refactor: 重构代码结构

services/__init__.py

@@ -0,0 +1,2 @@
+# services包初始化文件
+# 用于组织股票分析服务的各个模块 

services/ai_analyzer.py

@@ -0,0 +1,269 @@
+import pandas as pd
+import numpy as np
+import os
+import json
+import asyncio
+import httpx
+from typing import Dict, List, Optional, Any, Generator, AsyncGenerator
+from dotenv import load_dotenv
+from logger import get_logger
+from utils.api_utils import APIUtils
+
+# 获取日志器
+logger = get_logger()
+
+class AIAnalyzer:
+    """
+    异步AI分析服务
+    负责调用AI API对股票数据进行分析
+    """
+    
+    def __init__(self, custom_api_url=None, custom_api_key=None, custom_api_model=None, custom_api_timeout=None):
+        """
+        初始化AI分析服务
+        
+        Args:
+            custom_api_url: 自定义API URL
+            custom_api_key: 自定义API密钥
+            custom_api_model: 自定义API模型
+            custom_api_timeout: 自定义API超时时间
+        """
+        # 加载环境变量
+        load_dotenv()
+        
+        # 设置API配置
+        self.API_URL = custom_api_url or os.getenv('API_URL')
+        self.API_KEY = custom_api_key or os.getenv('API_KEY')
+        self.API_MODEL = custom_api_model or os.getenv('API_MODEL', 'gpt-3.5-turbo')
+        self.API_TIMEOUT = int(custom_api_timeout or os.getenv('API_TIMEOUT', 60))
+        
+        logger.debug(f"初始化AIAnalyzer: API_URL={self.API_URL}, API_MODEL={self.API_MODEL}, API_KEY={'已提供' if self.API_KEY else '未提供'}, API_TIMEOUT={self.API_TIMEOUT}")
+    
+    async def get_ai_analysis(self, df: pd.DataFrame, stock_code: str, market_type: str = 'A', stream: bool = False) -> AsyncGenerator[str, None]:
+        """
+        对股票数据进行AI分析
+        
+        Args:
+            df: 包含技术指标的DataFrame
+            stock_code: 股票代码
+            market_type: 市场类型，默认为'A'股
+            stream: 是否使用流式响应
+            
+        Returns:
+            异步生成器，生成分析结果字符串
+        """
+        try:
+            logger.info(f"开始AI分析 {stock_code}, 流式模式: {stream}")
+            recent_data = df.tail(14).to_dict('records')
+            
+            technical_summary = {
+                'trend': 'upward' if df.iloc[-1]['MA5'] > df.iloc[-1]['MA20'] else 'downward',
+                'volatility': f"{df.iloc[-1]['Volatility']:.2f}%",
+                'volume_trend': 'increasing' if df.iloc[-1]['Volume_Ratio'] > 1 else 'decreasing',
+                'rsi_level': df.iloc[-1]['RSI']
+            }
+            
+            # 根据市场类型调整分析提示
+            if market_type in ['ETF', 'LOF']:
+                prompt = f"""
+                分析基金 {stock_code}：
+
+                技术指标概要：
+                {technical_summary}
+                
+                近14日交易数据：
+                {recent_data}
+                
+                请分析该基金的技术面状况，包括：
+                1. 趋势分析：判断基金当前的趋势方向
+                2. 动量分析：基于RSI和交易量评估基金动量
+                3. 支撑与阻力位：确定关键价格位
+                4. 技术面总结
+                5. 投资建议
+
+                将分析结果格式化为JSON，像这样：
+                {{
+                    "trend_analysis": "趋势分析结果...",
+                    "momentum_analysis": "动量分析结果...",
+                    "support_resistance": "支撑阻力位分析...",
+                    "technical_summary": "技术面总结...",
+                    "investment_advice": "投资建议..."
