factor minner

factor_mining/mining.py

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+"""
+因子挖掘抽象层：支持多种挖掘算法（DEAP、DL、RL等）
+"""
+from abc import ABC, abstractmethod
+from typing import List, Dict, Optional, Any
+import pandas as pd
+from dataclasses import dataclass
+
+from factor_mining.operators import FactorFormula
+
+
+@dataclass
+class MiningConfig:
+    """挖掘配置基类"""
+    ret_horizon: int = 1
+    ic_window: int = 30
+    ic_method: str = "spearman"  # "spearman" or "pearson"
+    seed: Optional[int] = None
+
+
+class FactorMiner(ABC):
+    """因子挖掘器抽象基类"""
+    
+    def __init__(self, config: MiningConfig):
+        self.config = config
+    
+    @abstractmethod
+    def mine(
+        self,
+        data: pd.DataFrame,
+        feature_cols: List[str],
+        price_col: str = "close"
+    ) -> List[FactorFormula]:
+        """
+        挖掘因子
+        
+        Parameters:
+        -----------
+        data : DataFrame
+            数据
+        feature_cols : List[str]
+            特征列名列表
+        price_col : str
+            价格列名
+        
+        Returns:
+        --------
+        List[FactorFormula]: 挖掘出的因子公式列表
+        """
+        pass
+    
+    @abstractmethod
+    def get_name(self) -> str:
+        """获取挖掘器名称"""
+        pass
+
+
+class MiningPipeline:
+    """挖掘流程管理器"""
+    
+    def __init__(self):
+        self.miners: Dict[str, FactorMiner] = {}
+    
+    def register_miner(self, miner: FactorMiner):
+        """注册挖掘器"""
+        name = miner.get_name()
+        if name in self.miners:
+            raise ValueError(f"挖掘器 '{name}' 已存在")
+        self.miners[name] = miner
+    
+    def get_miner(self, name: str) -> Optional[FactorMiner]:
+        """获取挖掘器"""
+        return self.miners.get(name)
+    
+    def list_miners(self) -> List[str]:
+        """列出所有挖掘器"""
+        return list(self.miners.keys())
+    
+    def mine(
+        self,
+        miner_name: str,
+        data: pd.DataFrame,
+        feature_cols: List[str],
+        price_col: str = "close"
+    ) -> List[FactorFormula]:
+        """
+        使用指定挖掘器进行挖掘
+        
+        Parameters:
+        -----------
+        miner_name : str
+            挖掘器名称
+        data : DataFrame
+            数据
+        feature_cols : List[str]
+            特征列名列表
+        price_col : str
+            价格列名
+        
+        Returns:
+        --------
+        List[FactorFormula]: 挖掘出的因子公式列表
+        """
+        miner = self.get_miner(miner_name)
+        if miner is None:
+            raise ValueError(f"挖掘器 '{miner_name}' 不存在")
+        
+        return miner.mine(data, feature_cols, price_col)
+
+
+# 全局挖掘流程管理器
+_pipeline = MiningPipeline()
+
+
+def register_miner(miner: FactorMiner):
+    """注册挖掘器到全局管理器"""
+    _pipeline.register_miner(miner)
+
+
+def get_pipeline() -> MiningPipeline:
+    """获取全局挖掘流程管理器"""
+    return _pipeline
+