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