+                }}
+                """
+            else:
+                prompt = f"""
+                分析股票 {stock_code}：
+
+                技术指标概要：
+                {technical_summary}
+                
+                近14日交易数据：
+                {recent_data}
+                
+                请分析该股票的技术面状况，包括：
+                1. 趋势分析：当前趋势方向及强度
+                2. 动量分析：基于MACD、RSI等指标
+                3. 支撑与阻力位：关键价格位分析
+                4. 成交量分析：交易量的变化及意义
+                5. 波动性评估：ATR和波动率分析
+                6. 技术面总结
+                7. 投资建议：根据技术分析给出操作建议
+
+                将分析结果格式化为JSON，像这样：
+                {{
+                    "trend_analysis": "趋势分析结果...",
+                    "momentum_analysis": "动量分析结果...",
+                    "support_resistance": "支撑阻力位分析...",
+                    "volume_analysis": "成交量分析...",
+                    "volatility_assessment": "波动性评估...",
+                    "technical_summary": "技术面总结...",
+                    "investment_advice": "投资建议..."
+                }}
+                """
+            
+            # 格式化API URL
+            api_url = APIUtils.format_api_url(self.API_URL)
+            
+            # 准备请求数据
+            request_data = {
+                "model": self.API_MODEL,
+                "messages": [{"role": "user", "content": prompt}],
+                "temperature": 0.7,
+                "stream": stream
+            }
+            
+            # 准备请求头
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {self.API_KEY}"
+            }
+            
+            # 异步请求API
+            async with httpx.AsyncClient(timeout=self.API_TIMEOUT) as client:
+                # 记录请求
+                logger.debug(f"发送AI请求: URL={api_url}, MODEL={self.API_MODEL}, STREAM={stream}")
+                
+                if stream:
+                    # 流式响应处理
+                    async with client.stream("POST", api_url, json=request_data, headers=headers) as response:
+                        if response.status_code != 200:
+                            error_text = await response.aread()
+                            error_data = json.loads(error_text)
+                            error_message = error_data.get('error', {}).get('message', '未知错误')
+                            logger.error(f"AI API请求失败: {response.status_code} - {error_message}")
+                            yield json.dumps({"error": f"API请求失败: {error_message}"})
+                            return
+                            
+                        # 处理流式响应
+                        buffer = ""
+                        collected_messages = []
+                        
+                        async for chunk in response.aiter_text():
+                            if chunk:
+                                chunk_str = chunk.strip()
+                                if chunk_str.startswith("data: "):
+                                    chunk_str = chunk_str[6:]  # 去除"data: "前缀
+                                    
+                                if chunk_str == "[DONE]":
+                                    continue
+                                    
+                                try:
+                                    # 解析数据块
+                                    chunk_data = json.loads(chunk_str)
+                                    delta = chunk_data.get("choices", [{}])[0].get("delta", {})
+                                    content = delta.get("content", "")
+                                    
+                                    if content:
+                                        buffer += content
+                                        # 尝试提取完整的JSON
+                                        if buffer.strip().startswith("{") and buffer.strip().endswith("}"):
+                                            try:
+                                                result_json = json.loads(buffer)
+                                                yield json.dumps({
+                                                    "stock_code": stock_code,
+                                                    "analysis": result_json
+                                                })
+                                                buffer = ""  # 重置缓冲区
+                                            except json.JSONDecodeError:
