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