
eq = r.equity_curve["equity"].dropna()
days = (eq.index - eq.index[0]).total_seconds() / 86400
ax1.plot(days, eq.values, label=f"{y}", color=colors[i], linewidth=0.8)
ax1.set_title(f"BB(10,2.5) D方案 逐年独立回测 (200U起步)", fontsize=13, fontweight="bold")
ax1.set_xlabel("天数")
ax1.set_ylabel("权益 (USDT)")
ax1.axhline(y=200, color="gray", linestyle="--", alpha=0.5, label="本金200U")
ax1.legend(loc="upper left", fontsize=9)
ax1.grid(True, alpha=0.3)
# 图2: 连续回测权益曲线
ax2 = axes[1]
eq_curve = r_all.equity_curve["equity"].dropna()
ax2.plot(eq_curve.index, eq_curve.values, color="steelblue", linewidth=0.6)
ax2.set_title(f"BB(10,2.5) D方案 连续回测 2020-2025 (200U→{final_eq_all:.0f}U)", fontsize=13, fontweight="bold")
ax2.set_xlabel("日期")
ax2.set_ylabel("权益 (USDT)")
ax2.axhline(y=200, color="gray", linestyle="--", alpha=0.5)
ax2.grid(True, alpha=0.3)
# 图3: 连续回测日PnL
ax3 = axes[2]
daily_pnl = r_all.daily_stats["pnl"].astype(float)
colors_pnl = ['green' if x >= 0 else 'red' for x in daily_pnl.values]
ax3.bar(daily_pnl.index, daily_pnl.values, color=colors_pnl, width=1, alpha=0.7)
ax3.set_title("日 PnL 分布", fontsize=13, fontweight="bold")
ax3.set_xlabel("日期")
ax3.set_ylabel("PnL (USDT)")
ax3.axhline(y=0, color="black", linewidth=0.5)
ax3.grid(True, alpha=0.3)
plt.tight_layout()
chart_path = out_dir / "bb_trade_d_plan_2020_2025.png"
plt.savefig(chart_path, bbox_inches="tight")
print(f"图表已保存: {chart_path}")

125
strategy/strategy_signal.py
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@@ -1,125 +0,0 @@
"""
信号生成模块 - 多指标加权投票 + 多时间框架过滤
"""
import numpy as np
import pandas as pd
def generate_indicator_signals(df: pd.DataFrame, params: dict) -> pd.DataFrame:
"""
根据指标值生成每个指标的独立信号 (+1 做多 / -1 做空 / 0 中性)
df 必须已经包含 compute_all_indicators 计算出的列
"""
out = df.copy()
# --- 布林带 %B ---
out['sig_bb'] = 0
out.loc[out['bb_pct'] < params.get('bb_oversold', 0.0), 'sig_bb'] = 1
out.loc[out['bb_pct'] > params.get('bb_overbought', 1.0), 'sig_bb'] = -1
# --- 肯特纳通道 ---
out['sig_kc'] = 0
out.loc[out['kc_pct'] < params.get('kc_oversold', 0.0), 'sig_kc'] = 1
out.loc[out['kc_pct'] > params.get('kc_overbought', 1.0), 'sig_kc'] = -1
# --- 唐奇安通道 ---
out['sig_dc'] = 0
out.loc[out['dc_pct'] < params.get('dc_oversold', 0.2), 'sig_dc'] = 1
out.loc[out['dc_pct'] > params.get('dc_overbought', 0.8), 'sig_dc'] = -1
# --- EMA 交叉 ---
out['sig_ema'] = 0
out.loc[out['ema_diff'] > 0, 'sig_ema'] = 1
out.loc[out['ema_diff'] < 0, 'sig_ema'] = -1
# --- MACD ---
out['sig_macd'] = 0
out.loc[out['macd_hist'] > 0, 'sig_macd'] = 1
out.loc[out['macd_hist'] < 0, 'sig_macd'] = -1
# --- ADX + DI ---
adx_thresh = params.get('adx_threshold', 25)
out['sig_adx'] = 0
out.loc[(out['adx'] > adx_thresh) & (out['di_diff'] > 0), 'sig_adx'] = 1
out.loc[(out['adx'] > adx_thresh) & (out['di_diff'] < 0), 'sig_adx'] = -1
# --- Supertrend ---
out['sig_st'] = out['st_dir']