+                                                # JSON不完整，继续收集
+                                                pass
+                                        
+                                        # 达到一定长度就输出
+                                        if len(buffer) > 100:
+                                            yield json.dumps({
+                                                "stock_code": stock_code,
+                                                "partial_content": buffer
+                                            })
+                                            collected_messages.append(buffer)
+                                            buffer = ""
+                                except json.JSONDecodeError:
+                                    # 忽略无法解析的块
+                                    continue
+                        
+                        # 处理最后的缓冲区
+                        if buffer:
+                            yield json.dumps({
+                                "stock_code": stock_code,
+                                "partial_content": buffer
+                            })
+                            collected_messages.append(buffer)
+                        
+                        # 尝试从整个内容中提取JSON
+                        full_content = "".join(collected_messages)
+                        
+                        # 如果没有成功解析JSON，返回原始内容
+                        if not full_content.strip().startswith("{"):
+                            yield json.dumps({
+                                "stock_code": stock_code,
+                                "raw_analysis": full_content
+                            })
+                else:
+                    # 非流式响应处理
+                    response = await client.post(api_url, json=request_data, headers=headers)
+                    
+                    if response.status_code != 200:
+                        error_data = response.json()
+                        error_message = error_data.get('error', {}).get('message', '未知错误')
+                        logger.error(f"AI API请求失败: {response.status_code} - {error_message}")
+                        yield json.dumps({"error": f"API请求失败: {error_message}"})
+                        return
+                    
+                    response_data = response.json()
+                    analysis_text = response_data.get("choices", [{}])[0].get("message", {}).get("content", "")
+                    
+                    try:
+                        # 尝试解析JSON
+                        analysis_json = json.loads(analysis_text)
+                        yield json.dumps({
+                            "stock_code": stock_code,
+                            "analysis": analysis_json
+                        })
+                    except json.JSONDecodeError:
+                        # 返回原始文本
+                        yield json.dumps({
+                            "stock_code": stock_code,
+                            "raw_analysis": analysis_text
+                        })
+                        
+            logger.info(f"完成对 {stock_code} 的AI分析")
+                
+        except Exception as e:
+            logger.error(f"AI分析 {stock_code} 时出错: {str(e)}")
+            logger.exception(e)
+            yield json.dumps({"error": f"分析出错: {str(e)}"})
+    
+    def _truncate_json_for_logging(self, json_obj, max_length=500):
+        """
+        截断JSON对象以便记录日志
+        
+        Args:
+            json_obj: JSON对象
+            max_length: 最大长度
+            
+        Returns:
+            截断后的字符串
+        """
+        json_str = json.dumps(json_obj, ensure_ascii=False)
+        if len(json_str) <= max_length:
+            return json_str
+        return json_str[:max_length] + "..." 

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) 

services/stock_analyzer_service.py

@@ -0,0 +1,178 @@
+import pandas as pd
+import numpy as np
+import asyncio
+import json
+from typing import Dict, List, Optional, Tuple, Any, AsyncGenerator
+from logger import get_logger
+from services.stock_data_provider import StockDataProvider
+from services.technical_indicator import TechnicalIndicator
+from services.stock_scorer import StockScorer
+from services.ai_analyzer import AIAnalyzer
+
+# 获取日志器
+logger = get_logger()
+
+class StockAnalyzerService:
+    """
+    股票分析服务
+    作为门面类协调数据提供、指标计算、评分和AI分析等组件
