diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py
index 9a58ba10..0d40868f 100644
--- a/freqtrade/templates/freqaiprimer.py
+++ b/freqtrade/templates/freqaiprimer.py
@@ -84,6 +84,39 @@ class FreqaiPrimer(IStrategy):
         else:
             logger.info(message)
     
+    def calculate_data_freshness(self, data_timestamp: datetime, pair: str, dataframe: DataFrame) -> float:
+        """
+        计算数据新鲜度（与交易所最新数据比较）
+        :param data_timestamp: 数据时间戳
+        :param pair: 交易对
+        :param dataframe: 数据框
+        :return: 数据延迟时间（分钟）
+        """
+        try:
+            # 转换为UTC+8时间
+            if data_timestamp.tzinfo is None:
+                data_timestamp = data_timestamp.replace(tzinfo=timezone.utc)
+            data_timestamp_utc8 = data_timestamp.astimezone(UTC_PLUS_8).replace(tzinfo=None)
+            
+            # 获取交易所的最新数据时间
+            exchange_latest_candle, exchange_latest_date = self.get_latest_candle(pair, self.timeframe, dataframe)
+            
+            if exchange_latest_date:
+                # 转换为UTC+8时间进行比较
+                if exchange_latest_date.tzinfo is None:
+                    exchange_latest_date = exchange_latest_date.replace(tzinfo=timezone.utc)
+                exchange_latest_utc8 = exchange_latest_date.astimezone(UTC_PLUS_8).replace(tzinfo=None)
+                freshness_minutes = (exchange_latest_utc8 - data_timestamp_utc8).total_seconds() / 60
+            else:
+                # 如果无法获取交易所最新时间，使用保守估计（3分钟延迟）
+                freshness_minutes = 3.0
+            
+            return max(0.0, freshness_minutes)  # 确保不会返回负数
+            
+        except Exception as e:
+            self.strategy_log(f"[{pair}] 数据新鲜度计算异常: {e}", "warning")
+            return 3.0  # 异常时返回保守估计
+    
     def update_freshness_stats(self, pair: str, data_age_minutes: float) -> None:
         """
         更新数据新鲜度统计
@@ -519,15 +552,9 @@ class FreqaiPrimer(IStrategy):
                                 latest_date = latest_date.to_pydatetime()
                         except:
                             pass                     
-                    # 计算ML数据新鲜度
+                    # 计算ML数据新鲜度（使用统一函数）
                     if isinstance(latest_date, datetime):
-                        # 转换为UTC+8时间
-                        if latest_date.tzinfo is None:
-                            latest_date = latest_date.replace(tzinfo=timezone.utc)
-                        latest_date_utc8 = latest_date.astimezone(UTC_PLUS_8).replace(tzinfo=None)
-                        
-                        current_time_utc8 = datetime.now().replace(tzinfo=UTC_PLUS_8).replace(tzinfo=None)
-                        ml_age_minutes = (current_time_utc8 - latest_date_utc8).total_seconds() / 60
+                        ml_age_minutes = self.calculate_data_freshness(latest_date, metadata['pair'], dataframe)
                         
                         # 检查目标变量的类型和新鲜度
                         target_vars = []
@@ -977,23 +1004,8 @@ class FreqaiPrimer(IStrategy):
                         display_latest = display_latest.replace(tzinfo=timezone.utc)
                     display_latest = display_latest.astimezone(UTC_PLUS_8)
                     latest_time_str = display_latest.strftime('%H:%M:%S')
-                    # 计算数据新鲜度（与交易所最新数据比较）
-                    latest_naive = display_latest.replace(tzinfo=None)
-                    
-                    # 获取交易所的最新数据时间
-                    exchange_latest_candle, exchange_latest_date = self.get_latest_candle(
-                        metadata['pair'], self.timeframe, dataframe
-                    )
-                    
-                    if exchange_latest_date:
-                        # 转换为UTC+8时间进行比较
-                        if exchange_latest_date.tzinfo is None:
-                            exchange_latest_date = exchange_latest_date.replace(tzinfo=timezone.utc)
-                        exchange_latest_utc8 = exchange_latest_date.astimezone(UTC_PLUS_8).replace(tzinfo=None)
-                        data_freshness = (exchange_latest_utc8 - latest_naive).total_seconds() / 60
-                    else:
-                        # 如果无法获取交易所最新时间，使用保守估计（3分钟延迟）
-                        data_freshness = 3.0
+                    # 计算数据新鲜度（使用统一函数）
+                    data_freshness = self.calculate_data_freshness(latest_data_date, metadata['pair'], dataframe)
                     
                     # 数据新鲜度判断
                     if data_freshness <= 3:  # 3分钟内为新鲜数据
@@ -1016,20 +1028,8 @@ class FreqaiPrimer(IStrategy):
                         latest_time_str = display_latest.strftime('%H:%M:%S')
                         latest_naive = display_latest.replace(tzinfo=None)
                         
-                        # 获取交易所的最新数据时间
-                        exchange_latest_candle, exchange_latest_date = self.get_latest_candle(
-                            metadata['pair'], self.timeframe, dataframe
-                        )
-                        
-                        if exchange_latest_date:
-                            # 转换为UTC+8时间进行比较
-                            if exchange_latest_date.tzinfo is None:
-                                exchange_latest_date = exchange_latest_date.replace(tzinfo=timezone.utc)
-                            exchange_latest_utc8 = exchange_latest_date.astimezone(UTC_PLUS_8).replace(tzinfo=None)
-                            data_freshness = (exchange_latest_utc8 - latest_naive).total_seconds() / 60
-                        else:
-                            # 如果无法获取交易所最新时间，使用保守估计（3分钟延迟）
-                            data_freshness = 3.0
+                        # 计算数据新鲜度（使用统一函数）
+                        data_freshness = self.calculate_data_freshness(latest_data_date, metadata['pair'], dataframe)
                         
                         # 数据新鲜度判断
                         if data_freshness <= 3:  # 3分钟内为新鲜数据
@@ -1311,17 +1311,11 @@ class FreqaiPrimer(IStrategy):
                                     except:
                                         pass
                                             
-                                # 计算数据延迟（分钟）
+                                # 计算数据延迟（分钟）- 使用统一函数
                                 if isinstance(data_timestamp, datetime):
-                                    current_time = datetime.now()
-                                    
-                                    # FreqAI dataframe 的 date 列是 UTC 时间
-                                    if data_timestamp.tzinfo is None:
-                                        data_timestamp = data_timestamp.replace(tzinfo=timezone.utc)
-                                    # 转换为 UTC+8 并移除时区信息
-                                    data_timestamp = data_timestamp.astimezone(UTC_PLUS_8).replace(tzinfo=None)
-                                                
-                                    data_age_minutes = (current_time - data_timestamp).total_seconds() / 60
+                                    # 获取当前数据框用于计算新鲜度
+                                    current_df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+                                    data_age_minutes = self.calculate_data_freshness(data_timestamp, pair, current_df)
                                     
                                     # 📊 更新统计数据
                                     self.update_freshness_stats(pair, data_age_minutes)