refactor: 重构代码结构

2025-03-06 17:11:15 +08:00
parent bcf64f0041
commit 1e53d16b3a
9 changed files with 1380 additions and 38 deletions
--- a/services/fund_service_async.py
+++ b/services/fund_service_async.py
@@ -0,0 +1,225 @@
+import asyncio
+import pandas as pd
+from typing import List, Dict, Any, Optional
+from logger import get_logger
+from datetime import datetime, timedelta
+
+# 获取日志器
+logger = get_logger()
+
+class FundServiceAsync:
+    """
+    异步基金服务
+    提供基金数据的异步搜索和获取功能
+    """
+    
+    def __init__(self):
+        """初始化异步基金服务"""
+        logger.debug("初始化FundServiceAsync")
+        
+        # 添加缓存
+        self._etf_cache = None
+        self._lof_cache = None
+        self._cache_timestamp = None
+        self._cache_duration = timedelta(minutes=30)  # 缓存30分钟
+    
+    async def search_funds(self, keyword: str, market_type: str = 'ETF') -> List[Dict[str, Any]]:
+        """
+        异步搜索基金代码
+        
+        Args:
+            keyword: 搜索关键词
+            market_type: 市场类型，'ETF'或'LOF'
+            
+        Returns:
+            匹配的基金列表
+        """
+        try:
+            logger.info(f"异步搜索基金: {keyword}, 类型: {market_type}")
+            
+            # 获取基金数据
+            df = await self._get_funds_data(market_type)
+            
+            # 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,
+                })
+            
+            logger.info(f"基金搜索完成，找到 {len(formatted_results)} 个匹配项")
+            return formatted_results
+            
+        except Exception as e:
+            error_msg = f"搜索基金代码失败: {str(e)}"
+            logger.error(error_msg)
+            logger.exception(e)
+            raise Exception(error_msg)
+    
+    async def _get_funds_data(self, market_type: str = 'ETF') -> pd.DataFrame:
+        """
+        异步获取基金数据，支持缓存
+        
+        Args:
+            market_type: 市场类型，'ETF'或'LOF'
+            
+        Returns:
+            包含基金数据的DataFrame
+        """
+        # 检查缓存是否有效
+        now = datetime.now()
+        cache_valid = (
+            self._cache_timestamp is not None and 
+            (now - self._cache_timestamp) < self._cache_duration
+        )
+        
+        if market_type == 'ETF' and cache_valid and self._etf_cache is not None:
+            logger.debug("使用ETF缓存数据")
+            return self._etf_cache
+        elif market_type == 'LOF' and cache_valid and self._lof_cache is not None:
+            logger.debug("使用LOF缓存数据")
+            return self._lof_cache
+        
+        # 缓存无效，重新获取数据
+        try:
+            logger.debug(f"从API获取{market_type}数据")
+            
+            # 使用线程池执行同步的akshare调用
+            if market_type == 'ETF':
+                df = await asyncio.to_thread(self._get_etf_data)
+                self._etf_cache = df
+            else:
+                df = await asyncio.to_thread(self._get_lof_data)
+                self._lof_cache = df
+                
+            self._cache_timestamp = now
+            return df
+            
+        except Exception as e:
+            logger.error(f"获取{market_type}数据失败: {str(e)}")
+            logger.exception(e)
+            raise
+    
+    def _get_etf_data(self) -> pd.DataFrame:
+        """
+        获取ETF数据（同步方法，将被异步方法调用）
+        
+        Returns:
+            包含ETF数据的DataFrame
+        """
+        import akshare as ak
+        
+        try:
+            # 获取ETF基金数据
+            df = ak.fund_etf_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",
+            })
+            
+            return df
+            
+        except Exception as e:
+            logger.error(f"获取ETF数据失败: {str(e)}")
+            logger.exception(e)
+            raise Exception(f"获取ETF数据失败: {str(e)}")
+    
+    def _get_lof_data(self) -> pd.DataFrame:
+        """
+        获取LOF数据（同步方法，将被异步方法调用）
+        
+        Returns:
+            包含LOF数据的DataFrame
+        """
+        import akshare as ak
+        
+        try:
+            # 获取LOF基金数据
+            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",
+            })
+            
+            return df
+            
+        except Exception as e:
+            logger.error(f"获取LOF数据失败: {str(e)}")
+            logger.exception(e)
+            raise Exception(f"获取LOF数据失败: {str(e)}")
+            
+    async def get_fund_detail(self, symbol: str, market_type: str = 'ETF') -> Dict[str, Any]:
+        """
+        异步获取单个基金详细信息
+        
+        Args:
+            symbol: 基金代码
+            market_type: 市场类型，'ETF'或'LOF'
+            
+        Returns:
+            基金详细信息
+        """
+        try:
+            logger.info(f"获取{market_type}基金详情: {symbol}")
+            
+            # 获取基金数据
+            df = await self._get_funds_data(market_type)
+            
+            # 精确匹配基金代码
+            result = df[df['symbol'] == symbol]
+            
+            if len(result) == 0:
+                raise Exception(f"未找到基金代码: {symbol}")
+            
+            # 获取第一行数据
+            row = result.iloc[0]
+            
+            # 格式化为字典
+            fund_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,
+                '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,
+                'discount_rate': float(row['discount_rate'].strip('%'))/100 if pd.notna(row['discount_rate']) else 0.0
+            }
+            
+            logger.info(f"获取基金详情成功: {symbol}")
+            return fund_detail
+            
+        except Exception as e:
+            error_msg = f"获取基金详情失败: {str(e)}"
+            logger.error(error_msg)
+            logger.exception(e)
+            raise Exception(error_msg)