# --- RSI ---
rsi_ob = params.get('rsi_overbought', 70)
rsi_os = params.get('rsi_oversold', 30)
out['sig_rsi'] = 0
out.loc[out['rsi'] < rsi_os, 'sig_rsi'] = 1
out.loc[out['rsi'] > rsi_ob, 'sig_rsi'] = -1
# --- Stochastic ---
stoch_ob = params.get('stoch_overbought', 80)
stoch_os = params.get('stoch_oversold', 20)
out['sig_stoch'] = 0
out.loc[(out['stoch_k'] < stoch_os) & (out['stoch_k'] > out['stoch_d']), 'sig_stoch'] = 1
out.loc[(out['stoch_k'] > stoch_ob) & (out['stoch_k'] < out['stoch_d']), 'sig_stoch'] = -1
# --- CCI ---
cci_ob = params.get('cci_overbought', 100)
cci_os = params.get('cci_oversold', -100)
out['sig_cci'] = 0
out.loc[out['cci'] < cci_os, 'sig_cci'] = 1
out.loc[out['cci'] > cci_ob, 'sig_cci'] = -1
# --- Williams %R ---
wr_ob = params.get('wr_overbought', -20)
wr_os = params.get('wr_oversold', -80)
out['sig_wr'] = 0
out.loc[out['wr'] < wr_os, 'sig_wr'] = 1
out.loc[out['wr'] > wr_ob, 'sig_wr'] = -1
# --- WMA ---
out['sig_wma'] = 0
out.loc[out['wma_diff'] > 0, 'sig_wma'] = 1
out.loc[out['wma_diff'] < 0, 'sig_wma'] = -1
return out
SIGNAL_COLS = [
'sig_bb', 'sig_kc', 'sig_dc', 'sig_ema', 'sig_macd',
'sig_adx', 'sig_st', 'sig_rsi', 'sig_stoch', 'sig_cci',
'sig_wr', 'sig_wma',
]
WEIGHT_KEYS = [
'w_bb', 'w_kc', 'w_dc', 'w_ema', 'w_macd',
'w_adx', 'w_st', 'w_rsi', 'w_stoch', 'w_cci',
'w_wr', 'w_wma',
]
def compute_composite_score(df: pd.DataFrame, params: dict) -> pd.Series:
"""
加权投票计算综合得分 (-1 ~ +1)
"""
weights = np.array([params.get(k, 1.0) for k in WEIGHT_KEYS])
total_w = weights.sum()
if total_w == 0:
total_w = 1.0
signals = df[SIGNAL_COLS].values # (N, 12)
score = (signals * weights).sum(axis=1) / total_w
return pd.Series(score, index=df.index, name='score')
def apply_htf_filter(score: pd.Series, htf_df: pd.DataFrame, params: dict) -> pd.Series:
"""
用高时间框架（如1h）的趋势方向过滤信号
htf_df 需要包含 'ema_diff' 列
只允许与大趋势同向的信号通过
"""
# 将 htf 的 ema_diff reindex 到主时间框架
htf_trend = htf_df['ema_diff'].reindex(score.index, method='ffill')
filtered = score.copy()
# 大趋势向上时，屏蔽做空信号
filtered.loc[(htf_trend > 0) & (filtered < 0)] = 0
# 大趋势向下时，屏蔽做多信号
filtered.loc[(htf_trend < 0) & (filtered > 0)] = 0
return filtered

226
strategy/train.py
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@@ -1,226 +0,0 @@
"""
Optuna 训练入口 - 在 2020-2022 数据上搜索最优参数
"""
import json
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import optuna
from optuna.samplers import TPESampler
import numpy as np
from strategy.data_loader import load_klines
from strategy.indicators import compute_all_indicators
from strategy.strategy_signal import (
generate_indicator_signals, compute_composite_score,
apply_htf_filter, WEIGHT_KEYS,
)
from strategy.backtest_engine import BacktestEngine
# ============================================================
# 全局加载数据 (只加载一次)
# ============================================================
print("正在加载 2020-2022 训练数据...")
