新建回测系统，并提交

risk/stop_loss.py

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+"""止盈止损模块。
+
+支持多种止损策略：
+- fixed_pct: 固定百分比止损/止盈
+- atr: ATR倍数止损
+- trailing: 跟踪止盈（最高价回撤）
+
+使用方法：
+    stop_loss = StopLoss(method="fixed_pct", stop_pct=0.05)
+    take_profit = StopLoss(method="fixed_pct", stop_pct=0.15)
+    
+    # 在策略的 on_bar 中检查
+    if stop_loss.should_exit(ts_code, high, low, close, cost_price):
+        # 执行卖出
+"""
+from __future__ import annotations
+
+from typing import Dict, Optional
+
+import pandas as pd
+
+from utils.logger import setup_logger
+
+logger = setup_logger(__name__)
+
+
+class StopLoss:
+    """止盈止损管理器。
+    
+    支持多种方法：
+    - fixed_pct: 固定百分比
+    - atr: ATR倍数
+    - trailing: 跟踪止盈
+    """
+    
+    def __init__(
+        self,
+        method: str = "fixed_pct",
+        stop_pct: float = 0.05,
+        atr_multiplier: float = 2.0,
+        atr_period: int = 14,
+        trailing_pct: float = 0.10,
+    ):
+        """初始化止损管理器。
+        
+        参数：
+            method: 止损方法 ("fixed_pct", "atr", "trailing")
+            stop_pct: 固定百分比止损/止盈比例（0.05 表示 5%）
+            atr_multiplier: ATR倍数
+            atr_period: ATR周期
+            trailing_pct: 跟踪止盈回撤比例
+        """
+        self.method = method
+        self.stop_pct = stop_pct
+        self.atr_multiplier = atr_multiplier
+        self.atr_period = atr_period
+        self.trailing_pct = trailing_pct
+        
+        # 跟踪止盈需要记录每只股票的最高价
+        self.highest_price: Dict[str, float] = {}
+    
+    def should_exit(
+        self,
+        ts_code: str,
+        current_price: float,
+        cost_price: float,
+        high: Optional[float] = None,
+        low: Optional[float] = None,
+        atr: Optional[float] = None,
+    ) -> tuple[bool, str]:
+        """判断是否应该止损/止盈。
+        
+        参数：
+            ts_code: 股票代码
+            current_price: 当前价格（通常是收盘价）
+            cost_price: 成本价
+            high: 当日最高价（用于跟踪止盈）
+            low: 当日最低价（用于止损）
+            atr: ATR指标值（用于ATR止损）
+            
+        返回：
+            (bool, str): (是否应该退出, 原因)
+        """
+        if self.method == "fixed_pct":
+            return self._fixed_pct_exit(ts_code, current_price, cost_price)
+        elif self.method == "atr":
+            return self._atr_exit(ts_code, current_price, cost_price, atr)
+        elif self.method == "trailing":
+            return self._trailing_exit(ts_code, current_price, cost_price, high)
+        else:
+            logger.warning(f"未知的止损方法: {self.method}")
+            return False, ""
+    
+    def _fixed_pct_exit(self, ts_code: str, current_price: float, cost_price: float) -> tuple[bool, str]:
+        """固定百分比止损/止盈。
+        
+        止损：当前价格 < 成本价 * (1 - stop_pct)
+        止盈：当前价格 > 成本价 * (1 + stop_pct)
+        """
+        profit_pct = (current_price - cost_price) / cost_price
+        
+        if profit_pct <= -self.stop_pct:
+            reason = f"固定止损 {profit_pct*100:.2f}%"
+            # logger.info(f"[STOP] {ts_code} 触发{reason}，成本={cost_price:.2f}, 当前={current_price:.2f}")  # 已移除：止损日志
+            return True, reason
+        
+        # 注意：这里的 stop_pct 实际用作止盈比例
+        # 如果需要区分止损和止盈，可以增加一个 take_profit_pct 参数
+        if profit_pct >= self.stop_pct:
+            reason = f"固定止盈 {profit_pct*100:.2f}%"
