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/enhanced_recovery_momentum.py

@@ -0,0 +1,99 @@
+"""
+Enhanced Recovery Momentum Strategy.
+
+Improvements over base RecoveryMomentumStrategy:
+1. Inverse-volatility weighting (allocate more to lower-vol winners → better risk-adjusted)
+2. Monthly rebalancing (controls turnover)
+3. Momentum filter gate: only pick stocks with positive 6-month momentum
+   (avoids "dead cat bounces" — recovery without underlying trend)
+4. Volatility regime scaling: reduce exposure when market vol is elevated
+"""
+
+import numpy as np
+import pandas as pd
+from strategies.base import Strategy
+
+
+class EnhancedRecoveryMomentumStrategy(Strategy):
+    """
+    Recovery + momentum with inverse-vol weighting and regime awareness.
+    """
+
+    def __init__(
+        self,
+        recovery_window: int = 63,
+        mom_lookback: int = 252,
+        mom_skip: int = 21,
+        intermediate_mom: int = 126,
+        vol_window: int = 60,
+        rebal_freq: int = 21,
+        top_n: int = 20,
+        regime_scale: bool = True,
+    ):
+        self.recovery_window = recovery_window
+        self.mom_lookback = mom_lookback
+        self.mom_skip = mom_skip
+        self.intermediate_mom = intermediate_mom
+        self.vol_window = vol_window
+        self.rebal_freq = rebal_freq
+        self.top_n = top_n
+        self.regime_scale = regime_scale
+
+    def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame:
+        # Factor 1: Recovery — price / rolling min
+        recovery = data / data.rolling(self.recovery_window, min_periods=self.recovery_window).min() - 1
+
+        # Factor 2: 12-1 month momentum
+        momentum = data.shift(self.mom_skip).pct_change(self.mom_lookback - self.mom_skip)
+
+        # Factor 3: Intermediate momentum (6m) as a filter gate
+        # Only consider stocks with positive intermediate trend
+        intermediate = data.shift(self.mom_skip).pct_change(self.intermediate_mom - self.mom_skip)
+        trend_gate = intermediate > 0
+
+        # Cross-sectional percentile ranks
+        rec_rank = recovery.rank(axis=1, pct=True, na_option="keep")
+        mom_rank = momentum.rank(axis=1, pct=True, na_option="keep")
+
+        # Composite score (50/50), gated by intermediate trend
+        composite = 0.5 * rec_rank + 0.5 * mom_rank
+        composite = composite.where(trend_gate, np.nan)
+
+        # Select top_n
+        rank = composite.rank(axis=1, ascending=False, na_option="bottom")
+        n_valid = composite.notna().sum(axis=1)
+        enough = n_valid >= self.top_n
+        top_mask = (rank <= self.top_n) & enough.values.reshape(-1, 1)
+
+        # Inverse-vol weighting among selected stocks
+        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)
+
+        # Regime scaling: reduce exposure when market vol is high
+        if self.regime_scale:
+            market_vol = vol.mean(axis=1)
+            vol_median = market_vol.rolling(252, min_periods=126).median()
+            vol_ratio = (vol_median / market_vol).clip(0.5, 1.2)
+            signals = signals.mul(vol_ratio, axis=0)
+            # Re-normalize: cap at 1.0
+            row_totals = signals.sum(axis=1)
+            overflow = row_totals > 1.0
+            signals.loc[overflow] = signals.loc[overflow].div(
+                row_totals[overflow], axis=0
+            )
+
+        # Monthly rebalance: keep only rebal-day signals, forward-fill
+        warmup = max(self.mom_lookback, self.recovery_window, self.vol_window + 252)
+        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)