276 lines
12 KiB
Python
276 lines
12 KiB
Python
# src/backtest_engine.py
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from datetime import datetime
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from typing import Type, Dict, Any, List, Optional
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import numpy as np
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import pandas as pd
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from src.indicators.base_indicators import Indicator
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# 导入所有需要协调的模块
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from .core_data import Bar, Order, Trade, PortfolioSnapshot
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from .data_manager import DataManager
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from .execution_simulator import ExecutionSimulator
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from .backtest_context import BacktestContext
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from .strategies.base_strategy import Strategy
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class BacktestEngine:
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"""
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回测引擎：协调数据流、策略执行、订单模拟和结果记录。
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"""
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def __init__(self,
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data_manager: DataManager,
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strategy_class: Type[Strategy],
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strategy_params: Dict[str, Any],
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# current_segment_symbol: str, # 这个参数不再需要，因为 symbol 会动态更新
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initial_capital: float = 100000.0,
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slippage_rate: float = 0.0001,
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commission_rate: float = 0.0002,
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roll_over_mode: bool = False,
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start_time: Optional[datetime] = None, # 新增开始时间
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end_time: Optional[datetime] = None, # 新增结束时间
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indicators: List[Indicator] = [],
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): # 新增换月模式参数
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"""
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初始化回测引擎。
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Args:
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data_manager (DataManager): 已经初始化好的数据管理器实例。
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strategy_class (Type[Strategy]): 策略类（而不是实例），引擎会负责实例化。
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strategy_params (Dict[str, Any]): 传递给策略的参数字典。
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initial_capital (float): 初始交易资金。
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slippage_rate (float): 交易滑点率。
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commission_rate (float): 交易佣金率。
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roll_over_mode (bool): 是否启用主连合约换月模式。
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"""
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self.data_manager = data_manager
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self.initial_capital = initial_capital
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self.simulator = ExecutionSimulator(
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initial_capital=initial_capital,
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slippage_rate=slippage_rate,
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commission_rate=commission_rate
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)
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# 传入引擎自身给 context，以便 context 可以获取引擎的状态（如 is_rollover_bar）
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self.context = BacktestContext(self.data_manager, self.simulator)
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self.context.set_engine(self) # 建立 Context 到 Engine 的引用
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# self.current_segment_symbol = current_segment_symbol # 此行移除或作为内部变量动态管理
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# 实例化策略。初始 symbol 会在 run_backtest 中根据第一根 Bar 动态设置。
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self.strategy = strategy_class(self.context, symbol="INITIAL_PLACEHOLDER_SYMBOL", **strategy_params)
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self.indicators = indicators
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self.portfolio_snapshots: List[PortfolioSnapshot] = []
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self.trade_history: List[Trade] = []
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self.all_bars: List[Bar] = []
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self.close_list: List[float] = []
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self.open_list: List[float] = []
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self.high_list: List[float] = []
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self.low_list: List[float] = []
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self.volume_list: List[float] = []
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self._history_bars: List[Bar] = [] # 引擎层面保留的历史 Bar，通常供策略在 on_bar 中使用
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self._max_history_bars: int = strategy_params.get('history_bars_limit', 200)
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# 换月相关状态
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self.roll_over_mode = roll_over_mode # 是否启用换月模式
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self._last_processed_bar_symbol: Optional[str] = None # 记录上一根 K 线的 symbol
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self.is_rollover_bar: bool = False # 标记当前 K 线是否为换月 K 线（禁止开仓）
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# 新增时间过滤属性
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self.start_time = start_time
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self.end_time = end_time
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print("\n--- 回测引擎初始化完成 ---")
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print(f" 策略: {strategy_class.__name__}")
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print(f" 初始资金: {initial_capital:.2f}")
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print(f" 换月模式: {'启用' if roll_over_mode else '禁用'}")
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def run_backtest(self):
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"""
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运行整个回测流程，包含换月逻辑。
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"""
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print("\n--- 回测开始 ---")
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# 调用策略的初始化方法
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self.strategy.on_init()
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self.strategy.trading = True
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last_processed_bar: Optional[Bar] = None # 用于在换月时引用旧合约的最后一根 K 线
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# 主回测循环
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while True:
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current_bar = self.data_manager.get_next_bar()
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if current_bar is None:
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break # 没有更多数据，回测结束
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if self.start_time and current_bar.datetime < self.start_time:
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continue
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# 如果设置了结束时间，且当前K线在结束时间之后，则终止回测
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if self.end_time and current_bar.datetime >= self.end_time:
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print(f"到达结束时间 {self.end_time}，回测终止。")
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break
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# --- 换月逻辑判断和处理 (在处理 current_bar 之前进行) ---
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# 1. 重置 is_rollover_bar 标记
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self.is_rollover_bar = False
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# 4. 更新 Context 和 Simulator 的当前 Bar 和时间
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self.context.set_current_bar(current_bar)
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self.simulator.update_time(current_time=current_bar.datetime)
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# 2. 如果启用换月模式，并且检测到合约 symbol 变化
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if self.roll_over_mode and \
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self._last_processed_bar_symbol is not None and \
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current_bar.symbol != self._last_processed_bar_symbol:
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old_symbol = self._last_processed_bar_symbol
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new_symbol = current_bar.symbol
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# 确认 last_processed_bar 确实是旧合约的最后一根 K 线
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if last_processed_bar and last_processed_bar.symbol == old_symbol:
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self.strategy.log(f"检测到换月！从 [{old_symbol}] 切换到 [{new_symbol}]。"
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f"在旧合约最后一根K线 ({last_processed_bar.datetime}) 执行强制平仓和取消操作。")
