实现单品种连续多合约回测

data/tqsdk/tq_copy_data.py

@@ -160,7 +160,7 @@ def collect_and_save_tqsdk_data_stream(
             save_folder = os.path.join(output_dir, safe_symbol)
             os.makedirs(save_folder, exist_ok=True)
 
-            file_name = f"{safe_symbol}_{freq_folder}_{start_date_str.replace('-', '')}_{end_date_str.replace('-', '')}_{freq}.csv"
+            file_name = f"{safe_symbol}_{freq}.csv"
             file_path = os.path.join(save_folder, file_name)
 
             df.to_csv(file_path, index=True)
@@ -190,10 +190,10 @@ if __name__ == "__main__":
     # 示例1: 在回测模式下获取沪深300指数主连的日线数据 (用于历史回测)
     # 这种方式适合获取相对较短或中等长度的历史K线数据。
     df_if_backtest_daily = collect_and_save_tqsdk_data_stream(
-        symbol="SHFE.rb2501",
+        symbol="SHFE.rb2410",
-        freq="min1",
+        freq="min60",
-        start_date_str="2024-09-01",
+        start_date_str="2024-05-01",
-        end_date_str="2024-12-01",
+        end_date_str="2024-09-01",
         mode="backtest",  # 指定为回测模式
         tq_user=TQ_USER_NAME,
         tq_pwd=TQ_PASSWORD

main.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "id": "initial_id",
    "metadata": {
     "ExecuteTime": {
@@ -19,7 +19,7 @@
       "The autoreload extension is already loaded. To reload it, use:\n",
       "  %reload_ext autoreload\n",
       "初始化数据管理器...\n",
-      "数据加载成功: /mnt/d/PyProject/NewQuant/data/data/SHFE_rb2501/SHFE_rb2501_m60_20240901_20241201_min60.csv\n",
+      "数据加载成功: /mnt/d/PyProject/NewQuant/data/data/SHFE_rb2501/SHFE_rb2501_min60.csv\n",
       "数据范围从 2024-08-30 14:00:00 到 2024-11-29 21:00:00\n",
       "总计 404 条记录。\n",
       "\n",
@@ -444,9 +444,12 @@
       "[2024-11-29 14:00:00] Strategy processing Bar. Current close price: 3318.00. Current Portfolio Value: 99973.05\n",
       "[2024-11-29 21:00:00] Strategy processing Bar. Current close price: 3301.00. Current Portfolio Value: 99990.05\n",
       "Bar 对象流生成完毕。\n",
+      "\n",
+      "--- 回测片段结束，检查并平仓所有持仓 ---\n",
+      "[2024-11-29 21:00:00] 回测结束平仓: 平仓 SHFE_rb2501 (-1 手) @ 3301.00。\n",
       "--- 回测结束 ---\n",
       "总计处理了 404 根K线。\n",
-      "总计发生了 1 笔交易。\n",
+      "总计发生了 2 笔交易。\n",
       "\n",
       "回测运行完毕。\n",
       "\n",
@@ -462,11 +465,12 @@
       "最大回撤           : 0.63%\n",
       "夏普比率           : -0.02\n",
       "卡玛比率           : -0.04\n",
-      "总交易次数          : 1\n",
+      "总交易次数          : 2\n",
-      "交易成本           : 0.66\n",
+      "交易成本           : 1.32\n",
       "\n",
       "--- 部分交易明细 (最近5笔) ---\n",
       "  2024-08-30 21:00:00 | SELL       | SHFE_rb2501 | Vol: 1 | Price: 3291.70 | Commission: 0.66\n",
+      "  2024-11-29 21:00:00 | BUY        | SHFE_rb2501 | Vol: 1 | Price: 3304.30 | Commission: 0.66\n",
       "正在绘制绩效图表...\n"
      ]
     },
@@ -512,7 +516,7 @@
     "def main():\n",
     "    # --- 配置参数 ---\n",
     "    # 获取当前脚本所在目录，假设数据文件在项目根目录下的 data 文件夹内\n",
-    "    data_file_path = '/mnt/d/PyProject/NewQuant/data/data/SHFE_rb2501/SHFE_rb2501_m60_20240901_20241201_min60.csv'\n",
+    "    data_file_path = '/mnt/d/PyProject/NewQuant/data/data/SHFE_rb2501/SHFE_rb2501_min60.csv'\n",
     "\n",
     "    initial_capital = 100000.0\n",
     "    slippage_rate = 0.001  # 假设每笔交易0.1%的滑点\n",
@@ -536,6 +540,7 @@
     "    engine = BacktestEngine(\n",
     "        data_manager=data_manager,\n",
     "        strategy_class=SimpleLimitBuyStrategy,\n",
+    "        current_segment_symbol='SHFE_rb2501',\n",
     "        strategy_params=strategy_parameters,\n",
     "        initial_capital=initial_capital,\n",
     "        slippage_rate=slippage_rate,\n",
@@ -582,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "id": "9dd93e564f0e2b55",
    "metadata": {
     "ExecuteTime": {
@@ -597,7 +602,7 @@
        "'/home/liaozhaorun/.fonts/simhei.ttf'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -611,7 +616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "id": "a14196c49af33461",
    "metadata": {
     "ExecuteTime": {

src/backtest_engine.py

@@ -20,6 +20,7 @@ class BacktestEngine:
                  data_manager: DataManager,
                  strategy_class: Type[Strategy],
                  strategy_params: Dict[str, Any],
+                 current_segment_symbol: str,
                  initial_capital: float = 100000.0,
                  slippage_rate: float = 0.0001,
                  commission_rate: float = 0.0002):
@@ -41,6 +42,7 @@ class BacktestEngine:
             commission_rate=commission_rate
         )
         self.context = BacktestContext(self.data_manager, self.simulator)
+        self.current_segment_symbol = current_segment_symbol
 
