Gahow Wang
722cade974
Share point-in-time signals and a cash-funded order ledger across backtests and trading. Correct eligibility, missing-price fills, quality factors, regime inputs, warmup windows, and cash-injection accounting. Add reproducible registered-strategy comparisons and paired forward candidates with a 20% resize band. Validation: 125 Python tests, 23 web tests, TypeScript/Vite build, 87 strategy causality checks, and real-data target/order/equity parity for 10 strategies.
72 lines
3.1 KiB
Python
72 lines
3.1 KiB
Python
import numpy as np
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import pandas as pd
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from strategies.base import StockStrategy, mask_scores
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class RecoveryMomentumStrategy(StockStrategy):
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"""
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Recovery + momentum composite factor strategy.
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Combines the two strongest predictive factors (by IC analysis):
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1. Recovery: price relative to 63-day low (how far stock has bounced
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from its recent trough). Higher = stronger recovery trajectory.
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2. Long-term momentum: 12-1 month return. Avoids most recent month
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to sidestep short-term reversal noise.
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Rank-combines both factors (50/50), selects top_n, equal-weights.
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Rebalances monthly to control turnover.
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The recovery factor captures stocks in strong V-shaped rebounds —
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a pattern that tends to persist. Combined with momentum, it filters
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for stocks with both long-term strength AND recent acceleration.
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"""
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def __init__(self, recovery_window: int = 63, mom_lookback: int = 252,
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mom_skip: int = 21, rebal_freq: int = 21, top_n: int = 10):
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self.recovery_window = recovery_window
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self.mom_lookback = mom_lookback
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self.mom_skip = mom_skip
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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, eligible: pd.DataFrame | None = None) -> pd.DataFrame:
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# Factor 1: Recovery — price / rolling min
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recovery = data / data.rolling(self.recovery_window).min() - 1
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# Factor 2: 12-1 month momentum
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momentum = data.shift(self.mom_skip).pct_change(self.mom_lookback - self.mom_skip, fill_method=None)
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# Cross-sectional percentile ranks. na_option="keep" is critical:
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# with "bottom" + ascending=True default, NaN stocks get pct=1.0, so
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# non-member / delisted / pre-IPO names leak into the composite as
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# "top" candidates. "keep" propagates NaN and the final ascending=False
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# + "bottom" rank pushes them to the end where they are not selected.
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rec_rank = mask_scores(recovery, eligible).rank(axis=1, pct=True, na_option="keep")
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mom_rank = mask_scores(momentum, eligible).rank(axis=1, pct=True, na_option="keep")
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# Composite score (50/50)
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composite = 0.5 * rec_rank + 0.5 * mom_rank
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# Select top_n
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rank = mask_scores(composite, eligible).rank(axis=1, ascending=False, na_option="bottom")
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composite = mask_scores(composite, eligible)
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n_valid = composite.notna().sum(axis=1)
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enough = n_valid >= self.top_n
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top_mask = (rank <= self.top_n) & enough.values.reshape(-1, 1)
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raw = top_mask.astype(float)
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row_sums = raw.sum(axis=1).replace(0, np.nan)
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signals = raw.div(row_sums, axis=0).fillna(0.0)
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# Monthly rebalance: keep only rebal-day signals, forward-fill
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warmup = max(self.mom_lookback, self.recovery_window)
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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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