ADD: 支持ETF、 LOF基金！

fund_service.py

@@ -0,0 +1,51 @@
+import akshare as ak
+import pandas as pd
+
+class FundService:
+    def search_funds(self, keyword, market_type='ETF'):
+        """
+        搜索基金代码
+        :param keyword: 搜索关键词
+        :return: 匹配的基金列表
+        """
+        try:
+            # 获取ETF和LOF数据
+            if market_type == 'ETF':
+                df = ak.fund_etf_spot_em()
+            else:
+                df = ak.fund_lof_spot_em()
+            
+            # 转换列名
+            df = df.rename(columns={
+                "代码": "symbol",
+                "名称": "name",
+                "最新价": "price",
+                "涨跌额": "price_change",
+                "涨跌幅": "price_change_percent",
+                "成交量": "volume",
+                "流通市值": "market_value",
+                "总市值": "total_value",
+                "基金折价率": "discount_rate",
+            })
+            
+            # 模糊匹配搜索（同时匹配代码和名称）
+            mask = (df['name'].str.contains(keyword, case=False, na=False) | 
+                   df['symbol'].str.contains(keyword, case=False, na=False))
+            results = df[mask]
+            
+            # 格式化返回结果并处理 NaN 值
+            formatted_results = []
+            for _, row in results.iterrows():
+                formatted_results.append({
+                    'name': row['name'] if pd.notna(row['name']) else '',
+                    'symbol': str(row['symbol']) if pd.notna(row['symbol']) else '',
+                    'price': float(row['price']) if pd.notna(row['price']) else 0.0,
+                    'volume': float(row['volume']) if pd.notna(row['volume']) else 0.0,
+                    'market_value': float(row['market_value']) if pd.notna(row['market_value']) else 0.0,
+                    'total_value': float(row['total_value']) if pd.notna(row['total_value']) else 0.0,
+                })
+                
+            return formatted_results
+            
+        except Exception as e:
+            raise Exception(f"搜索基金代码失败: {str(e)}")