refactor: 重构代码结构

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})