+    """
+    
+    def __init__(self, custom_api_url=None, custom_api_key=None, custom_api_model=None, custom_api_timeout=None):
+        """
+        初始化股票分析服务
+        
+        Args:
+            custom_api_url: 自定义API URL
+            custom_api_key: 自定义API密钥
+            custom_api_model: 自定义API模型
+            custom_api_timeout: 自定义API超时时间
+        """
+        # 初始化各个组件
+        self.data_provider = StockDataProvider()
+        self.indicator = TechnicalIndicator()
+        self.scorer = StockScorer()
+        self.ai_analyzer = AIAnalyzer(
+            custom_api_url=custom_api_url,
+            custom_api_key=custom_api_key,
+            custom_api_model=custom_api_model,
+            custom_api_timeout=custom_api_timeout
+        )
+        
+        logger.info("初始化StockAnalyzerService完成")
+    
+    async def analyze_stock(self, stock_code: str, market_type: str = 'A', stream: bool = False) -> AsyncGenerator[str, None]:
+        """
+        分析单只股票
+        
+        Args:
+            stock_code: 股票代码
+            market_type: 市场类型，默认为'A'股
+            stream: 是否使用流式响应
+            
+        Returns:
+            异步生成器，生成分析结果的JSON字符串
+        """
+        try:
+            logger.info(f"开始分析股票: {stock_code}, 市场: {market_type}")
+            
+            # 获取股票数据
+            df = await self.data_provider.get_stock_data(stock_code, market_type)
+            
+            # 计算技术指标
+            df_with_indicators = self.indicator.calculate_indicators(df)
+            
+            # 计算评分
+            score = self.scorer.calculate_score(df_with_indicators)
+            recommendation = self.scorer.get_recommendation(score)
+            
+            # 生成基本分析结果
+            basic_result = {
+                "stock_code": stock_code,
+                "score": score,
+                "recommendation": recommendation,
+                "data_point_count": len(df),
+                "market_type": market_type
+            }
+            
+            # 输出基本分析结果
+            logger.info(f"基本分析结果: {json.dumps(basic_result)}")
+            yield json.dumps(basic_result)
+            
+            # 使用AI进行深入分析
+            async for analysis_chunk in self.ai_analyzer.get_ai_analysis(df_with_indicators, stock_code, market_type, stream):
+                yield analysis_chunk
+                
+            logger.info(f"完成股票分析: {stock_code}")
+            
+        except Exception as e:
+            error_msg = f"分析股票 {stock_code} 时出错: {str(e)}"
+            logger.error(error_msg)
+            logger.exception(e)
+            yield json.dumps({"error": error_msg})
+    
+    async def scan_stocks(self, stock_codes: List[str], market_type: str = 'A', min_score: int = 0, stream: bool = False) -> AsyncGenerator[str, None]:
+        """
+        批量扫描股票
+        
+        Args:
+            stock_codes: 股票代码列表
+            market_type: 市场类型
+            min_score: 最低评分阈值
+            stream: 是否使用流式响应
+            
+        Returns:
+            异步生成器，生成扫描结果的JSON字符串
+        """
+        try:
+            logger.info(f"开始批量扫描 {len(stock_codes)} 只股票, 市场: {market_type}")
+            
+            # 输出初始状态
+            yield json.dumps({
+                "status": "scanning",
+                "total_stocks": len(stock_codes),
+                "market_type": market_type,
+                "min_score": min_score
+            })
+            
+            # 批量获取股票数据
+            stock_data_dict = await self.data_provider.get_multiple_stocks_data(stock_codes, market_type)
+            
+            # 计算技术指标
+            stock_with_indicators = {}
+            for code, df in stock_data_dict.items():
+                try:
+                    stock_with_indicators[code] = self.indicator.calculate_indicators(df)
+                except Exception as e:
+                    logger.error(f"计算 {code} 技术指标时出错: {str(e)}")
+            
+            # 评分股票
+            results = self.scorer.batch_score_stocks(stock_with_indicators)
+            
+            # 过滤低于最低评分的股票
+            filtered_results = [r for r in results if r[1] >= min_score]
+            
+            # 输出评分结果
+            yield json.dumps({
+                "scan_results": [
+                    {
+                        "stock_code": code,
+                        "score": score,
