feat: add new trading strategies

Add 12 strategy modules including adaptive blend, composite alpha,
cross-asset momentum, ensemble alpha, trend rider v5/v6, and more.

strategies/improved_momentum_quality.py

@@ -0,0 +1,94 @@
+"""
+Improved Momentum Quality Strategy.
+
+Improvements over base MomentumQualityStrategy:
+1. Monthly rebalancing (original rebalances daily → high turnover)
+2. Added recovery factor (strong predictor per IC analysis)
+3. Replaced expensive .apply() consistency calc with vectorized version
+4. Inverse-vol weighting instead of equal-weight
+5. NaN handling fixed throughout
+"""
+
+import numpy as np
+import pandas as pd
+from strategies.base import Strategy
+
+
+class ImprovedMomentumQualityStrategy(Strategy):
+    """
+    Momentum + quality + recovery with monthly rebal and inv-vol weighting.
+    """
+
+    def __init__(
+        self,
+        momentum_period: int = 252,
+        skip: int = 21,
+        quality_window: int = 252,
+        recovery_window: int = 63,
+        vol_window: int = 60,
+        rebal_freq: int = 21,
+        top_n: int = 20,
+    ):
+        self.momentum_period = momentum_period
+        self.skip = skip
+        self.quality_window = quality_window
+        self.recovery_window = recovery_window
+        self.vol_window = vol_window
+        self.rebal_freq = rebal_freq
+        self.top_n = top_n
+
+    def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame:
+        # --- Momentum factor ---
+        momentum = data.shift(self.skip).pct_change(self.momentum_period - self.skip)
+
+        # --- Quality: return consistency (vectorized) ---
+        # Fraction of positive 21-day returns over rolling window
+        monthly_ret = data.pct_change(21)
+        positive_indicator = (monthly_ret > 0).astype(float)
+        consistency = positive_indicator.rolling(
+            self.quality_window, min_periods=self.quality_window // 2
+        ).mean()
+
+        # --- Quality: inverse max drawdown ---
+        rolling_max = data.rolling(self.quality_window, min_periods=self.quality_window // 2).max()
+        drawdown = data / rolling_max - 1
+        worst_dd = drawdown.rolling(self.quality_window, min_periods=self.quality_window // 2).min()
+        inv_dd = -worst_dd  # higher = smaller drawdown = better
+
+        # --- Recovery factor ---
+        recovery = data / data.rolling(self.recovery_window, min_periods=self.recovery_window).min() - 1
+
+        # --- Cross-sectional ranking ---
+        mom_rank = momentum.rank(axis=1, pct=True, na_option="keep")
+        con_rank = consistency.rank(axis=1, pct=True, na_option="keep")
+        dd_rank = inv_dd.rank(axis=1, pct=True, na_option="keep")
+        rec_rank = recovery.rank(axis=1, pct=True, na_option="keep")
+
+        # Composite: momentum 35%, recovery 25%, consistency 20%, drawdown 20%
+        scores = (0.35 * mom_rank + 0.25 * rec_rank +
+                  0.20 * con_rank + 0.20 * dd_rank)
+
+        # --- Select top_n ---
+        n_valid = scores.notna().sum(axis=1)
+        enough = n_valid >= self.top_n
+        score_rank = scores.rank(axis=1, ascending=False, na_option="bottom")
+        top_mask = (score_rank <= self.top_n) & enough.values.reshape(-1, 1)
+
+        # --- Inverse-vol weighting ---
+        returns = data.pct_change()
+        vol = returns.rolling(self.vol_window, min_periods=30).std().replace(0, np.nan)
+        inv_vol = (1.0 / vol).where(top_mask, 0.0)
+        row_sums = inv_vol.sum(axis=1).replace(0, np.nan)
+        signals = inv_vol.div(row_sums, axis=0).fillna(0.0)
+
+        # --- Monthly rebalance ---
+        warmup = max(self.momentum_period, self.quality_window, self.recovery_window) + self.skip
+        rebal_mask = pd.Series(False, index=data.index)
+        rebal_indices = list(range(warmup, len(data), self.rebal_freq))
+        rebal_mask.iloc[rebal_indices] = True
+
+        signals[~rebal_mask] = np.nan
+        signals = signals.ffill().fillna(0.0)
+        signals.iloc[:warmup] = 0.0
+
+        return signals.shift(1).fillna(0.0)