DF_5M = load_klines('5m', '2020-01-01', '2023-01-01')
DF_1H = load_klines('1h', '2020-01-01', '2023-01-01')
print(f" 5m: {len(DF_5M)} 条, 1h: {len(DF_1H)} 条")
print("数据加载完成。\n")
def build_params(trial: optuna.Trial) -> dict:
"""从 Optuna trial 构建完整参数字典"""
p = {}
# --- 指标参数 ---
p['bb_period'] = trial.suggest_int('bb_period', 10, 50)
p['bb_std'] = trial.suggest_float('bb_std', 1.0, 3.5, step=0.1)
p['kc_period'] = trial.suggest_int('kc_period', 10, 50)
p['kc_mult'] = trial.suggest_float('kc_mult', 0.5, 3.0, step=0.1)
p['dc_period'] = trial.suggest_int('dc_period', 10, 50)
p['ema_fast'] = trial.suggest_int('ema_fast', 3, 20)
p['ema_slow'] = trial.suggest_int('ema_slow', 15, 60)
p['macd_fast'] = trial.suggest_int('macd_fast', 6, 20)
p['macd_slow'] = trial.suggest_int('macd_slow', 18, 40)
p['macd_signal'] = trial.suggest_int('macd_signal', 5, 15)
p['adx_period'] = trial.suggest_int('adx_period', 7, 30)
p['st_period'] = trial.suggest_int('st_period', 5, 20)
p['st_mult'] = trial.suggest_float('st_mult', 1.0, 5.0, step=0.1)
p['rsi_period'] = trial.suggest_int('rsi_period', 7, 28)
p['stoch_k'] = trial.suggest_int('stoch_k', 5, 21)
p['stoch_d'] = trial.suggest_int('stoch_d', 2, 7)
p['stoch_smooth'] = trial.suggest_int('stoch_smooth', 2, 7)
p['cci_period'] = trial.suggest_int('cci_period', 10, 40)
p['wr_period'] = trial.suggest_int('wr_period', 7, 28)
p['wma_period'] = trial.suggest_int('wma_period', 10, 50)
# --- 信号阈值参数 ---
p['bb_oversold'] = trial.suggest_float('bb_oversold', -0.3, 0.3, step=0.05)
p['bb_overbought'] = trial.suggest_float('bb_overbought', 0.7, 1.3, step=0.05)
p['kc_oversold'] = trial.suggest_float('kc_oversold', -0.3, 0.3, step=0.05)
p['kc_overbought'] = trial.suggest_float('kc_overbought', 0.7, 1.3, step=0.05)
p['dc_oversold'] = trial.suggest_float('dc_oversold', 0.0, 0.3, step=0.05)
p['dc_overbought'] = trial.suggest_float('dc_overbought', 0.7, 1.0, step=0.05)
p['adx_threshold'] = trial.suggest_float('adx_threshold', 15, 35, step=1)
p['rsi_overbought'] = trial.suggest_float('rsi_overbought', 60, 85, step=1)
p['rsi_oversold'] = trial.suggest_float('rsi_oversold', 15, 40, step=1)
p['stoch_overbought'] = trial.suggest_float('stoch_overbought', 70, 90, step=1)
p['stoch_oversold'] = trial.suggest_float('stoch_oversold', 10, 30, step=1)
p['cci_overbought'] = trial.suggest_float('cci_overbought', 80, 200, step=5)
p['cci_oversold'] = trial.suggest_float('cci_oversold', -200, -80, step=5)
p['wr_overbought'] = trial.suggest_float('wr_overbought', -30, -10, step=1)
p['wr_oversold'] = trial.suggest_float('wr_oversold', -90, -70, step=1)
# --- 权重 ---
for wk in WEIGHT_KEYS:
p[wk] = trial.suggest_float(wk, 0.0, 1.0, step=0.05)
# --- 回测参数 ---
p['open_threshold'] = trial.suggest_float('open_threshold', 0.1, 0.6, step=0.02)
p['max_positions'] = trial.suggest_int('max_positions', 1, 3)
p['take_profit_pct'] = trial.suggest_float('take_profit_pct', 0.003, 0.025, step=0.001)
# 止损约束: N单同时止损 + 手续费 <= 50U
# N * 1250 * sl_pct + N * 1.25 <= 50
# sl_pct <= (50 - N*1.25) / (N*1250)
n = p['max_positions']
max_sl = (50.0 - n * 1.25) / (n * 1250.0)
max_sl = round(max(max_sl, 0.002), 3) # 至少 0.2%
p['stop_loss_pct'] = trial.suggest_float('stop_loss_pct', 0.002, max_sl, step=0.001)
