ATR回调止损

freqtrade/templates/OKXRegressionStrategy.py

@@ -583,21 +583,41 @@ class OKXRegressionStrategy(IStrategy):
         except Exception as e:
             logger.error(f"FreqAI fit 失败：{str(e)}")
             raise
-    def _callback_stop_loss(self, dataframe: DataFrame, metadata: dict, callback_percent: float = 0.015) -> DataFrame:
+    def _callback_stop_loss(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
-        回调止损逻辑：当价格从近期高点回撤超过指定百分比，
-        并结合 RSI 或布林带信号，同时考虑高时间框架趋势。
+        动态回调止损逻辑：基于ATR调整回撤阈值，并结合RSI和布林带过滤信号。
         """
+        pair = metadata.get('pair', 'unknown')
 
-        # 计算滚动最高价（过去 N 根K线内的最高点）
+        # 设置默认参数
+        atr_col = 'ATR_14'
         rolling_high_period = 20
+        rsi_overbought = 70
+
+        # 设置不同币种的回调乘数
+        callback_multipliers = {
+            "BTC/USDT": 1.5,
+            "ETH/USDT": 1.8,
+            "OKB/USDT": 2.0,
+            "TON/USDT": 2.2,
+        }
+        callback_multiplier = callback_multipliers.get(pair, 2.0)
+
+        # 确保ATR列存在
+        if atr_col not in dataframe.columns:
+            dataframe[atr_col] = ta.ATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=14)
+
+        # 计算动态回调百分比（基于ATR）
+        dataframe['callback_threshold'] = dataframe[atr_col] * callback_multiplier
+
+        # 计算滚动最高价
         dataframe['rolling_high'] = dataframe['close'].rolling(window=rolling_high_period).max()
 
-        # 计算当前价格相对于最近高点的回撤比例
+        # 计算当前价格相对于最近高点的回撤比例（使用ATR标准化）
         dataframe['callback_ratio'] = (dataframe['close'] - dataframe['rolling_high']) / dataframe['rolling_high']
+        dataframe['callback_condition_atr'] = (dataframe['close'] - dataframe['rolling_high']) <= -dataframe['callback_threshold']
 
-        # 获取 RSI 和布林带信息
-        rsi_overbought = 70
+        # 获取RSI和布林带信息
         dataframe['in_overbought'] = dataframe['rsi'] > rsi_overbought
         dataframe['below_bb_upper'] = dataframe['close'] < dataframe['bb_upper']
 
@@ -605,14 +625,28 @@ class OKXRegressionStrategy(IStrategy):
         dataframe['trend_up'] = dataframe['close'] > dataframe['trend_1h']
         dataframe['trend_down'] = dataframe['close'] < dataframe['trend_1h']
 
-        # 回调止损条件：
-        # 1. 当前价格回撤超过设定的百分比
-        # 2. RSI 处于超买状态 OR 价格跌破布林带上轨
-        # 3. 当前处于下降趋势（高时间框架确认）
+        # 综合回调止损条件
         callback_condition = (
-            (dataframe['callback_ratio'] <= -callback_percent) &
+            dataframe['callback_condition_atr'] &
             ((dataframe['in_overbought'] | (~dataframe['below_bb_upper']))) &
-            (dataframe['trend_down'])
+            dataframe['trend_down']
+        )
+
+        # 应用回调止损逻辑
+        dataframe.loc[callback_condition, 'exit_long'] = 1
+
+        return dataframe
+        dataframe['below_bb_upper'] = dataframe['close'] < dataframe['bb_upper']
+
+        # 获取高时间框架趋势（1小时均线）
+        dataframe['trend_up'] = dataframe['close'] > dataframe['trend_1h']
+        dataframe['trend_down'] = dataframe['close'] < dataframe['trend_1h']
+
+        # 综合回调止损条件
+        callback_condition = (
+            dataframe['callback_condition_atr'] &
+            ((dataframe['in_overbought'] | (~dataframe['below_bb_upper']))) &
+            dataframe['trend_down']
         )
 
         # 应用回调止损逻辑