+                        "recommendation": rec
+                    } for code, score, rec in filtered_results
+                ],
+                "total_matched": len(filtered_results),
+                "total_scanned": len(results)
+            })
+            
+            # 如果需要进一步分析，对评分较高的股票进行AI分析
+            if stream and filtered_results:
+                top_stocks = filtered_results[:3]  # 只分析前3只评分最高的股票
+                
+                for stock_code, score, _ in top_stocks:
+                    df = stock_with_indicators.get(stock_code)
+                    if df is not None:
+                        # 输出正在分析的股票信息
+                        yield json.dumps({
+                            "analyzing": stock_code,
+                            "score": score
+                        })
+                        
+                        # AI分析
+                        async for analysis_chunk in self.ai_analyzer.get_ai_analysis(df, stock_code, market_type, stream):
+                            yield analysis_chunk
+            
+            # 输出扫描完成信息
+            yield json.dumps({
+                "status": "completed",
+                "total_scanned": len(results),
+                "total_matched": len(filtered_results)
+            })
+            
+            logger.info(f"完成批量扫描 {len(stock_codes)} 只股票, 符合条件: {len(filtered_results)}")
+            
+        except Exception as e:
+            error_msg = f"批量扫描股票时出错: {str(e)}"
+            logger.error(error_msg)
+            logger.exception(e)
+            yield json.dumps({"error": error_msg}) 

services/stock_data_provider.py

@@ -0,0 +1,184 @@
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import asyncio
+import os
+from typing import Dict, List, Optional, Tuple, Any
+from logger import get_logger
+
+# 获取日志器
+logger = get_logger()
+
+class StockDataProvider:
+    """
+    异步股票数据提供服务
+    负责获取股票、基金等金融产品的历史数据
+    """
+    
+    def __init__(self):
+        """初始化数据提供者服务"""
+        logger.debug("初始化StockDataProvider")
+    
+    async def get_stock_data(self, stock_code: str, market_type: str = 'A', 
+                            start_date: Optional[str] = None, 
+                            end_date: Optional[str] = None) -> pd.DataFrame:
+        """
+        异步获取股票或基金数据
+        
+        Args:
+            stock_code: 股票代码
+            market_type: 市场类型，默认为'A'股
+            start_date: 开始日期，格式YYYYMMDD，默认为一年前
+            end_date: 结束日期，格式YYYYMMDD，默认为今天
+            
+        Returns:
+            包含历史数据的DataFrame
+        """
+        # 使用线程池执行同步的akshare调用
+        return await asyncio.to_thread(
+            self._get_stock_data_sync, 
+            stock_code, 
+            market_type, 
+            start_date, 
+            end_date
+        )
+    
+    def _get_stock_data_sync(self, stock_code: str, market_type: str = 'A', 
+                           start_date: Optional[str] = None, 
+                           end_date: Optional[str] = None) -> pd.DataFrame:
+        """
+        同步获取股票数据的实现
+        将被异步方法调用
+        """
+        import akshare as ak
+        
+        if start_date is None:
+            start_date = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d')
+        if end_date is None:
+            end_date = datetime.now().strftime('%Y%m%d')
+            
+        try:
+            # 验证股票代码格式
+            if market_type == 'A':
+                # 上海证券交易所股票代码以6开头
+                # 深圳证券交易所股票代码以0或3开头
+                # 科创板股票代码以688开头
+                # 北京证券交易所股票代码以8开头
+                valid_prefixes = ['0', '3', '6', '688', '8']
+                valid_format = False
+                
+                for prefix in valid_prefixes:
+                    if stock_code.startswith(prefix):
+                        valid_format = True
+                        break
+                
+                if not valid_format:
+                    error_msg = f"无效的A股股票代码格式: {stock_code}。A股代码应以0、3、6、688或8开头"
+                    logger.error(f"[股票代码格式错误] {error_msg}")
+                    raise ValueError(error_msg)
+
+                logger.debug(f"获取A股数据: {stock_code}")
+                df = ak.stock_zh_a_hist(
+                    symbol=stock_code,
+                    start_date=start_date,
+                    end_date=end_date,
+                    adjust="qfq"
+                )
