基本完毕

execution/backtest_engine.py

@@ -0,0 +1,95 @@
+import pandas as pd
+from tqdm import tqdm
+from core.risk import RiskController
+from config import SIGNAL_THRESHOLDS, BACKTEST_CONFIG, SPREAD
+from logger import logger
+
+class BacktestEngine:
+    def __init__(self, df, trade_direction="both"):
+        self.df = df
+        self.trade_direction = trade_direction
+        self.spread = BACKTEST_CONFIG.get("spread", SPREAD)
+        
+    def run_strategy(self, strategy):
+        signals = strategy.run_backtest(self.df)
+        buy_count = (signals == 1).sum()
+        sell_count = (signals == -1).sum()
+        logger.info(f"策略 {strategy.__class__.__module__} 信号统计 - 买入: {buy_count}, 卖出: {sell_count}")
+        return signals
+
+    def combine_signals(self, signals_list, weights):
+        df_signals = pd.concat(signals_list, axis=1).fillna(0)
+        weighted_signals = df_signals * weights
+        combined = weighted_signals.sum(axis=1)
+
+        def apply_threshold(score):
+            if score > SIGNAL_THRESHOLDS["buy_threshold"]:
+                return 1
+            elif score < SIGNAL_THRESHOLDS["sell_threshold"]:
+                return -1
+            else:
+                return 0
+
+        combined_signal = combined.apply(apply_threshold)
+        buy_signals = (combined_signal == 1).sum()
+        sell_signals = (combined_signal == -1).sum()
+        logger.info(f"组合信号统计 - 买入: {buy_signals}, 卖出: {sell_signals}")
+        return combined_signal
+
+    def run_backtest(self, signals, symbol="XAUUSD"):
+        df = self.df.copy()
+        df['signal'] = signals.shift(1).fillna(0)
+        
+        risk_controller = RiskController(self.trade_direction)
+        logger.info(f"回测开始: 交易方向={self.trade_direction}")
+        
+        # 使用tqdm创建进度条
+        for i in tqdm(range(1, len(df)), desc=f"Backtesting ({len(df)} bars)"):
+            current_signal = df['signal'].iloc[i]
+            # 计算考虑双向点差的买卖价格
+            close_price = df['close'].iloc[i]
+            spread_points = self.spread
+            spread_half = spread_points * 0.01 / 2  # XAUUSD: 1点 = 0.01，双向点差各一半
+            
+            current_price = {
+                'bid': close_price - spread_half,  # 卖出价格（中间价 - 点差/2）
+                'ask': close_price + spread_half,  # 买入价格（中间价 + 点差/2）
+                'last': close_price  # 最后成交价（中间价）
+            }
+            
+            direction = None
+            if current_signal == 1:
+                direction = "buy"
+            elif current_signal == -1:
+                direction = "sell"
+            
+            if direction:
+                risk_controller.process_trading_signal(
+                    direction, current_price, abs(current_signal), dry_run=True
+                )
+            
+            risk_controller.monitor_positions(current_price, dry_run=True)
+        
+        risk_controller.position_manager.force_close_all_positions(df['close'].iloc[-1], dry_run=True)
+        
+        logger.info("回测完成，生成性能报告...")
+        summary = risk_controller.position_manager.get_trade_summary()
+        
+        logger.info("=" * 80)
+        logger.info("回测性能报告")
+        logger.info("=" * 80)
+        logger.info(f"    总交易次数: {summary['total_trades']}")
+        logger.info(f"    盈利次数: {summary['winning_trades']}")
+        logger.info(f"    亏损次数: {summary['losing_trades']}")
+        logger.info(f"    胜率: {summary['win_rate']:.2f}%")
+        logger.info("-" * 40)
+        logger.info(f"    总盈亏: ${summary['total_profit_loss']:.2f}")
+        logger.info(f"    平均每笔交易盈亏: ${summary['avg_profit_loss']:.2f}")
+        logger.info(f"    最大盈利: ${summary['max_profit']:.2f}")
+        logger.info(f"    最大亏损: ${summary['max_loss']:.2f}")
+        logger.info("=" * 80)
+
+        risk_controller.position_manager.save_to_csv("backtest_trades.csv")
+        risk_controller.position_manager.save_to_json("backtest_trades.json")
+
+        return summary