diff --git a/datasource/flask_server.py b/datasource/flask_server.py
index 5e8458d..eedf9e4 100644
--- a/datasource/flask_server.py
+++ b/datasource/flask_server.py
@@ -484,11 +484,11 @@ def get_etf_nav():
             "hint": "Only A股ETF (codes starting with 51/52/15/16) supported",
         }), 400
     
-    # 获取净值
+    # 获取净值和溢价率
     f = get_fetcher()
     try:
         with f:
-            price_df, nav_df = f.fetch_etf_with_nav(code, start, end)
+            price_df, nav_df, premium_series = f.fetch_etf_with_nav(code, start, end)
         
         result = {
             "code": code,
@@ -496,14 +496,30 @@ def get_etf_nav():
             "nav": dataframe_to_json(nav_df) if nav_df else {"data": [], "count": 0},
         }
         
-        # 计算最新溢价率
-        if nav_df is not None and len(nav_df) > 0 and price_df is not None and len(price_df) > 0:
-            latest_nav = nav_df['nav'].iloc[-1]
-            latest_price = price_df['close'].iloc[-1]
-            if latest_nav > 0:
-                premium = (latest_price - latest_nav) / latest_nav
-                result['premium_rate'] = premium
-                result['premium_date'] = nav_df.index[-1].strftime('%Y-%m-%d')
+        # 添加历史溢价率序列
+        if premium_series is not None and len(premium_series) > 0:
+            # 转换为日期-溢价率列表
+            premium_data = [
+                {"date": date.strftime('%Y-%m-%d'), "premium": round(premium, 6)}
+                for date, premium in premium_series.items()
+            ]
+            result['premium_series'] = premium_data
+            
+            # 最新溢价率
+            latest_premium = premium_series.iloc[-1]
+            latest_date = premium_series.index[-1].strftime('%Y-%m-%d')
+            result['latest_premium'] = round(latest_premium, 6)
+            result['premium_date'] = latest_date
+            
+            # 溢价率统计
+            result['premium_stats'] = {
+                "mean": round(premium_series.mean(), 6),
+                "std": round(premium_series.std(), 6),
+                "min": round(premium_series.min(), 6),
+                "max": round(premium_series.max(), 6),
+                "median": round(premium_series.median(), 6),
+            }
+        
         
         return jsonify(result)
         
diff --git a/datasource/universal_fetcher.py b/datasource/universal_fetcher.py
index 6970541..0aa79d2 100644
--- a/datasource/universal_fetcher.py
+++ b/datasource/universal_fetcher.py
@@ -189,21 +189,64 @@ class UniversalDataFetcher:
         code: str,
         start_date: str,
         end_date: str
-    ) -> Tuple[Optional[pd.DataFrame], Optional[pd.DataFrame]]:
+    ) -> Tuple[Optional[pd.DataFrame], Optional[pd.DataFrame], Optional[pd.Series]]:
         """
-        获取ETF价格 + 净值
+        获取ETF价格 + 净值 + 溢价率序列
         
-        用于计算溢价率
+        计算每一天的溢价率，用于分析溢价率走势
         
         Args:
             code: ETF代码
+            start_date: 开始日期
+            end_date: 结束日期
         
         Returns:
-            (price_df, nav_df)
+            (price_df, nav_df, premium_series)
+            - price_df: ETF价格数据 (OHLCV)
+            - nav_df: ETF净值数据
+            - premium_series: 溢价率序列 (每天计算)
         """
         price_df = self._tushare.fetch_etf(code, start_date, end_date)
         nav_df = self._tushare.fetch_etf_nav(code, start_date, end_date)
-        return price_df, nav_df
+        
+        # 计算历史溢价率序列
+        premium_series = None
+        if price_df is not None and nav_df is not None and len(nav_df) > 0:
+            premium_series = self._calculate_premium_series(price_df, nav_df)
+        
+        return price_df, nav_df, premium_series
+    
+    def _calculate_premium_series(
+        self,
+        price_df: pd.DataFrame,
+        nav_df: pd.DataFrame
+    ) -> Optional[pd.Series]:
+        """
+        计算历史溢价率序列
+        
+        溢价率 = (ETF收盘价 - ETF净值) / ETF净值
+        
+        注意：净值数据通常T+1公布，需要处理日期对齐问题
+        
+        Args:
+            price_df: ETF价格数据（索引为日期）
+            nav_df: ETF净值数据（索引为日期）
+        
+        Returns:
+            溢价率Series（索引为日期，值为溢价率）
+        """
+        # 对齐日期：净值用ffill填充（因为T+1公布）
+        # 价格日期可能比净值日期多一天
+        aligned_nav = nav_df['nav'].reindex(price_df.index, method='ffill')
+        
+        # 计算溢价率
+        close_prices = price_df['close']
+        premium = (close_prices - aligned_nav) / aligned_nav
+        
+        # 过滤掉无效值（净值缺失的日期）
+        premium = premium.dropna()
+        
+        return premium
     
     def _fetch_us_index(
         self,