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基本完毕
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songkunling
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import sys
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import os
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import pandas as pd
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from datetime import datetime
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from tqdm import tqdm
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# 添加项目根目录到路径
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sys.path.insert(0, os.path.dirname(os.path.abspath(__name__)))
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from core.data_providers import BacktestDataProvider
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from core.risk import RiskController
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from execution.dynamic_weights import DynamicWeightManager # Still needed for strategy list
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from config import SYMBOL, TIMEFRAME, BACKTEST_COUNT, BACKTEST_START_DATE, BACKTEST_END_DATE, USE_DATE_RANGE, BACKTEST_CONFIG, INITIAL_CAPITAL, SIGNAL_THRESHOLDS, DEFAULT_WEIGHTS
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from logger import logger
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# Import all strategy classes
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from strategies.ma_cross import MACrossStrategy
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from strategies.rsi import RSIStrategy
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from strategies.bollinger import BollingerStrategy
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from strategies.mean_reversion import MeanReversionStrategy
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from strategies.momentum_breakout import MomentumBreakoutStrategy
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from strategies.macd import MACDStrategy
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from strategies.kdj import KDJStrategy
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from strategies.turtle import TurtleStrategy
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from strategies.daily_breakout import DailyBreakoutStrategy
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from strategies.wave_theory import WaveTheoryStrategy
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def run_full_backtest():
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"""完整的、独立的、已重构的回测流程"""
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# 1. 加载数据
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from core.utils import get_rates, initialize, shutdown
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initialize()
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if USE_DATE_RANGE:
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rates = get_rates(SYMBOL, TIMEFRAME, BACKTEST_COUNT, BACKTEST_START_DATE, BACKTEST_END_DATE)
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else:
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rates = get_rates(SYMBOL, TIMEFRAME, BACKTEST_COUNT)
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shutdown()
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if rates is None:
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logger.error("未能获取历史数据，回测终止。")
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return
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logger.info(f"实际获取数据量: {len(rates)} 条 (请求: {BACKTEST_COUNT} 条)")
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# 如果实际获取的数据量超过配置，则截取配置的数据量
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if len(rates) > BACKTEST_COUNT:
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rates = rates[-BACKTEST_COUNT:] # 取最新的数据
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logger.info(f"截取数据量为: {len(rates)} 条")
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df = pd.DataFrame(rates)
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df.set_index(pd.to_datetime(df['time'], unit='s'), inplace=True)
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# 2. 初始化组件 (data_provider, risk_controller)
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data_provider = BacktestDataProvider(df, initial_equity=INITIAL_CAPITAL)
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risk_controller = RiskController(data_provider, trade_direction=BACKTEST_CONFIG['trade_direction'])
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# weight_manager is not directly used for signal generation in this new architecture,
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# but its strategy_blueprints can be used to instantiate strategies.
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# 3. 策略实例化和信号预生成 (NEW STAGE 1)
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logger.info("开始预生成所有策略信号...")
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# Define strategy blueprints directly here or get from DynamicWeightManager if it's refactored
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# For now, let's define them directly as DynamicWeightManager is for real-time dynamic weights.
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strategy_blueprints = {
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'ma_cross': (MACrossStrategy, {}),
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'rsi': (RSIStrategy, {}),
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'bollinger': (BollingerStrategy, {}),
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'mean_reversion': (MeanReversionStrategy, {}),
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'momentum_breakout': (MomentumBreakoutStrategy, {}),
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'macd': (MACDStrategy, {}),
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'kdj': (KDJStrategy, {}),
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'turtle': (TurtleStrategy, {}),
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'daily_breakout': (DailyBreakoutStrategy, {}),
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'wave_theory': (WaveTheoryStrategy, {})
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}
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all_signals_df = pd.DataFrame(index=df.index)
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# Map strategy class names to config keys for weights
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strategy_name_to_config_key = {
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"MACrossStrategy": "ma_cross",
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"RSIStrategy": "rsi",
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"BollingerStrategy": "bollinger",
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"MeanReversionStrategy": "mean_reversion",
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"MomentumBreakoutStrategy": "momentum_breakout",
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"MACDStrategy": "macd",
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"KDJStrategy": "kdj",
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"TurtleStrategy": "turtle",
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"DailyBreakoutStrategy": "daily_breakout",
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"WaveTheoryStrategy": "wave_theory"
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}
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strategies_for_backtest = []
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for name, (strategy_class, params) in strategy_blueprints.items():
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# Pass None for data_provider, symbol, timeframe as run_backtest only needs df
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# But strategy __init__ expects them. So, pass dummy values.
