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加入一个回测，

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ddrwode
2026-03-04 18:02:02 +08:00
parent de22d1f3ae
commit 4b5a66d588
5 changed files with 60414 additions and 16 deletions
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1.html Normal file
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bb_backtest_20250101_20251231_trades.csv Normal file
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bb_backtest_20260101_20260228_trades.csv Normal file
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bb_backtest_march_2026.py
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@@ -56,9 +56,10 @@ class BollingerBandBacktest:
self.fee_rate = 0.0005 # 万五手续费
self.rebate_rate = 0.9 # 90%返佣
self.margin_ratio_1 = 0.01 # 首次开仓保证金比例1%
self.margin_ratio_2 = 0.02 # 加仓保证金比例2%
self.margin_ratio_2 = 0.01 # 加仓保证金比例1%
self.stop_loss_ratio = 0.5 # 止损比例50%
self.entry_slippage = 0.0002 # 开仓滑点（2bps）
self.rebate_credit_hour = 8 # 次日早上8点返佣到账（上海时间）
# 账户状态
self.capital = self.initial_capital
@@ -81,6 +82,9 @@ class BollingerBandBacktest:
# 交易记录
self.trades = []
self.daily_pnl = []
self.pending_rebates = [] # 待到账返佣队列
self.total_rebate_credited = 0.0
self.current_run_label = "default"
def calculate_bollinger_bands(self, df):
"""计算布林带"""
@@ -91,9 +95,54 @@ class BollingerBandBacktest:
df['middle'] = df['sma']
return df
def get_net_fee(self, fee):
"""计算扣除返佣后的净手续费"""
return fee * (1 - self.rebate_rate)
def get_rebate_amount(self, fee):
"""计算返佣金额"""
return fee * self.rebate_rate
def schedule_rebate(self, fee, timestamp):
"""登记返佣到账时间（次日08:00，上海时间）"""
rebate = self.get_rebate_amount(fee)
if rebate <= 0:
return
trade_utc = pd.Timestamp(timestamp, tz='UTC')
trade_local = trade_utc.tz_convert('Asia/Shanghai')
credit_local = (trade_local + pd.Timedelta(days=1)).normalize() + pd.Timedelta(hours=self.rebate_credit_hour)
credit_utc = credit_local.tz_convert('UTC').tz_localize(None)
self.pending_rebates.append({
'credit_time': credit_utc,
'amount': rebate,
'trade_time': trade_utc.tz_localize(None),
})
def apply_pending_rebates(self, current_time):
"""处理当前时刻前应到账的返佣"""
if not self.pending_rebates:
return
remaining = []
for item in self.pending_rebates:
if item['credit_time'] <= current_time:
amount = item['amount']
self.capital += amount
self.total_rebate_credited += amount
self.trades.append({
'timestamp': current_time.strftime('%Y-%m-%d %H:%M'),
'action': '返佣到账',
'price': None,
'size': None,
'rebate': amount,
'capital': self.capital,
'reason': f"次日08:00返佣到账({item['trade_time'].strftime('%Y-%m-%d %H:%M')}手续费)"
})
logger.info(
f"[{current_time.strftime('%Y-%m-%d %H:%M')}] 返佣到账: {amount:.4f}U | 可用资金: {self.capital:.4f}U"
)
else:
remaining.append(item)
self.pending_rebates = remaining
def apply_entry_slippage(self, price, direction):
"""按方向施加不利滑点"""
@@ -305,8 +354,7 @@ class BollingerBandBacktest:
position_size = margin * self.leverage / price
fee = position_size * price * self.fee_rate
net_fee = self.get_net_fee(fee)
required = margin + net_fee
required = margin + fee
if self.capital < required:
logger.warning(
f"[{timestamp}] 可用资金不足，无法开仓 | 需要: {required:.4f}U | 可用: {self.capital:.4f}U"
@@ -315,6 +363,7 @@ class BollingerBandBacktest:
# 冻结保证金并扣除手续费
self.capital -= required
self.schedule_rebate(fee, timestamp)
if self.position_count == 0:
self.position = position_size if direction == 'long' else -position_size
@@ -341,14 +390,16 @@ class BollingerBandBacktest:
'price': price,
'size': position_size,
'margin': margin,
'fee': net_fee,
'fee': fee,
'rebate': self.get_rebate_amount(fee),
'capital': self.capital,
'reason': reason
})
logger.info(
f"[{timestamp}] {action} @ {price:.2f} | 仓位: {position_size:.4f} | "
f"保证金: {margin:.4f}U | 手续费: {net_fee:.4f}U | 可用资金: {self.capital:.4f}U | {reason}"
f"保证金: {margin:.4f}U | 手续费: {fee:.4f}U | 返佣待到账: {self.get_rebate_amount(fee):.4f}U | "
f"可用资金: {self.capital:.4f}U | {reason}"
)
return True
@@ -368,10 +419,10 @@ class BollingerBandBacktest:
pnl = close_size * (self.entry_price - price)
fee = close_size * price * self.fee_rate
net_fee = self.get_net_fee(fee)
released_margin = self.total_margin * ratio