+            # logger.info(f"[STOP] {ts_code} 触发{reason}，成本={cost_price:.2f}, 当前={current_price:.2f}")  # 已移除：止盈日志
+            return True, reason
+        
+        return False, ""
+    
+    def _atr_exit(self, ts_code: str, current_price: float, cost_price: float, atr: Optional[float]) -> tuple[bool, str]:
+        """ATR倍数止损。
+        
+        止损：当前价格 < 成本价 - ATR * multiplier
+        """
+        if atr is None or atr <= 0:
+            logger.warning(f"{ts_code} ATR值无效，跳过ATR止损")
+            return False, ""
+        
+        stop_price = cost_price - atr * self.atr_multiplier
+        
+        if current_price < stop_price:
+            profit_pct = (current_price - cost_price) / cost_price
+            reason = f"ATR止损 {profit_pct*100:.2f}% (ATR={atr:.2f})"
+            # logger.info(f"[STOP] {ts_code} 触发{reason}，成本={cost_price:.2f}, 止损价={stop_price:.2f}, 当前={current_price:.2f}")  # 已移除
+            return True, reason
+        
+        return False, ""
+    
+    def _trailing_exit(self, ts_code: str, current_price: float, cost_price: float, high: Optional[float]) -> tuple[bool, str]:
+        """跟踪止盈。
+        
+        记录持仓期间的最高价，当价格从最高价回撤超过 trailing_pct 时卖出。
+        """
+        # 使用当日最高价或当前价更新历史最高价
+        if high is not None:
+            price_to_track = max(current_price, high)
+        else:
+            price_to_track = current_price
+        
+        if ts_code not in self.highest_price:
+            self.highest_price[ts_code] = price_to_track
+        else:
+            self.highest_price[ts_code] = max(self.highest_price[ts_code], price_to_track)
+        
+        highest = self.highest_price[ts_code]
+        drawdown_from_high = (highest - current_price) / highest
+        
+        # 只有在盈利的情况下才触发跟踪止盈
+        if current_price > cost_price and drawdown_from_high >= self.trailing_pct:
+            profit_pct = (current_price - cost_price) / cost_price
+            reason = f"跟踪止盈 从最高点{highest:.2f}回撤{drawdown_from_high*100:.2f}%，盈利{profit_pct*100:.2f}%"
+            # logger.info(f"[STOP] {ts_code} 触发{reason}，成本={cost_price:.2f}, 当前={current_price:.2f}")  # 已移除
+            
+            # 清除最高价记录
+            del self.highest_price[ts_code]
+            return True, reason
+        
+        return False, ""
+    
+    def reset_tracking(self, ts_code: str) -> None:
+        """重置某只股票的跟踪状态（例如清仓后）。"""
+        if ts_code in self.highest_price:
+            del self.highest_price[ts_code]
+    
+    def clear_all(self) -> None:
+        """清空所有跟踪状态。"""
+        self.highest_price.clear()
+
+
+def calculate_atr(df: pd.DataFrame, period: int = 14) -> pd.Series:
+    """计算ATR指标（Average True Range）。
+    
+    参数：
+        df: 包含 ['high', 'low', 'close'] 的DataFrame
+        period: ATR周期
+        
+    返回：
+        pd.Series: ATR值
+    """
+    high = df["high"]
+    low = df["low"]
+    close = df["close"]
+    
+    # True Range = max(high-low, abs(high-prev_close), abs(low-prev_close))
+    tr1 = high - low
+    tr2 = (high - close.shift(1)).abs()
+    tr3 = (low - close.shift(1)).abs()
+    
+    tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+    
+    # ATR = MA(TR, period)
+    atr = tr.rolling(window=period).mean()
+    
+    return atr