refactor: 重构代码结构

services/us_stock_service_async.py

@@ -0,0 +1,151 @@
+import asyncio
+import pandas as pd
+from typing import List, Dict, Any, Optional
+from logger import get_logger
+
+# 获取日志器
+logger = get_logger()
+
+class USStockServiceAsync:
+    """
+    异步美股服务
+    提供美股数据的异步搜索和获取功能
+    """
+    
+    def __init__(self):
+        """初始化异步美股服务"""
+        logger.debug("初始化USStockServiceAsync")
+        
+        # 可选：添加缓存以减少频繁请求
+        self._cache = None
+        self._cache_timestamp = None
+    
+    async def search_us_stocks(self, keyword: str) -> List[Dict[str, Any]]:
+        """
+        异步搜索美股代码
+        
+        Args:
+            keyword: 搜索关键词
+            
+        Returns:
+            匹配的股票列表
+        """
+        try:
+            logger.info(f"异步搜索美股: {keyword}")
+            
+            # 使用线程池执行同步的akshare调用
+            df = await asyncio.to_thread(self._get_us_stocks_data)
+            
+            # 模糊匹配搜索
+            mask = df['name'].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,
+                    'market_value': float(row['market_value']) if pd.notna(row['market_value']) else 0.0
+                })
+            
+            logger.info(f"美股搜索完成，找到 {len(formatted_results)} 个匹配项")
+            return formatted_results
+            
+        except Exception as e:
+            error_msg = f"搜索美股代码失败: {str(e)}"
+            logger.error(error_msg)
+            logger.exception(e)
+            raise Exception(error_msg)
+    
+    def _get_us_stocks_data(self) -> pd.DataFrame:
+        """
+        获取美股数据（同步方法，将被异步方法调用）
+        
+        Returns:
+            包含美股数据的DataFrame
+        """
+        import akshare as ak
+        
+        try:
+            # 获取美股数据
+            df = ak.stock_us_spot_em()
+            
+            # 转换列名
+            df = df.rename(columns={
+                "序号": "index",
+                "名称": "name",
+                "最新价": "price",
+                "涨跌额": "price_change",
+                "涨跌幅": "price_change_percent",
+                "开盘价": "open",
+                "最高价": "high",
+                "最低价": "low",
+                "昨收价": "pre_close",
+                "总市值": "market_value",
+                "市盈率": "pe_ratio",
+                "成交量": "volume",
+                "成交额": "turnover",
+                "振幅": "amplitude",
+                "换手率": "turnover_rate",
+                "代码": "symbol"
+            })
+            
+            return df
+            
+        except Exception as e:
+            logger.error(f"获取美股数据失败: {str(e)}")
+            logger.exception(e)
+            raise Exception(f"获取美股数据失败: {str(e)}")
+            
+    async def get_us_stock_detail(self, symbol: str) -> Dict[str, Any]:
+        """
+        异步获取单个美股详细信息
+        
+        Args:
+            symbol: 股票代码
+            
+        Returns:
+            股票详细信息
+        """
+        try:
+            logger.info(f"获取美股详情: {symbol}")
+            
+            # 使用线程池执行同步的akshare调用
+            df = await asyncio.to_thread(self._get_us_stocks_data)
+            
+            # 精确匹配股票代码
+            result = df[df['symbol'] == symbol]
+            
+            if len(result) == 0:
+                raise Exception(f"未找到股票代码: {symbol}")
+            
+            # 获取第一行数据
+            row = result.iloc[0]
+            
+            # 格式化为字典
+            stock_detail = {
+                '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,
+                'price_change': float(row['price_change']) if pd.notna(row['price_change']) else 0.0,
+                'price_change_percent': float(row['price_change_percent'].strip('%'))/100 if pd.notna(row['price_change_percent']) else 0.0,
+                'open': float(row['open']) if pd.notna(row['open']) else 0.0,
+                'high': float(row['high']) if pd.notna(row['high']) else 0.0,
+                'low': float(row['low']) if pd.notna(row['low']) else 0.0,
+                'pre_close': float(row['pre_close']) if pd.notna(row['pre_close']) else 0.0,
+                'market_value': float(row['market_value']) if pd.notna(row['market_value']) else 0.0,
+                'pe_ratio': float(row['pe_ratio']) if pd.notna(row['pe_ratio']) else 0.0,
+                'volume': float(row['volume']) if pd.notna(row['volume']) else 0.0,
+                'turnover': float(row['turnover']) if pd.notna(row['turnover']) else 0.0
+            }
+            
+            logger.info(f"获取美股详情成功: {symbol}")
+            return stock_detail
+            
+        except Exception as e:
+            error_msg = f"获取美股详情失败: {str(e)}"
+            logger.error(error_msg)
+            logger.exception(e)
+            raise Exception(error_msg)