diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py
index d689e896..9a58ba10 100644
--- a/freqtrade/templates/freqaiprimer.py
+++ b/freqtrade/templates/freqaiprimer.py
@@ -977,17 +977,30 @@ 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（修正时区差异）
+                    # 计算数据新鲜度（与交易所最新数据比较）
                     latest_naive = display_latest.replace(tzinfo=None)
-                    current_time_utc8 = datetime.now().replace(tzinfo=UTC_PLUS_8).replace(tzinfo=None)
-                    data_freshness = (current_time_utc8 - latest_naive).total_seconds() / 60
                     
-                    # 修正时区差异后的判断
-                    if data_freshness <= 10:
-                        fresh_icon = "✅"
-                    elif data_freshness <= 30:
-                        fresh_icon = "⚠️"
+                    # 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
+                    
+                    # 数据新鲜度判断
+                    if data_freshness <= 3:  # 3分钟内为新鲜数据
+                        fresh_icon = "✅"
+                    elif data_freshness <= 10:  # 3-10分钟为可接受延迟
+                        fresh_icon = "⚠️"
+                    else:  # 超过10分钟为过期数据
                         fresh_icon = "❌"
                     self.strategy_log(
                         f"[{metadata['pair']}] 数据新鲜度: {fresh_icon} {data_freshness:.1f}min | "
@@ -1002,15 +1015,28 @@ class FreqaiPrimer(IStrategy):
                         display_latest = display_latest.astimezone(UTC_PLUS_8)
                         latest_time_str = display_latest.strftime('%H:%M:%S')
                         latest_naive = display_latest.replace(tzinfo=None)
-                        current_time_utc8 = datetime.now().replace(tzinfo=UTC_PLUS_8).replace(tzinfo=None)
-                        data_freshness = (current_time_utc8 - latest_naive).total_seconds() / 60
                         
-                        # 修正时区差异后的判断
-                        if data_freshness <= 10:
-                            fresh_icon = "✅"
-                        elif data_freshness <= 30:
-                            fresh_icon = "⚠️"
+                        # 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
+                        
+                        # 数据新鲜度判断
+                        if data_freshness <= 3:  # 3分钟内为新鲜数据
+                            fresh_icon = "✅"
+                        elif data_freshness <= 10:  # 3-10分钟为可接受延迟
+                            fresh_icon = "⚠️"
+                        else:  # 超过10分钟为过期数据
                             fresh_icon = "❌"
                         self.strategy_log(
                             f"[{metadata['pair']}] 数据新鲜度: {fresh_icon} {data_freshness:.1f}min | "