+                
+            elif market_type in ['HK']:
+                logger.debug(f"获取港股数据: {stock_code}")
+                df = ak.stock_hk_daily(
+                    symbol=stock_code,
+                    start_date=start_date,
+                    end_date=end_date,
+                    adjust="qfq"
+                )
+                
+            elif market_type in ['US']:
+                logger.debug(f"获取美股数据: {stock_code}")
+                df = ak.stock_us_daily(
+                    symbol=stock_code,
+                    adjust="qfq"
+                )
+                # 过滤日期
+                df = df[(df.index >= start_date) & (df.index <= end_date)]
+                
+            elif market_type in ['ETF', 'LOF']:
+                logger.debug(f"获取{market_type}基金数据: {stock_code}")
+                df = ak.fund_etf_hist_sina(
+                    symbol=stock_code,
+                    start_date=start_date.replace('-', ''),
+                    end_date=end_date.replace('-', '')
+                )
+                
+            else:
+                error_msg = f"不支持的市场类型: {market_type}"
+                logger.error(f"[市场类型错误] {error_msg}")
+                raise ValueError(error_msg)
+                
+            # 标准化列名
+            if market_type == 'A':
+                # 根据实际数据结构调整列名映射
+                # 实际数据列：['日期', '股票代码', '开盘', '收盘', '最高', '最低', '成交量', '成交额', '振幅', '涨跌幅', '涨跌额', '换手率']
+                df.columns = ['Date', 'Code', 'Open', 'Close', 'High', 'Low', 'Volume', 'Amount', 'Amplitude', 'Change_pct', 'Change', 'Turnover']
+            elif market_type in ['HK', 'US']:
+                # 根据实际情况调整
+                df.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Amount']
+            elif market_type in ['ETF', 'LOF']:
+                # 基金数据可能有不同的列
+                df.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Amount']
+                
+            # 确保日期列是日期类型
+            if 'Date' in df.columns:
+                df['Date'] = pd.to_datetime(df['Date'])
+                df.set_index('Date', inplace=True)
+                
+            # 确保按日期升序排序
+            df.sort_index(inplace=True)
+                
+            logger.info(f"成功获取{market_type}数据 {stock_code}, 数据点数: {len(df)}")
+            return df
+            
+        except Exception as e:
+            error_msg = f"获取{market_type}数据失败 {stock_code}: {str(e)}"
+            logger.error(error_msg)
+            logger.exception(e)
+            raise Exception(error_msg)
+            
+    async def get_multiple_stocks_data(self, stock_codes: List[str], 
+                                     market_type: str = 'A',
+                                     start_date: Optional[str] = None, 
+                                     end_date: Optional[str] = None,
+                                     max_concurrency: int = 5) -> Dict[str, pd.DataFrame]:
+        """
+        异步批量获取多只股票数据
+        
+        Args:
+            stock_codes: 股票代码列表
+            market_type: 市场类型，默认为'A'股
+            start_date: 开始日期，格式YYYYMMDD
+            end_date: 结束日期，格式YYYYMMDD
+            max_concurrency: 最大并发数，默认为5
+            
+        Returns:
+            字典，键为股票代码，值为对应的DataFrame
+        """
+        # 使用信号量控制并发数
+        semaphore = asyncio.Semaphore(max_concurrency)
+        
+        async def get_with_semaphore(code):
+            async with semaphore:
+                try:
+                    return code, await self.get_stock_data(code, market_type, start_date, end_date)
+                except Exception as e:
+                    logger.error(f"获取股票 {code} 数据时出错: {str(e)}")
+                    return code, None
+        
+        # 创建异步任务
+        tasks = [get_with_semaphore(code) for code in stock_codes]
+        
+        # 等待所有任务完成
+        results = await asyncio.gather(*tasks)
+        
+        # 构建结果字典，过滤掉失败的请求
+        return {code: df for code, df in results if df is not None} 

services/stock_scorer.py

@@ -0,0 +1,128 @@
+import pandas as pd
+import numpy as np
+from typing import Dict, Optional, Any, List, Tuple
+from logger import get_logger
+
+# 获取日志器
+logger = get_logger()
+
+class StockScorer:
+    """
+    股票评分服务
+    负责根据技术指标计算股票的综合评分
+    """
+    
+    def __init__(self):
+        """初始化股票评分服务"""
+        logger.debug("初始化StockScorer")
