refactor: 重构代码结构

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