1、新增dp策略

src/strategies/base_strategy.py

@@ -19,11 +19,11 @@ class Strategy(ABC):
     """
 
     def __init__(
-        self,
-        context: "BacktestContext",
-        symbol: str,
-        enable_log: bool = True,
-        **params: Any,
+            self,
+            context: "BacktestContext",
+            symbol: str,
+            enable_log: bool = True,
+            **params: Any,
     ):
         """
         Args:
@@ -36,6 +36,9 @@ class Strategy(ABC):
         self.params = params
         self.enable_log = enable_log
         self.trading = False
+        # 缓存指标用
+        self._indicator_cache = None  # type: Optional[Tuple[np.ndarray, ...]]
+        self._cache_length = 0  # 上次缓存时数据长度
 
     def on_init(self):
         """
@@ -74,6 +77,9 @@ class Strategy(ABC):
         """
         pass
 
+    def on_start_trading(self):
+        pass
+
     # --- 新增/修改的辅助方法 ---
 
     def send_order(self, order: "Order") -> Optional[Order]:
@@ -83,7 +89,6 @@ class Strategy(ABC):
         """
         if not self.trading:
             return None
-        
         if self.context.is_rollover_bar:
             self.log(f"当前是换月K线，禁止开仓订单")
             return None
@@ -205,10 +210,22 @@ class Strategy(ABC):
         return self.context.get_price_history(key)
 
     def get_indicator_tuple(self):
-        close = np.array(self.get_price_history("close"))
-        open = np.array(self.get_price_history("open"))
+        """获取价格数据的 numpy 数组元组，带缓存功能。"""
+        close_data = self.get_price_history("close")
+        current_length = len(close_data)
+
+        # 如果长度没有变化，直接返回缓存
+        if self._indicator_cache is not None and current_length == self._cache_length:
+            return self._indicator_cache
+
+        # 数据有变化，重新创建数组并更新缓存
+        close = np.array(close_data)
+        open_price = np.array(self.get_price_history("open"))
         high = np.array(self.get_price_history("high"))
         low = np.array(self.get_price_history("low"))
         volume = np.array(self.get_price_history("volume"))
 
-        return (close, open, high, low, volume)
+        self._indicator_cache = (close, open_price, high, low, volume)
+        self._cache_length = current_length
+
+        return self._indicator_cache