feat(flask): OHLCV 端点自动附加 ETF 净值和溢价率

flask_server.py:
- 当 asset_type 为 china_etf 时，自动调用 fetch_etf_with_nav
- 响应中添加 nav、premium_series、latest_premium、premium_stats

flask_api_source.py:
- 解析 ETF 数据中的净值和溢价率信息
- 将 nav_df、premium_series、premium_stats 存入 DataFrame.attrs

datasource/flask_api_source.py

@@ -142,6 +142,30 @@ class FlaskAPIDataSource:
                 if 'info' in data:
                     df.attrs['info'] = data['info']
                 
+                # ETF 数据自动附加净值和溢价率信息
+                if data.get('asset_type') == 'china_etf':
+                    # 净值数据
+                    nav_section = data.get('nav', {})
+                    if nav_section.get('data'):
+                        nav_df = pd.DataFrame(nav_section['data'])
+                        if 'date' in nav_df.columns:
+                            nav_df['date'] = pd.to_datetime(nav_df['date'])
+                            nav_df = nav_df.set_index('date')
+                        df.attrs['nav'] = nav_df
+                    
+                    # 溢价率序列
+                    if 'premium_series' in data:
+                        df.attrs['premium_series'] = data['premium_series']
+                    
+                    # 最新溢价率
+                    if 'latest_premium' in data:
+                        df.attrs['latest_premium'] = data['latest_premium']
+                        df.attrs['premium_date'] = data.get('premium_date')
+                    
+                    # 溢价率统计
+                    if 'premium_stats' in data:
+                        df.attrs['premium_stats'] = data['premium_stats']
+                
                 print(f"✓ {code}: {len(df)} 条数据 ({start_date} ~ {end_date})")
                 return df
                 

datasource/flask_server.py

@@ -630,6 +630,44 @@ def get_ohlcv():
     
     result['cached'] = is_cached
     result['asset_type'] = final_type.value  # 使用最终类型
+    
+    # 如果是中国 ETF，自动附加净值和溢价率数据
+    if final_type == AssetType.CHINA_ETF:
+        try:
+            f = get_fetcher()
+            with f:
+                price_df, nav_df, premium_series = f.fetch_etf_with_nav(code, start, end)
+            
+            # 添加净值数据
+            if nav_df is not None and len(nav_df) > 0:
+                result['nav'] = dataframe_to_json(nav_df)
+            
+            # 添加溢价率序列
+            if premium_series is not None and len(premium_series) > 0:
+                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),
+                }
+        except Exception as e:
+            # 净值获取失败不影响主数据返回
+            result['nav_error'] = str(e)
+    
     # 如果用户指定了类型但与自动检测不同，显示提示
     if asset_type_param and detected_type != final_type:
         result['type_override'] = {