chore: 移除不再使用的 ChanMacro、system、tests。

这些目录已废弃，从仓库中清理。

Co-authored-by: Cursor <cursoragent@cursor.com>

ChanMacro/expectancy/decay.py

@@ -1,55 +0,0 @@
-"""
-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)