return p
def objective(trial: optuna.Trial) -> float:
params = build_params(trial)
# 确保 ema_slow > ema_fast, macd_slow > macd_fast
if params['ema_slow'] <= params['ema_fast']:
return -1e6
if params['macd_slow'] <= params['macd_fast']:
return -1e6
try:
# 计算指标
df_5m = compute_all_indicators(DF_5M, params)
df_1h = compute_all_indicators(DF_1H, params)
# 生成信号
df_5m = generate_indicator_signals(df_5m, params)
df_1h = generate_indicator_signals(df_1h, params)
# 综合得分
score = compute_composite_score(df_5m, params)
# 高时间框架过滤
score = apply_htf_filter(score, df_1h, params)
# 回测
engine = BacktestEngine(
initial_capital=1000.0,
margin_per_trade=25.0,
leverage=50,
fee_rate=0.0005,
rebate_ratio=0.70,
max_daily_drawdown=50.0,
min_hold_bars=1,
stop_loss_pct=params['stop_loss_pct'],
take_profit_pct=params['take_profit_pct'],
max_positions=params['max_positions'],
)
result = engine.run(df_5m, score, open_threshold=params['open_threshold'])
num_trades = result['num_trades']
if num_trades < 50:
return -1e6 # 交易次数太少，不可靠
total_pnl = result['total_pnl']
max_dd = result['max_daily_dd'] # 负数 (引擎已保证 >= -50)
avg_daily = result['avg_daily_pnl']
# 引擎内部已经有每日 50U 回撤熔断，这里不再硬约束
# 目标: 最大化总收益
score_val = total_pnl
# 奖励日均收益高的方案
if avg_daily >= 50:
score_val *= 1.3
elif avg_daily >= 30:
score_val *= 1.15
trial.set_user_attr('total_pnl', total_pnl)
trial.set_user_attr('num_trades', num_trades)
trial.set_user_attr('win_rate', result['win_rate'])
trial.set_user_attr('max_daily_dd', max_dd)
trial.set_user_attr('avg_daily_pnl', avg_daily)
trial.set_user_attr('profit_factor', result['profit_factor'])
return score_val
except Exception as e:
print(f"Trial {trial.number} 异常: {e}")
return -1e6
def main():
study = optuna.create_study(
direction='maximize',
sampler=TPESampler(seed=42, n_startup_trials=30),
study_name='eth_strategy_v1',
)
# 设置日志级别
optuna.logging.set_verbosity(optuna.logging.WARNING)
n_trials = 1000
print(f"开始 Optuna 优化, 共 {n_trials} 次试验 (多单并发版)...")
print("=" * 60)
def callback(study, trial):
if trial.number % 10 == 0:
best = study.best_trial
print(f"[Trial {trial.number:>4d}] "
f"当前值={trial.value:.2f} | "
f"最佳值={best.value:.2f} | "
f"PnL={best.user_attrs.get('total_pnl', 0):.1f}U | "
f"胜率={best.user_attrs.get('win_rate', 0):.1%} | "
f"日均={best.user_attrs.get('avg_daily_pnl', 0):.1f}U | "
f"最大日回撤={best.user_attrs.get('max_daily_dd', 0):.1f}U")
study.optimize(objective, n_trials=n_trials, callbacks=[callback], show_progress_bar=True)
# 输出最佳结果
best = study.best_trial
print("\n" + "=" * 60)
print("训练完成！最佳参数:")
print("=" * 60)
print(f" 目标值: {best.value:.4f}")
print(f" 总收益: {best.user_attrs.get('total_pnl', 0):.2f}U")
print(f" 交易次数: {best.user_attrs.get('num_trades', 0)}")
print(f" 胜率: {best.user_attrs.get('win_rate', 0):.2%}")
print(f" 日均收益: {best.user_attrs.get('avg_daily_pnl', 0):.2f}U")
print(f" 最大日回撤: {best.user_attrs.get('max_daily_dd', 0):.2f}U")
print(f" 盈亏比: {best.user_attrs.get('profit_factor', 0):.2f}")
# 保存最佳参数
output_path = os.path.join(os.path.dirname(__file__), 'best_params_2020_2022.json')
with open(output_path, 'w') as f:
json.dump(best.params, f, indent=2, ensure_ascii=False)
print(f"\n最佳参数已保存到: {output_path}")
if __name__ == '__main__':
main()