简单波动率策略，实现+网格搜索

src/backtest_engine.py

@@ -36,6 +36,7 @@ class BacktestEngine:
             commission_rate (float): 交易佣金率。
         """
         self.data_manager = data_manager
+        self.initial_capital = initial_capital
         self.simulator = ExecutionSimulator(
             initial_capital=initial_capital,
             slippage_rate=slippage_rate,
@@ -76,6 +77,7 @@ class BacktestEngine:
 
             # 设置当前Bar到Context，供策略访问
             self.context.set_current_bar(current_bar)
+            self.simulator.update_time(current_time=current_bar.datetime)
 
             # 更新历史Bar缓存
             self._history_bars.append(current_bar)
@@ -129,7 +131,7 @@ class BacktestEngine:
             for symbol_held, quantity in positions_to_close.items():
                 if quantity != 0:
                     print(f"[{last_processed_bar.datetime}] 回测结束平仓: 平仓 {symbol_held} ({quantity} 手) @ {last_processed_bar.close:.2f}。")
-                    direction = "SELL" if quantity > 0 else "BUY"
+                    direction = "CLOSE_LONG" if quantity > 0 else "CLOSE_SELL"
                     volume = abs(quantity)
                     
                     # 使用当前合约的最后一根Bar的价格进行平仓
@@ -148,6 +150,17 @@ class BacktestEngine:
         print(f"总计处理了 {len(self.portfolio_snapshots)} 根K线。")
         print(f"总计发生了 {len(self.trade_history)} 笔交易。")
 
+        final_portfolio_value = 0.0
+        if last_processed_bar:
+            final_portfolio_value = self.simulator.get_portfolio_value(last_processed_bar)
+        else:  # 如果数据为空，或者回测根本没跑，则净值为初始资金
+            final_portfolio_value = self.initial_capital
+
+        total_return_percentage = ((final_portfolio_value - self.initial_capital) / self.initial_capital) * 100
+
+        print(f"最终总净值: {final_portfolio_value:.2f}")
+        print(f"总收益率: {total_return_percentage:.2f}%")
+
     def get_backtest_results(self) -> Dict[str, Any]:
         """
         返回回测结果数据，供结果分析模块使用。