+    
+    def calculate_score(self, df: pd.DataFrame) -> int:
+        """
+        计算股票评分（满分100分）
+        
+        Args:
+            df: 包含技术指标的DataFrame
+            
+        Returns:
+            股票评分（0-100的整数）
+        """
+        try:
+            # 使用最新的数据点进行评分
+            latest = df.iloc[-1]
+            
+            # 初始得分为0
+            score = 0
+            
+            # 移动平均线评分（25分）
+            if latest['MA5'] > latest['MA20'] > latest['MA60']:
+                # 短期、中期和长期均线呈多头排列
+                score += 25
+            elif latest['MA5'] > latest['MA20']:
+                # 短期均线在中期均线之上
+                score += 15
+            elif latest['Close'] > latest['MA20']:
+                # 股价在中期均线之上
+                score += 10
+                
+            # RSI评分（25分）
+            rsi = latest['RSI']
+            if 45 <= rsi <= 55:
+                # RSI在中间区域，可能即将爆发
+                score += 15
+            elif 55 < rsi < 70:
+                # RSI在强势区域但未超买
+                score += 25
+            elif 30 < rsi < 45:
+                # RSI在弱势区域但未超卖
+                score += 10
+            elif rsi >= 70:
+                # RSI超买
+                score += 5
+            elif rsi <= 30:
+                # RSI超卖
+                score += 15
+                
+            # MACD得分（20分）
+            if latest['MACD'] > latest['Signal']:
+                score += 20
+                
+            # 成交量得分（30分）
+            if latest['Volume_Ratio'] > 1.5:
+                score += 30
+            elif latest['Volume_Ratio'] > 1:
+                score += 15
+                
+            return score
+            
+        except Exception as e:
+            logger.error(f"计算评分时出错: {str(e)}")
+            logger.exception(e)
+            raise
+            
+    def get_recommendation(self, score: int) -> str:
+        """
+        根据评分获取投资建议
+        
+        Args:
+            score: 股票评分（0-100）
+            
+        Returns:
+            投资建议文本
+        """
+        if score >= 80:
+            return "强烈推荐"
+        elif score >= 70:
+            return "推荐"
+        elif score >= 60:
+            return "谨慎推荐"
+        elif score >= 40:
+            return "观望"
+        elif score >= 20:
+            return "不推荐"
+        else:
+            return "强烈不推荐"
+            
+    def batch_score_stocks(self, stock_dfs: Dict[str, pd.DataFrame]) -> List[Tuple[str, int, str]]:
+        """
+        批量评分多只股票
+        
+        Args:
+            stock_dfs: 字典，键为股票代码，值为DataFrame
+            
+        Returns:
+            评分结果列表，每项为(股票代码, 评分, 推荐)的三元组
+        """
+        results = []
+        
+        for stock_code, df in stock_dfs.items():
+            try:
+                score = self.calculate_score(df)
+                recommendation = self.get_recommendation(score)
+                results.append((stock_code, score, recommendation))
+            except Exception as e:
+                logger.error(f"评分股票 {stock_code} 时出错: {str(e)}")
+                
+        # 按评分降序排序
+        results.sort(key=lambda x: x[1], reverse=True)
+        
+        return results 

services/technical_indicator.py

@@ -0,0 +1,187 @@
+import pandas as pd
+import numpy as np
+from typing import Dict, Optional, Any
+from logger import get_logger
+
+# 获取日志器
+logger = get_logger()
+
+class TechnicalIndicator:
+    """
+    技术指标计算服务
+    负责计算常见的股票技术指标
+    """
+    
+    def __init__(self, params: Optional[Dict[str, Any]] = None):
+        """
+        初始化技术指标计算服务
+        
+        Args:
+            params: 技术指标参数配置
+        """
+        # 默认参数设置
+        self.params = params or {
+            'ma_periods': {'short': 5, 'medium': 20, 'long': 60},
+            'rsi_period': 14,
+            'bollinger_period': 20,
+            'bollinger_std': 2,
+            'volume_ma_period': 20,
+            'atr_period': 14
+        }
+        
+        logger.debug(f"初始化TechnicalIndicator，参数: {self.params}")
+    
+    def calculate_ema(self, series: pd.Series, period: int) -> pd.Series:
+        """
+        计算指数移动平均线
+        
+        Args:
+            series: 价格序列
+            period: 周期
+            
+        Returns:
+            EMA序列
+        """
+        return series.ewm(span=period, adjust=False).mean()
+    
+    def calculate_rsi(self, series: pd.Series, period: int) -> pd.Series:
