diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py
index 95786fca..fce11d3e 100644
--- a/freqtrade/templates/freqaiprimer.py
+++ b/freqtrade/templates/freqaiprimer.py
@@ -96,6 +96,15 @@ class FreqaiPrimer(IStrategy):
         pairs = self.dp.current_whitelist()
         return [(pair, '15m') for pair in pairs] + [(pair, '1h') for pair in pairs]
 
+    def _validate_dataframe_columns(self, dataframe: DataFrame, required_columns: list, metadata: dict):
+        """
+        验证数据框中是否包含所有需要的列。
+        如果缺少列，则记录警告日志。
+        """
+        missing_columns = [col for col in required_columns if col not in dataframe.columns]
+        if missing_columns:
+            logger.warning(f"[{metadata['pair']}] 数据框中缺少以下列: {missing_columns}")
+    
     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)
@@ -185,6 +194,11 @@ class FreqaiPrimer(IStrategy):
         df_1h = df_1h.rename(columns={'index': 'date'})
         df_1h = df_1h[['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h', 'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h']].fillna(method='ffill')
         
+        # 确保所有需要的列都被合并
+        df_1h = df_1h[['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h', 
+                       'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h',
+                       'macd_1h', 'macd_signal_1h']]  # 确保包含 macd_1h 和 macd_signal_1h
+        
         # 合并 1h 数据
         dataframe = dataframe.merge(df_1h, how='left', on='date').fillna(method='ffill')
 
@@ -241,6 +255,9 @@ class FreqaiPrimer(IStrategy):
                          'trend_3m', 'trend_15m', 'trend_1h_ema', 'market_score', 'market_state',
                          'bullish_engulfing', 'volume', 'volume_ma']].tail(5))
 
+        # 打印最终数据框的列名以验证
+        logger.info(f"[{metadata['pair']}] 最终数据框列名: {list(dataframe.columns)}")
+        
         return dataframe
 
     def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: