Gahow Wang
42218741d4
Backtesting engine supporting 11 strategies across US (S&P 500) and CN (CSI 300) markets with open-to-close execution, proportional + fixed per-trade fees. Daily trader (trader.py) with auto/morning/evening/simulate/status commands and cron-friendly `auto` mode for unattended daily runs on a server. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
67 lines
2.6 KiB
Python
67 lines
2.6 KiB
Python
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 RecoveryMomentumStrategy(Strategy):
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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) -> 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)
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# Cross-sectional percentile ranks
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rec_rank = recovery.rank(axis=1, pct=True, na_option="bottom")
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mom_rank = momentum.rank(axis=1, pct=True, na_option="bottom")
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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 = composite.rank(axis=1, ascending=False, na_option="bottom")
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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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