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strategies_for_backtest.append(strategy_class(None, SYMBOL, TIMEFRAME, **params))
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for strategy in tqdm(strategies_for_backtest, desc="Generating Strategy Signals"):
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if hasattr(strategy, 'run_backtest') and callable(getattr(strategy, 'run_backtest')):
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try:
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strategy_signals = strategy.run_backtest(df.copy()) # Pass a copy to avoid modifying original df
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if strategy_signals is not None:
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all_signals_df[strategy.name] = strategy_signals
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logger.info(f"策略 {strategy.name} 信号生成成功")
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else:
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logger.warning(f"策略 {strategy.name} 返回空信号")
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except Exception as e:
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logger.error(f"策略 {strategy.name} 执行失败: {str(e)}")
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# 为失败的策略创建全0信号序列
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all_signals_df[strategy.name] = pd.Series(0, index=df.index)
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continue
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else:
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logger.warning(f"策略 {strategy.name} 没有实现 'run_backtest' 方法，将被跳过。")
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# 组合信号
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logger.info("开始组合策略信号...")
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combined_weighted_signals = pd.Series(0, index=df.index)
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if not all_signals_df.empty:
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weighted_signals_list = []
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for col_name in all_signals_df.columns:
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# Extract strategy class name from column name (e.g., 'MACrossStrategy')
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strategy_class_name = col_name
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config_key = strategy_name_to_config_key.get(strategy_class_name)
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if config_key and config_key in DEFAULT_WEIGHTS:
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weight = DEFAULT_WEIGHTS[config_key]
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weighted_signals_list.append(all_signals_df[col_name] * weight)
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else:
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logger.warning(f"未找到策略 {strategy_class_name} 的默认权重，使用权重1.0。")
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weighted_signals_list.append(all_signals_df[col_name] * 1.0)
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if weighted_signals_list:
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combined_weighted_signals = pd.concat(weighted_signals_list, axis=1).sum(axis=1)
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else:
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logger.warning("没有生成任何加权信号。")
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# 应用阈值得到最终信号
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final_signals = pd.Series(0, index=df.index)
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buy_threshold = SIGNAL_THRESHOLDS.get("buy_threshold", 1.0)
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sell_threshold = SIGNAL_THRESHOLDS.get("sell_threshold", -1.0)
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final_signals[combined_weighted_signals > buy_threshold] = 1
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final_signals[combined_weighted_signals < sell_threshold] = -1
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logger.info("信号预生成和组合完成。")
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# 4. 回测主循环 (NEW STAGE 2)
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logger.info(f"回测主循环开始... 数据量: {len(df)} 条")
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# Reset data_provider's internal index to 0 for the loop
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data_provider.current_index = 0
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for i in tqdm(range(len(df)), desc=f"Backtesting ({len(df)} bars)"):
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try:
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# Get current price from the data_provider (which uses its internal index)
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current_price = data_provider.get_current_price(SYMBOL)
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if not current_price:
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logger.warning(f"无法获取当前价格在索引 {i}，跳过。")
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continue
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# Get the pre-generated signal for the current bar
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current_signal = final_signals.iloc[i]
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direction = None
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if current_signal == 1:
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direction = "buy"
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elif current_signal == -1:
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direction = "sell"
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if direction:
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risk_controller.process_trading_signal(direction, current_price, abs(current_signal))
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# Monitor and update positions
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risk_controller.monitor_positions(current_price)
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# Advance data provider to the next time step
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data_provider.tick()
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except Exception as e:
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logger.error(f"回测循环中在索引 {i} 发生错误: {str(e)}")
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# 继续下一个bar，不中断整个回测
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continue
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# 5. 结束和报告 (Keep as is)
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logger.info("回测完成，生成性能报告...")
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summary = risk_controller.position_manager.get_trade_summary()
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# 打印报告
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logger.info("=" * 80)
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logger.info("回测性能报告")
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logger.info(f"总交易次数: {summary.get('total_trades', 0)}")
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logger.info(f"胜率: {summary.get('win_rate', 0):.2f}%")
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logger.info(f"总盈亏: ${summary.get('total_profit_loss', 0):.2f}")
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logger.info("=" * 80)
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# 保存交易记录
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try:
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risk_controller.position_manager.save_trade_history("backtest_trades")
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logger.info("交易记录保存成功")
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except Exception as e:
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logger.error(f"保存交易记录失败: {str(e)}")
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# 尝试手动保存
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try:
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import json
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trades = risk_controller.position_manager.closed_trades
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if trades:
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with open("backtest_trades_manual.json", 'w', encoding='utf-8') as f:
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json.dump(trades, f, ensure_ascii=False, indent=2, default=str)
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logger.info("手动保存交易记录到 backtest_trades_manual.json")
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except Exception as e2:
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logger.error(f"手动保存交易记录也失败: {str(e2)}")
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def main():
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print("=" * 60)
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print("MetaTrader 5 智能交易系统 - 回测")
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print("=" * 60)
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try:
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run_full_backtest()
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print("\n回测完成！")
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except Exception as e:
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import traceback
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print(f"\n回测出错: {e}")
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traceback.print_exc()
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if __name__ == "__main__":
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main()
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