         # 实例化策略
         self.strategy = strategy_class(self.context, **strategy_params)
@@ -107,6 +109,8 @@ class BacktestEngine:
             self.portfolio_snapshots.append(snapshot)
             self.all_bars.append(current_bar)
 
+            last_processed_bar = current_bar
+
             # 记录交易历史（从模拟器获取）
             # 简化处理：每次获取模拟器中的所有交易历史，并更新引擎的trade_history
             # 更好的做法是模拟器提供一个方法，返回自上次查询以来的新增交易
@@ -117,7 +121,25 @@ class BacktestEngine:
             # 这里可以做一个增量获取，或者简单地在循环结束后统一获取
             # 目前我们在执行模拟器中已经将成交记录在了 trade_log 中，所以这里不用重复记录，
             # 而是等到回测结束后再统一获取。
-            pass  # 不在此处记录 self.trade_history
+            # 不在此处记录 self.trade_history
+
+        print("\n--- 回测片段结束，检查并平仓所有持仓 ---")
+        if last_processed_bar: # 确保至少有一根Bar被处理过
+            positions_to_close = self.simulator.get_current_positions()
+            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"
+                    volume = abs(quantity)
+                    
+                    # 使用当前合约的最后一根Bar的价格进行平仓
+                    # 注意：这里假设平仓的symbol_held就是当前segment的symbol
+                    # 如果策略可能同时持有其他旧合约的仓位（多主力同时持有），这里需要更复杂的逻辑来获取正确的平仓价格
+                    # 但在主力合约切换场景下，通常只持有当前主力合约的仓位。
+                    rollover_order = Order(symbol=symbol_held, direction=direction, volume=volume, price_type="MARKET")
+                    self.simulator.send_order(rollover_order, current_bar=last_processed_bar)
+        else:
+            print("没有处理任何Bar，无需平仓。")
 
         # 回测结束后，获取所有交易记录
         self.trade_history = self.simulator.get_trade_history()
@@ -135,4 +157,10 @@ class BacktestEngine:
             "trade_history": self.trade_history,
             "initial_capital": self.simulator.initial_capital,  # 或 self.initial_capital
             "all_bars": self.all_bars
-        }
+        }
+
+    def get_simulator(self) -> ExecutionSimulator:  # <--- 新增的方法
+        """
+        返回引擎内部的 ExecutionSimulator 实例，以便外部可以访问和修改其状态。
+        """
+        return self.simulator

src/execution_simulator.py

@@ -236,7 +236,7 @@ class ExecutionSimulator:
             float: 当前的投资组合总价值。
         """
         total_value = self.cash
-        
+
         # 在单品种场景下，我们假设 self.positions 最多只包含一个品种
         # 并且这个品种就是 current_bar.symbol 所代表的品种
         symbol_in_position = list(self.positions.keys())[0] if self.positions else None
@@ -246,7 +246,7 @@ class ExecutionSimulator:
             # 持仓市值 = 数量 * 当前市场价格 (current_bar.close)
             # 无论多头(quantity > 0)还是空头(quantity < 0)，这个计算都是正确的
             total_value += quantity * current_bar.close
-            
+
             # 您也可以选择在这里打印调试信息
             # print(f"  DEBUG Portfolio Value Calculation: Cash={self.cash:.2f}, "
             #       f"Position for {symbol_in_position}: {quantity} @ {current_bar.close:.2f}, "
@@ -254,7 +254,7 @@ class ExecutionSimulator:
 
         # 如果没有持仓，或者持仓品种与当前Bar品种不符 (理论上单品种不会发生)
         # 那么 total_value 依然是 self.cash
-        
+
         return total_value
 
     def get_current_positions(self) -> Dict[str, int]:
@@ -268,3 +268,22 @@ class ExecutionSimulator:
         返回所有成交记录的副本。
         """
         return self.trade_log.copy()
+
+    def reset(self, new_initial_capital: float = None, new_initial_positions: Dict[str, int] = None) -> None:
+        """
+        重置模拟器状态到新的初始条件。
+        可以在总回测开始时调用，或在合约切换时调整资金和持仓。
+        """
+        print("ExecutionSimulator: 重置状态。")
+        self.cash = new_initial_capital if new_initial_capital is not None else self.initial_capital
+        self.positions = new_initial_positions.copy() if new_initial_positions is not None else {}
+        self.trade_history = []
+        self.current_orders = {}
+
+    def clear_trade_history(self) -> None:
+        """
+        清空当前模拟器的交易历史。
+        在每个合约片段结束时调用，以便我们只收集当前片段的交易记录。
+        """
+        print("ExecutionSimulator: 清空交易历史。")
+        self.trade_history = []

src/strategies/simple_limit_buy_strategy.py

@@ -101,10 +101,10 @@ class SimpleLimitBuyStrategy(Strategy):
             order = Order(
                 id=order_id,
                 symbol=self.symbol,
-                direction="SELL",
+                direction="BUY",
                 volume=trade_volume,
-                price_type="LIMIT",
+                price_type="MARKET",
-                limit_price=limit_price,
+                # limit_price=limit_price,
                 submitted_time=bar.datetime
             )