diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py
index e73bcc10..fd81f7a5 100644
--- a/freqtrade/templates/freqaiprimer.py
+++ b/freqtrade/templates/freqaiprimer.py
@@ -3,13 +3,13 @@ from pandas import DataFrame
 import pandas_ta as ta
 from freqtrade.exchange import timeframe_to_minutes
 
-class FreqaiPrimer(IStrategy):
+class ShortTermMultiTimeframeStrategy(IStrategy):
     # 策略参数
     minimal_roi = {
-        "0": 0.04,    # 4% ROI（10 分钟内）
-        "60": 0.02,   # 2% ROI（1 小时）
-        "180": 0.01,  # 1% ROI（3 小时）
-        "360": 0.0    # 0% ROI（6 小时）
+        "0": 0.04,    # 4% ROI (10 分钟内)
+        "60": 0.02,   # 2% ROI (1 小时)
+        "180": 0.01,  # 1% ROI (3 小时)
+        "360": 0.0    # 0% ROI (6 小时)
     }
     
     stoploss = -0.015  # 初始止损 -1.5%
@@ -27,6 +27,12 @@ class FreqaiPrimer(IStrategy):
     rsi_overbought = 70  # RSI 超买阈值
     rsi_oversold = 30    # RSI 超卖阈值
 
+    def informative_pairs(self):
+        # 定义辅助时间框架
+        pairs = self.dp.current_whitelist()
+        informative_pairs = [(pair, '15m') for pair in pairs] + [(pair, '1h') for pair in pairs]
+        return informative_pairs
+
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         # 计算 3m 周期的指标
         bb_3m = ta.bbands(dataframe['close'], length=self.bb_length, std=self.bb_std)
@@ -46,7 +52,12 @@ class FreqaiPrimer(IStrategy):
         df_15m['bb_lower_15m'] = bb_15m[f'BBL_{self.bb_length}_{self.bb_std}']
         df_15m['bb_upper_15m'] = bb_15m[f'BBU_{self.bb_length}_{self.bb_std}']
         df_15m['rsi_15m'] = ta.rsi(df_15m['close'], length=self.rsi_length)
-        dataframe = self.merge_informative_pair(dataframe, df_15m, self.timeframe, '15m')
+        
+        # 手动合并 15m 数据
+        df_15m = df_15m[['date', 'bb_lower_15m', 'bb_upper_15m', 'rsi_15m']]
+        df_15m = df_15m.rename(columns={'date': 'date_15m'})
+        dataframe = dataframe.merge(df_15m, how='left', left_on='date', right_on='date_15m')
+        dataframe = dataframe.fillna(method='ffill')  # 前向填充缺失值
 
         # 获取 1h 数据
         df_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h')
@@ -54,12 +65,55 @@ class FreqaiPrimer(IStrategy):
         df_1h['bb_lower_1h'] = bb_1h[f'BBL_{self.bb_length}_{self.bb_std}']
         df_1h['bb_upper_1h'] = bb_1h[f'BBU_{self.bb_length}_{self.bb_std}']
         df_1h['rsi_1h'] = ta.rsi(df_1h['close'], length=self.rsi_length)
-        dataframe = self.merge_informative_pair(dataframe, df_1h, self.timeframe, '1h')
+        
+        # 手动合并 1h 数据
+        df_1h = df_1h[['date', 'bb_lower_1h', 'bb_upper_1h', 'rsi_1h']]
+        df_1h = df_1h.rename(columns={'date': 'date_1h'})
+        dataframe = dataframe.merge(df_1h, how='left', left_on='date', right_on='date_1h')
+        dataframe = dataframe.fillna(method='ffill')  # 前向填充缺失值
 
         # K线形态：看涨吞没
         dataframe['bullish_engulfing'] = (
-            (dataframe['close'].shift(1) < dataframe['open'].shift(1)) &  # 前一根是阴线
-            (dataframe['close'] > dataframe['open']) &  # 当前是阳线
-            (dataframe['close'] > dataframe['open'].shift(1)) &  # 吞没前一根
+            (dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
+            (dataframe['close'] > dataframe['open']) &
+            (dataframe['close'] > dataframe['open'].shift(1)) &
+            (dataframe['open'] < dataframe['close'].shift(1))
+        )
 
+        return dataframe
 
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # 做多条件
+        dataframe.loc[
+            (
+                (dataframe['close'] <= dataframe['bb_lower_3m']) &
+                (dataframe['rsi_3m'] < self.rsi_oversold) &
+                (dataframe['rsi_15m'] < self.rsi_oversold) &
+                (dataframe['close'] <= dataframe['bb_lower_1h']) &
+                (dataframe['bullish_engulfing']) &
+                (dataframe['volume'] > dataframe['volume_ma'])
+            ),
+            'enter_long'] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # 做多退出
+        dataframe.loc[
+            (
+                (dataframe['close'] >= dataframe['bb_upper_3m']) |
+                (dataframe['rsi_3m'] > self.rsi_overbought)
+            ),
+            'exit_long'] = 1
+
+        return dataframe
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time, current_rate: float,
+                        current_profit: float, **kwargs) -> float:
+        # 动态止损基于 ATR
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = dataframe.iloc[-1]
+        atr = last_candle['atr']
+        if atr > 0:
+            return -1.5 * atr / current_rate  # 动态止损为 1.5 倍 ATR
+        return self.stoploss  # 回退到固定止损