Gahow Wang
d086930ab3
Add 12 strategy modules including adaptive blend, composite alpha, cross-asset momentum, ensemble alpha, trend rider v5/v6, and more.
95 lines
3.7 KiB
Python
95 lines
3.7 KiB
Python
"""
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Improved Momentum Quality Strategy.
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Improvements over base MomentumQualityStrategy:
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1. Monthly rebalancing (original rebalances daily → high turnover)
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2. Added recovery factor (strong predictor per IC analysis)
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3. Replaced expensive .apply() consistency calc with vectorized version
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4. Inverse-vol weighting instead of equal-weight
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5. NaN handling fixed throughout
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"""
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import numpy as np
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import pandas as pd
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from strategies.base import Strategy
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class ImprovedMomentumQualityStrategy(Strategy):
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"""
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Momentum + quality + recovery with monthly rebal and inv-vol weighting.
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"""
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def __init__(
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self,
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momentum_period: int = 252,
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skip: int = 21,
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quality_window: int = 252,
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recovery_window: int = 63,
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vol_window: int = 60,
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rebal_freq: int = 21,
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top_n: int = 20,
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):
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self.momentum_period = momentum_period
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self.skip = skip
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self.quality_window = quality_window
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self.recovery_window = recovery_window
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self.vol_window = vol_window
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self.rebal_freq = rebal_freq
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self.top_n = top_n
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def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame:
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# --- Momentum factor ---
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momentum = data.shift(self.skip).pct_change(self.momentum_period - self.skip)
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# --- Quality: return consistency (vectorized) ---
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# Fraction of positive 21-day returns over rolling window
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monthly_ret = data.pct_change(21)
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positive_indicator = (monthly_ret > 0).astype(float)
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consistency = positive_indicator.rolling(
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self.quality_window, min_periods=self.quality_window // 2
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).mean()
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# --- Quality: inverse max drawdown ---
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rolling_max = data.rolling(self.quality_window, min_periods=self.quality_window // 2).max()
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drawdown = data / rolling_max - 1
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worst_dd = drawdown.rolling(self.quality_window, min_periods=self.quality_window // 2).min()
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inv_dd = -worst_dd # higher = smaller drawdown = better
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# --- Recovery factor ---
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recovery = data / data.rolling(self.recovery_window, min_periods=self.recovery_window).min() - 1
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# --- Cross-sectional ranking ---
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mom_rank = momentum.rank(axis=1, pct=True, na_option="keep")
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con_rank = consistency.rank(axis=1, pct=True, na_option="keep")
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dd_rank = inv_dd.rank(axis=1, pct=True, na_option="keep")
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rec_rank = recovery.rank(axis=1, pct=True, na_option="keep")
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# Composite: momentum 35%, recovery 25%, consistency 20%, drawdown 20%
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scores = (0.35 * mom_rank + 0.25 * rec_rank +
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0.20 * con_rank + 0.20 * dd_rank)
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# --- Select top_n ---
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n_valid = scores.notna().sum(axis=1)
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enough = n_valid >= self.top_n
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score_rank = scores.rank(axis=1, ascending=False, na_option="bottom")
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top_mask = (score_rank <= self.top_n) & enough.values.reshape(-1, 1)
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# --- Inverse-vol weighting ---
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returns = data.pct_change()
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vol = returns.rolling(self.vol_window, min_periods=30).std().replace(0, np.nan)
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inv_vol = (1.0 / vol).where(top_mask, 0.0)
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row_sums = inv_vol.sum(axis=1).replace(0, np.nan)
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signals = inv_vol.div(row_sums, axis=0).fillna(0.0)
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# --- Monthly rebalance ---
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warmup = max(self.momentum_period, self.quality_window, self.recovery_window) + self.skip
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rebal_mask = pd.Series(False, index=data.index)
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rebal_indices = list(range(warmup, len(data), self.rebal_freq))
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rebal_mask.iloc[rebal_indices] = True
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signals[~rebal_mask] = np.nan
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signals = signals.ffill().fillna(0.0)
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signals.iloc[:warmup] = 0.0
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return signals.shift(1).fillna(0.0)
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