add files

backtest.py

@@ -1,5 +1,8 @@
 import pandas as pd
-
+from trade_logger import trade_logger
+from risk_management import RiskController
+from config import SIGNAL_THRESHOLDS
+from logger import logger
 class BacktestEngine:
     def __init__(self, df):
         """
@@ -11,9 +14,15 @@ class BacktestEngine:
         """
         执行策略的run_backtest，得到信号序列
         """
-        return strategy.run_backtest(self.df)
+        signals = strategy.run_backtest(self.df)
+        # 记录策略信号统计
+        buy_count = (signals == 1).sum()
+        sell_count = (signals == -1).sum()
+        neutral_count = (signals == 0).sum()
+        logger.info(f"策略 {strategy.__class__.__module__} 信号统计 - 买入: {buy_count}, 卖出: {sell_count}, 中性: {neutral_count}")
+        return signals
 
-    def combine_signals(self, signals_list, weights, buy_threshold, sell_threshold):
+    def combine_signals(self, signals_list, weights):
         """
         多策略信号加权合成，并根据阈值生成最终信号
         返回合成信号序列
@@ -23,14 +32,21 @@ class BacktestEngine:
         combined = weighted_signals.sum(axis=1)
 
         def apply_threshold(score):
-            if score >= buy_threshold:
+            if score > SIGNAL_THRESHOLDS["buy_threshold"]:
                 return 1
-            elif score <= sell_threshold:
+            elif score < SIGNAL_THRESHOLDS["sell_threshold"]:
                 return -1
             else:
                 return 0
 
         combined_signal = combined.apply(apply_threshold)
+        
+        # 记录信号统计
+        buy_signals = (combined_signal == 1).sum()
+        sell_signals = (combined_signal == -1).sum()
+        neutral_signals = (combined_signal == 0).sum()
+        logger.info(f"信号统计 - 买入: {buy_signals}, 卖出: {sell_signals}, 中性: {neutral_signals}")
+        
         return combined_signal
 
     def calc_returns(self, signals):
@@ -43,3 +59,101 @@ class BacktestEngine:
         df['strategy_returns'] = df['signal'] * df['returns']
         cum_ret = (1 + df['strategy_returns']).cumprod() - 1
         return cum_ret
+    
+    def calc_returns_with_trades(self, signals, symbol="XAUUSD"):
+        """
+        根据信号计算策略回测收益率，并记录每一笔交易
+        使用RiskController进行风险管理，防止频繁交易
+        """
+        df = self.df.copy()
+        df['signal'] = signals.shift(1).fillna(0)  # 防止未来函数
+        
+        # 初始化风险管理器
+        risk_controller = RiskController()
+        
+        # 记录交易
+        in_position = False
+        position_direction = None
+        position_price = 0
+        position_time = None
+        last_trade_time = None
+        min_trade_interval = 0  # 取消最小交易间隔限制
+        
+        for i in range(1, len(df)):
+            current_signal = df['signal'].iloc[i]
+            current_price = df['close'].iloc[i]
+            current_time = df.index[i] if hasattr(df.index, 'to_pydatetime') else i
+            
+            # 取消交易间隔限制检查
+            can_trade = True
+            
+            # 定期打印进度（每1000根K线）
+            if i % 1000 == 0:
+                logger.info(f"回测进度: {i}/{len(df)-1}")
+            
+            # 开仓信号
+            if current_signal != 0 and not in_position and can_trade:
+                direction = 'buy' if current_signal == 1 else 'sell'
+                
+                # 检查风险管理器是否允许交易
+                if risk_controller.should_allow_trade(direction):
+                    trade_logger.open_position(symbol, direction, current_price, current_time, f"signal_{current_signal}")
+                    in_position = True
+                    position_direction = direction
+                    position_price = current_price
+                    position_time = current_time
+                    last_trade_time = current_time
+                    
+                    # 更新RiskController的持仓信息
+                    position_type = "long" if direction == 'buy' else "short"
+                    risk_controller.update_position_entry(current_price, position_type)
+            
+            # 持仓时检查风险管理条件（无论信号如何）
+            if in_position:
+                # 使用RiskController检查风险管理条件
+                risk_action, risk_reason = risk_controller.check_risk_management(current_price)
+                
+                if risk_action != "none":
+                    trade_logger.close_position(symbol, current_price, current_time, f"risk_management_{risk_action}")
+                    risk_controller.execute_risk_action(risk_action, risk_reason)
+                    in_position = False
+                    continue  # 跳过后续信号处理
+            
+            # 中性信号处理
+            elif current_signal == 0 and in_position:
+                trade_logger.close_position(symbol, current_price, current_time, "signal_to_neutral")
+                in_position = False
+            
+            # 反向信号（取消交易间隔限制）
+            elif current_signal != 0 and in_position and can_trade:
+                # 只有当信号明显反向时才平仓并反向
+                if (current_signal == 1 and position_direction == 'sell') or \
+                   (current_signal == -1 and position_direction == 'buy'):
+                    # 先平仓
+                    trade_logger.close_position(symbol, current_price, current_time, "signal_reverse")
+                    in_position = False
+                    
+                    # 立即开新仓（取消等待间隔）
+                    direction = 'buy' if current_signal == 1 else 'sell'
+                    if risk_controller.should_allow_trade(direction):
+                        trade_logger.open_position(symbol, direction, current_price, current_time, f"signal_{current_signal}")
+                        in_position = True
+                        position_direction = direction
+                        position_price = current_price
+                        position_time = current_time
+                        last_trade_time = current_time
+                        
+                        # 更新RiskController的持仓信息
+                        position_type = "long" if direction == 'buy' else "short"
+                        risk_controller.update_position_entry(current_price, position_type)
+            
+            # 持仓到最后
+            elif in_position and i == len(df) - 1:
+                trade_logger.close_position(symbol, current_price, current_time, "end_of_backtest")
+                in_position = False
+        
+        # 计算收益率
+        df['returns'] = df['close'].pct_change()
+        df['strategy_returns'] = df['signal'] * df['returns']
+        cum_ret = (1 + df['strategy_returns']).cumprod() - 1
+        return cum_ret