refactor: 重构代码结构

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