"""
动量因子（通用版本）

使用加权线性回归：得分 = 年化收益率 × R²

与现有 MomentumFactor 对比验证：
- 输入相同 → 输出应该相同
"""

import pandas as pd
import numpy as np
import math
from framework_v2.core import FactorBase


class MomentumFactor(FactorBase):
    """
    动量因子
    
    计算加权线性回归动量得分：
    得分 = 年化收益率 × R²
    
    参数：
    - n_days: 动量窗口（默认25）
    - weighted: 是否加权（默认True）
    - crash_filter: 是否启用崩盘过滤（默认True）
    """
    
    name = "momentum"
    category = "momentum"
    
    def __init__(
        self,
        n_days: int = 25,
        weighted: bool = True,
        crash_filter: bool = True
    ):
        super().__init__(n_days=n_days, weighted=weighted, crash_filter=crash_filter)
        self.n_days = n_days
        self.weighted = weighted
        self.crash_filter = crash_filter
    
    def compute(self, data: pd.DataFrame) -> pd.Series:
        """计算动量因子值"""
        if 'close' not in data.columns:
            raise ValueError("data must contain 'close' column")
        
        prices = data['close']
        
        if self.weighted:
            factor_values = prices.rolling(self.n_days).apply(
                lambda x: self._weighted_momentum_score(x.values),
                raw=False
            )
        else:
            factor_values = prices.pct_change(self.n_days)
        
        if self.crash_filter:
            factor_values = self._apply_crash_filter(prices, factor_values)
        
        return factor_values
    
    def _weighted_momentum_score(self, prices: np.ndarray) -> float:
        """计算加权动量得分（完全复制现有逻辑）"""
        if len(prices) < 5:
            return 0.0
        
        # 价格下界 clip，防止 log(0) 或 log(负数)
        prices = np.clip(prices, 0.01, None)
        y = np.log(prices)
        
        # 异常值检测
        if np.any(np.isnan(y)) or np.any(np.isinf(y)):
            return 0.0
        
        x = np.arange(len(y))
        weights = np.linspace(1, 2, len(y))
        
        slope, intercept = np.polyfit(x, y, 1, w=weights)
        annualized_returns = math.exp(slope * 250) - 1
        
        y_pred = slope * x + intercept
        ss_res = np.sum(weights * (y - y_pred) ** 2)
        ss_tot = np.sum(weights * (y - np.average(y, weights=weights)) ** 2)
        r2 = 1 - ss_res / ss_tot if ss_tot > 0 else 0
        
        return annualized_returns * r2
    
    def _apply_crash_filter(self, prices: pd.Series, factor_values: pd.Series) -> pd.Series:
        """崩盘过滤：连续3天跌>5%清零（完全复制现有逻辑）"""
        result = factor_values.copy()
        
        for i in range(3, len(prices)):
            r1 = prices.iloc[i] / prices.iloc[i-1]
            r2 = prices.iloc[i-1] / prices.iloc[i-2]
            r3 = prices.iloc[i-2] / prices.iloc[i-3]
            
            con1 = min(r1, r2, r3) < 0.95
            con2 = (r1 < 1) and (r2 < 1) and (r3 < 1) and (prices.iloc[i] / prices.iloc[i-3] < 0.95)
            
            if con1 or con2:
                result.iloc[i] = 0.0
        
        return result