+        """
+        计算相对强弱指标(RSI)
+        
+        Args:
+            series: 价格序列
+            period: 周期
+            
+        Returns:
+            RSI序列
+        """
+        delta = series.diff()
+        gain = delta.where(delta > 0, 0)
+        loss = -delta.where(delta < 0, 0)
+        
+        avg_gain = gain.rolling(window=period).mean()
+        avg_loss = loss.rolling(window=period).mean()
+        
+        rs = avg_gain / avg_loss
+        rsi = 100 - (100 / (1 + rs))
+        
+        return rsi
+    
+    def calculate_macd(self, series: pd.Series) -> tuple:
+        """
+        计算MACD指标
+        
+        Args:
+            series: 价格序列
+            
+        Returns:
+            (MACD线, 信号线, 柱状图)的元组
+        """
+        ema12 = self.calculate_ema(series, 12)
+        ema26 = self.calculate_ema(series, 26)
+        
+        macd = ema12 - ema26
+        signal = self.calculate_ema(macd, 9)
+        histogram = macd - signal
+        
+        return macd, signal, histogram
+    
+    def calculate_bollinger_bands(self, series: pd.Series, period: int, std_dev: float) -> tuple:
+        """
+        计算布林带
+        
+        Args:
+            series: 价格序列
+            period: 周期
+            std_dev: 标准差倍数
+            
+        Returns:
+            (中轨, 上轨, 下轨)的元组
+        """
+        middle = series.rolling(window=period).mean()
+        std = series.rolling(window=period).std()
+        
+        upper = middle + std_dev * std
+        lower = middle - std_dev * std
+        
+        return middle, upper, lower
+    
+    def calculate_atr(self, df: pd.DataFrame, period: int) -> pd.Series:
+        """
+        计算平均真实波幅(ATR)
+        
+        Args:
+            df: 包含High, Low, Close列的DataFrame
+            period: 周期
+            
+        Returns:
+            ATR序列
+        """
+        high = df['High']
+        low = df['Low']
+        close = df['Close']
+        
+        tr1 = high - low
+        tr2 = abs(high - close.shift())
+        tr3 = abs(low - close.shift())
+        
+        tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+        atr = tr.rolling(window=period).mean()
+        
+        return atr
+    
+    def calculate_indicators(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        计算所有技术指标
+        
+        Args:
+            df: 原始价格数据，包含Open, High, Low, Close, Volume列
+            
+        Returns:
+            添加了技术指标的DataFrame
+        """
+        try:
+            # 复制数据框
+            result_df = df.copy()
+            
+            # 移动平均线
+            for name, period in self.params['ma_periods'].items():
+                result_df[f'MA{period}'] = result_df['Close'].rolling(window=period).mean()
+            
+            # RSI
+            result_df['RSI'] = self.calculate_rsi(result_df['Close'], self.params['rsi_period'])
+            
+            # MACD
+            macd, signal, histogram = self.calculate_macd(result_df['Close'])
+            result_df['MACD'] = macd
+            result_df['Signal'] = signal
+            result_df['Histogram'] = histogram
+            
+            # 布林带
+            middle, upper, lower = self.calculate_bollinger_bands(
+                result_df['Close'], 
+                self.params['bollinger_period'], 
+                self.params['bollinger_std']
+            )
+            result_df['BB_Middle'] = middle
+            result_df['BB_Upper'] = upper
+            result_df['BB_Lower'] = lower
+            
+            # 成交量移动平均
+            result_df['Volume_MA'] = result_df['Volume'].rolling(window=self.params['volume_ma_period']).mean()
+            
+            # 成交量比率
+            result_df['Volume_Ratio'] = result_df['Volume'] / result_df['Volume_MA']
+            
+            # ATR
+            result_df['ATR'] = self.calculate_atr(result_df, self.params['atr_period'])
+            
+            # 波动率 (过去20天收盘价的标准差/均值)
+            result_df['Volatility'] = result_df['Close'].rolling(window=20).std() / result_df['Close'].rolling(window=20).mean() * 100
+            
+            return result_df
+            
+        except Exception as e:
+            logger.error(f"计算技术指标时出错: {str(e)}")
+            logger.exception(e)
+            raise 

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)