diff --git a/Dockerfile b/Dockerfile
index 23044d8..b69b913 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -21,7 +21,7 @@ ENV TZ=Asia/Shanghai
 EXPOSE 80
 
 # 启动Flask数据API服务（默认端口80）
-# CMD ["python", "datasource/flask_server.py", "--host", "0.0.0.0"]
+CMD ["python", "datasource/flask_server.py", "--host", "0.0.0.0"]
 
 # 运行定时任务调度器（如需使用Flask服务，取消上面注释并注释掉下面）
-CMD ["python", "scripts/daily_scheduler.py", "--time", "09:00"]
\ No newline at end of file
+# CMD ["python", "scripts/daily_scheduler.py", "--time", "09:00"]
\ No newline at end of file
diff --git a/datasource/flask_server.py b/datasource/flask_server.py
index 5b6a25e..85a1f27 100644
--- a/datasource/flask_server.py
+++ b/datasource/flask_server.py
@@ -180,8 +180,21 @@ def _slice_data_from_cache(cached_data: Dict, start: str, end: str) -> Dict:
     
     # 从缓存数据中重建 DataFrame
     records = cached_data['df_json']['data']
+    info_data = cached_data['df_json'].get('info', None)  # 从缓存获取 info
+    
     if not records:
-        return cached_data
+        result = {
+            'data': [],
+            'count': 0,
+            'code': cached_data['code'],
+            'asset_type': cached_data['asset_type'],
+            'requested_range': {'start': start, 'end': end},
+            'available_range': {'start': cached_data['data_start'], 'end': cached_data['data_end']},
+        }
+        # 保留 info（如果有）
+        if info_data:
+            result['info'] = info_data
+        return result
     
     # 转换为 DataFrame
     df = pd.DataFrame(records)
@@ -189,6 +202,10 @@ def _slice_data_from_cache(cached_data: Dict, start: str, end: str) -> Dict:
         df['date'] = pd.to_datetime(df['date'])
         df = df.set_index('date')
     
+    # 恢复 attrs（如果有 info）
+    if info_data:
+        df.attrs['info'] = info_data
+    
     # 切片日期范围
     start_dt = pd.to_datetime(start)
     end_dt = pd.to_datetime(end)
@@ -199,17 +216,7 @@ def _slice_data_from_cache(cached_data: Dict, start: str, end: str) -> Dict:
     # 切片（使用 loc 进行日期范围选择）
     sliced_df = df.loc[start_dt:end_dt]
     
-    if len(sliced_df) == 0:
-        return {
-            'data': [],
-            'count': 0,
-            'code': cached_data['code'],
-            'asset_type': cached_data['asset_type'],
-            'requested_range': {'start': start, 'end': end},
-            'available_range': {'start': cached_data['data_start'], 'end': cached_data['data_end']},
-        }
-    
-    # 转换为 JSON 格式
+    # 转换为 JSON 格式（dataframe_to_json 会处理 df.attrs['info']）
     result = dataframe_to_json(sliced_df)
     result['code'] = cached_data['code']
     result['asset_type'] = cached_data['asset_type']
@@ -337,10 +344,37 @@ def get_cache_info() -> Dict:
 # DataFrame 转换
 # ============================================================
 
+class JSONEncoder(json.JSONEncoder):
+    """自定义 JSON 编码器，处理特殊类型"""
+    def default(self, obj):
+        # 处理 pandas Timestamp
+        if hasattr(obj, 'isoformat'):
+            return obj.isoformat()
+        # 处理 numpy 类型
+        if hasattr(obj, 'item'):
+            return obj.item()
+        # 处理 NaN/Infinity
+        if isinstance(obj, float):
+            if obj != obj:  # NaN
+                return None
+            if obj == float('inf'):
+                return None
+            if obj == float('-inf'):
+                return None
+        return super().default(obj)
+
+
 def dataframe_to_json(df: pd.DataFrame) -> Dict:
-    """将 DataFrame 转换为 JSON 可序列化的字典"""
+    """将 DataFrame 转换为 JSON 可序列化的字典
+    
+    如果 df.attrs 中有 info 字段，会放到最外层返回
+    """
     if df is None or len(df) == 0:
-        return {"data": [], "count": 0}
+        result = {"data": [], "count": 0}
+        # 即使空数据也返回 info（如果有）
+        if hasattr(df, 'attrs') and 'info' in df.attrs:
+            result['info'] = df.attrs['info']
+        return result
     
     # 重置索引
     df_reset = df.reset_index()
@@ -357,18 +391,32 @@ def dataframe_to_json(df: pd.DataFrame) -> Dict:
             except Exception:
                 pass
     
-    # 转换为字典列表
-    records = df_reset.to_dict(orient='records')
+    # 处理特殊值（NaN, Infinity）
+    df_clean = df_reset.copy()
+    for col in df_clean.columns:
+        if df_clean[col].dtype in ['float64', 'float32']:
+            df_clean[col] = df_clean[col].replace([float('inf'), float('-inf')], None)
+            df_clean[col] = df_clean[col].where(df_clean[col].notna(), None)
     
-    return {
+    # 转换为字典列表
+    records = df_clean.to_dict(orient='records')
+    
+    # 构建返回结果
+    result = {
         "data": records,
         "count": len(records),
-        "columns": list(df_reset.columns),
+        "columns": list(df_clean.columns),
         "date_range": {
             "start": df.index.min().strftime('%Y-%m-%d') if len(df) > 0 else None,
             "end": df.index.max().strftime('%Y-%m-%d') if len(df) > 0 else None,
         }
     }
+    
+    # 将 info 从 df.attrs 放到最外层
+    if hasattr(df, 'attrs') and 'info' in df.attrs:
+        result['info'] = df.attrs['info']
+    
+    return result
 
 
 def validate_date(date_str: str) -> bool:
diff --git a/datasource/yfinance_source.py b/datasource/yfinance_source.py
index f0fa0f3..76d53b3 100644
--- a/datasource/yfinance_source.py
+++ b/datasource/yfinance_source.py
@@ -55,6 +55,7 @@ class YFinanceSource:
         
         Returns:
             DataFrame with columns: date, open, high, low, close, volume
+            股票元信息存储在 df.attrs['info'] 中
         """
         import yfinance as yf
         
@@ -67,6 +68,13 @@ class YFinanceSource:
         try:
             ticker = yf.Ticker(yf_code)
             
+            # 获取股票信息（仅对股票/ETF有效，指数可能没有）
+            stock_info = {}
+            try:
+                stock_info = ticker.info or {}
+            except Exception:
+                pass  # 指数可能没有info
+            
             # end_date 需要加一天（yfinance的end是排他的）
             end_dt = datetime.strptime(end_date, "%Y-%m-%d") + timedelta(days=1)
             
@@ -96,6 +104,10 @@ class YFinanceSource:
             # 添加代码列
             df["code"] = code
             
+            # 将股票信息存储到 DataFrame.attrs 中（最外层结构）
+            df.attrs['info'] = stock_info
+            df.attrs['code'] = code
+            
             return df[['code', 'open', 'high', 'low', 'close', 'volume']]
             
         except Exception as e: