"""
expectancy/decay.py — Time-weighted sample decay.

2024 market structure ≠ 2026 market structure.
Recent samples get higher weight via exponential decay.
"""

from datetime import date as Date
from typing import Optional
import numpy as np


class TimeDecay:
    """Exponential time decay for sample weighting."""

    def __init__(self, half_life_days: int = 180):
        self.half_life = half_life_days
        self._decay_rate = np.log(2) / half_life_days

    def weight(self, sample_date: Date, reference_date: Optional[Date] = None) -> float:
        """
        Compute decay weight for a sample.
        weight = exp(-days_ago * decay_rate)
        """
        if reference_date is None:
            reference_date = Date.today()
        days = (reference_date - sample_date).days
        return np.exp(-days * self._decay_rate)

    def weights(self, dates: list[Date], reference_date: Optional[Date] = None) -> np.ndarray:
        """Compute decay weights for a list of dates."""
        return np.array([self.weight(d, reference_date) for d in dates])

    def weighted_win_rate(self, wins: np.ndarray, weights: np.ndarray) -> float:
        """Weighted win rate: sum(wins * weights) / sum(weights)."""
        total_weight = weights.sum()
        if total_weight == 0:
            return 0.0
        return float((wins * weights).sum() / total_weight)

    def weighted_mean(self, values: np.ndarray, weights: np.ndarray) -> float:
        """Weighted mean."""
        total_weight = weights.sum()
        if total_weight == 0:
            return 0.0
        return float((values * weights).sum() / total_weight)

    def effective_samples(self, weights: np.ndarray) -> float:
        """Effective number of samples after decay weighting."""
        return float(weights.sum())

    @staticmethod
    def weight_at_age(days_ago: int, half_life_days: int = 180) -> float:
        """Quick weight lookup for a given age in days."""
        return np.exp(-days_ago * np.log(2) / half_life_days)