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# A. 强制平仓旧合约的所有持仓
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self.simulator.force_close_all_positions_for_symbol(old_symbol, last_processed_bar)
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# B. 取消旧合约的所有挂单
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self.simulator.cancel_all_pending_orders_for_symbol(old_symbol)
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# C. 标记【当前这根 Bar (即新合约的第一根 K 线)】为换月 K 线
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# 此时 self.is_rollover_bar 变为 True，将通过 Context 传递给策略，
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# 策略在该 K 线周期内不能开仓。
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self.is_rollover_bar = True
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# D. 通知策略换月事件，让策略有机会重置内部状态
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self.strategy.on_rollover(old_symbol, new_symbol)
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else:
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self.strategy.log(f"警告: 检测到换月从 {old_symbol} 到 {new_symbol}，但 last_processed_bar 为空或与旧合约不符。"
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"强制平仓/取消操作可能未正确执行。")
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# 3. 更新策略关注的当前合约 symbol
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self.strategy.symbol = current_bar.symbol
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self.strategy.on_open_bar(current_bar.open, current_bar.symbol)
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current_indicator_dict = {}
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close_array = np.array(self.close_list)
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open_array = np.array(self.open_list)
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high_array = np.array(self.high_list)
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low_array = np.array(self.low_list)
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volume_array = np.array(self.volume_list)
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for indicator in self.indicators:
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current_indicator_dict[indicator.get_name()] = indicator.get_latest_value(
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close_array,
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open_array,
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high_array,
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low_array,
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volume_array
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)
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self.simulator.process_pending_orders(current_bar, current_indicator_dict)
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self.all_bars.append(current_bar)
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self.close_list.append(current_bar.close)
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self.open_list.append(current_bar.open)
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self.high_list.append(current_bar.high)
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self.low_list.append(current_bar.low)
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self.volume_list.append(current_bar.volume)
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# 7. 调用策略的 on_bar 方法
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# self.strategy.on_bar(current_bar)
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self.strategy.on_close_bar(current_bar)
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self.simulator.process_pending_orders(current_bar, current_indicator_dict)
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# 8. 记录投资组合快照
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current_portfolio_value = self.simulator.get_portfolio_value(current_bar)
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current_positions = self.simulator.get_current_positions()
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price_at_snapshot = {current_bar.symbol: current_bar.close} # 使用当前 Bar 的收盘价记录快照
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snapshot = PortfolioSnapshot(
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datetime=current_bar.datetime,
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total_value=current_portfolio_value,
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cash=self.simulator.cash,
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positions=current_positions,
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price_at_snapshot=price_at_snapshot
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)
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self.portfolio_snapshots.append(snapshot)
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# 9. 更新 `_last_processed_bar_symbol` 和 `last_processed_bar` 为当前 Bar，为下一轮循环做准备
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self._last_processed_bar_symbol = current_bar.symbol
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last_processed_bar = current_bar
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# --- 回测结束后的清理工作 ---
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print("\n--- 回测结束，检查并平仓所有剩余持仓 ---")
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if last_processed_bar: # 确保至少有一根 Bar 被处理过
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# 在回测结束时，强制平仓所有可能存在的剩余持仓
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# 遍历所有持仓，确保全部清算
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remaining_positions_symbols = list(self.simulator.get_current_positions().keys())
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for symbol_held in remaining_positions_symbols:
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if self.simulator.get_current_positions().get(symbol_held, 0) != 0:
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self.strategy.log(f"回测结束清理: 强制平仓合约 {symbol_held} 的剩余持仓。")
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# 使用 simulator 的 force_close_all_positions_for_symbol 方法进行清理
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self.simulator.force_close_all_positions_for_symbol(symbol_held, last_processed_bar)
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self.simulator.cancel_all_pending_orders_for_symbol(symbol_held)
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else:
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print("没有处理任何 Bar，无需平仓。")
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# 回测结束后，获取所有交易记录
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self.trade_history = self.simulator.get_trade_history()
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print("--- 回测结束 ---")
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print(f"总计处理了 {len(self.all_bars)} 根K线。")
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print(f"总计发生了 {len(self.trade_history)} 笔交易。")
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final_portfolio_value = 0.0
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if last_processed_bar:
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final_portfolio_value = self.simulator.get_portfolio_value(last_processed_bar)
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else:
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final_portfolio_value = self.initial_capital
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total_return_percentage = ((final_portfolio_value - self.initial_capital) / self.initial_capital) * 100
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print(f"最终总净值: {final_portfolio_value:.2f}")
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print(f"总收益率: {total_return_percentage:.2f}%")
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def get_backtest_results(self) -> Dict[str, Any]:
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"""
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返回回测结果数据，供结果分析模块使用。
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"""
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return {
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"portfolio_snapshots": self.portfolio_snapshots,
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"trade_history": self.trade_history,
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"initial_capital": self.simulator.initial_capital,
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"all_bars": self.all_bars
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}
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def get_simulator(self) -> ExecutionSimulator:
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return self.simulator
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def get_bar_history(self):
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return self.all_bars
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def get_price_history(self, key: str):
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if key == 'close':
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return self.close_list
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elif key == 'open':
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return self.open_list
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elif key == 'high':
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return self.high_list
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elif key == 'low':
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return self.low_list
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elif key == 'volume':
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return self.volume_list
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return None
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