self.capital += released_margin + pnl - net_fee
self.capital += released_margin + pnl - fee
self.schedule_rebate(fee, timestamp)
if ratio >= 0.999:
self.position = 0
@@ -397,13 +448,15 @@ class BollingerBandBacktest:
'price': price,
'size': close_size,
'pnl': pnl,
'fee': net_fee,
'fee': fee,
'rebate': self.get_rebate_amount(fee),
'capital': self.capital,
'reason': reason
})
logger.info(f"[{timestamp}] 平仓{int(ratio*100)}% @ {price:.2f} | "
f"盈亏: {pnl:.4f}U | 手续费: {net_fee:.4f}U | 可用资金: {self.capital:.4f}U | {reason}")
f"盈亏: {pnl:.4f}U | 手续费: {fee:.4f}U | 返佣待到账: {self.get_rebate_amount(fee):.4f}U | "
f"可用资金: {self.capital:.4f}U | {reason}")
return True
def check_stop_loss(self, high, low, timestamp):
@@ -427,14 +480,34 @@ class BollingerBandBacktest:
def run_backtest(self, start_date, end_date):
"""运行回测"""
# 重置状态，支持同一实例重复回测
self.capital = self.initial_capital
self.position = 0
self.position_count = 0
self.entry_price = 0
self.total_margin = 0
self.mid_closed_half = False
self.trades = []
self.daily_pnl = []
self.pending_rebates = []
self.total_rebate_credited = 0.0
self.clear_delay_reversal()
logger.info(f"{'='*80}")
logger.info(f"开始回测: {start_date} ~ {end_date}")
logger.info(f"初始资金: {self.initial_capital}U | 杠杆: {self.leverage}x | BB({self.bb_period}, {self.bb_std})")
logger.info(f"{'='*80}")
# 从数据库加载数据
start_ts = int(pd.Timestamp(start_date).timestamp() * 1000)
end_ts = int(pd.Timestamp(end_date).timestamp() * 1000)
start_dt = pd.Timestamp(start_date)
end_dt = pd.Timestamp(end_date)
if isinstance(end_date, str) and len(end_date) <= 10:
end_dt = end_dt + pd.Timedelta(days=1) - pd.Timedelta(milliseconds=1)
self.current_run_label = f"{start_dt.strftime('%Y%m%d')}_{end_dt.strftime('%Y%m%d')}"
start_ts = int(start_dt.timestamp() * 1000)
end_ts = int(end_dt.timestamp() * 1000)
query = BitMartETH5M.select().where(
(BitMartETH5M.id >= start_ts) & (BitMartETH5M.id <= end_ts)
@@ -481,6 +554,9 @@ class BollingerBandBacktest:
lower = row['lower']
middle = row['middle']
# 先处理当前时刻返佣到账
self.apply_pending_rebates(row['datetime'])
# 检查止损
if self.check_stop_loss(high, low, timestamp):
continue
@@ -579,6 +655,9 @@ class BollingerBandBacktest:
total_pnl = sum([t.get('pnl', 0) for t in self.trades])
total_fee = sum([t.get('fee', 0) for t in self.trades])
total_rebate_expected = sum([t.get('rebate', 0) for t in self.trades if t.get('fee', 0) > 0])
pending_rebate = sum([x['amount'] for x in self.pending_rebates])
realized_net_fee = total_fee - self.total_rebate_credited
final_capital = self.capital
roi = (final_capital - self.initial_capital) / self.initial_capital * 100
@@ -586,7 +665,11 @@ class BollingerBandBacktest:
logger.info(f"初始资金: {self.initial_capital:.2f}U")
logger.info(f"最终资金: {final_capital:.2f}U")
logger.info(f"总盈亏: {total_pnl:.2f}U")
logger.info(f"总手续费: {total_fee:.2f}U")
logger.info(f"总手续费(开平全额): {total_fee:.2f}U")
logger.info(f"返佣应返总额: {total_rebate_expected:.2f}U")
logger.info(f"返佣已到账: {self.total_rebate_credited:.2f}U")
logger.info(f"返佣待到账: {pending_rebate:.2f}U")
logger.info(f"已实现净手续费: {realized_net_fee:.2f}U")
logger.info(f"净收益: {final_capital - self.initial_capital:.2f}U")
logger.info(f"收益率: {roi:.2f}%")
logger.info(f"总交易次数: {total_trades}")
@@ -597,7 +680,7 @@ class BollingerBandBacktest:
# 保存交易记录
trades_df = pd.DataFrame(self.trades)
output_file = 'bb_backtest_march_2026_trades.csv'
output_file = f'bb_backtest_{self.current_run_label}_trades.csv'
trades_df.to_csv(output_file, index=False, encoding='utf-8-sig')
logger.info(f"\n交易记录已保存到: {output_file}")
@@ -606,10 +689,15 @@ class BollingerBandBacktest:
'final_capital': final_capital,
'total_pnl': total_pnl,
'total_fee': total_fee,
'total_rebate_expected': total_rebate_expected,
'total_rebate_credited': self.total_rebate_credited,
'pending_rebate': pending_rebate,
'realized_net_fee': realized_net_fee,
'roi': roi,
'total_trades': total_trades,
'win_trades': win_trades,
'loss_trades': loss_trades,
'trades_file': output_file,
'trades': self.trades
}

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bb_sweep_results.csv
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