第一版流程

factors.py

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+"""
+因子挖掘模块：支持规则因子和遗传编程因子
+"""
+import numpy as np
+import pandas as pd
+from typing import Callable, Dict, List, Optional
+from abc import ABC, abstractmethod
+
+
+class BaseFactor(ABC):
+    """因子基类"""
+    
+    def __init__(self, name: str):
+        self.name = name
+    
+    @abstractmethod
+    def compute(self, data: pd.DataFrame) -> pd.Series:
+        """计算因子值"""
+        pass
+
+
+class RuleFactor(BaseFactor):
+    """规则因子：基于固定规则"""
+    
+    def __init__(self, name: str, compute_func: Callable[[pd.DataFrame], pd.Series]):
+        super().__init__(name)
+        self.compute_func = compute_func
+    
+    def compute(self, data: pd.DataFrame) -> pd.Series:
+        return self.compute_func(data)
+
+
+def create_trend_factor(data: pd.DataFrame) -> pd.Series:
+    """趋势因子：价格趋势方向"""
+    trend = pd.Series(0, index=data.index)
+    trend[data['close'] > data['ema16']] = 1
+    trend[data['close'] < data['ema4']] = -1
+    return trend
+
+
+def create_volatility_factor(data: pd.DataFrame) -> pd.Series:
+    """波动率因子：滚动12期收益率标准差"""
+    return data['volatility']
+
+
+def create_volume_price_factor(data: pd.DataFrame) -> pd.Series:
+    """量价因子：成交量放大且价格上涨"""
+    volume_signal = (data['volume'] > data['volume_ma6']).astype(int)
+    return volume_signal * data['return']
+
+
+def create_reversal_factor(data: pd.DataFrame) -> pd.Series:
+    """反转因子：短期反转效应"""
+    return -data['return'].shift(1)
+
+
+def create_momentum_factor(data: pd.DataFrame) -> pd.Series:
+    """动量因子：基于MACD"""
+    return data['macd']
+
+
+def create_rsi_factor(data: pd.DataFrame) -> pd.Series:
+    """RSI因子：相对强弱指数（标准化）"""
+    return (data['rsi'] - 50) / 50  # 归一化到[-1, 1]
+
+
+class FactorMiner:
+    """因子挖掘器"""
+    
+    def __init__(self):
+        self.factors: Dict[str, BaseFactor] = {}
+    
+    def register_factor(self, factor: BaseFactor):
+        """注册因子"""
+        self.factors[factor.name] = factor
+    
+    def register_rule_factor(self, name: str, compute_func: Callable):
+        """注册规则因子"""
+        factor = RuleFactor(name, compute_func)
+        self.register_factor(factor)
+    
+    def compute_all_factors(self, data: pd.DataFrame) -> pd.DataFrame:
+        """计算所有因子"""
+        factor_df = pd.DataFrame(index=data.index)
+        
+        for name, factor in self.factors.items():
+            try:
+                factor_df[name] = factor.compute(data)
+            except Exception as e:
+                print(f"计算因子 {name} 时出错: {e}")
+                factor_df[name] = np.nan
+        
+        return factor_df
+    
+    def get_factor(self, name: str) -> Optional[BaseFactor]:
+        """获取指定因子"""
+        return self.factors.get(name)
+
+
+def create_default_factors() -> FactorMiner:
+    """创建默认因子集合"""
+    miner = FactorMiner()
+    
+    # 注册基础因子
+    miner.register_rule_factor('TREND', create_trend_factor)
+    miner.register_rule_factor('VOL', create_volatility_factor)
+    miner.register_rule_factor('VOLP', create_volume_price_factor)
+    miner.register_rule_factor('REV', create_reversal_factor)
+    miner.register_rule_factor('MOM', create_momentum_factor)
+    miner.register_rule_factor('RSI', create_rsi_factor)
+    
+    return miner
+