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.
58 lines
2.2 KiB
Python
58 lines
2.2 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 MeanReversionStrategy(StockStrategy):
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"""
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Monthly "buy the dip" with momentum confirmation.
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Among stocks with positive 12-month momentum, overweight those
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that dipped most in the past month. Rebalances monthly to keep
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turnover low. Combines long-term trend (avoid losers) with
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short-term mean reversion (buy winners on sale).
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"""
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def __init__(self, mom_lookback: int = 252, dip_window: int = 21,
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rebal_freq: int = 21, top_n: int = 20):
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self.mom_lookback = mom_lookback
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self.dip_window = dip_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, eligible: pd.DataFrame | None = None) -> pd.DataFrame:
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long_mom = data.pct_change(self.mom_lookback, fill_method=None)
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has_momentum = long_mom > 0
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short_ret = data.pct_change(self.dip_window, fill_method=None)
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# Among positive-momentum stocks, rank by biggest dip
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scores = short_ret.where(has_momentum, np.nan)
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# Rank: most negative = rank 1 (biggest dip)
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rank = mask_scores(scores, eligible).rank(axis=1, ascending=True, na_option="bottom")
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scores = mask_scores(scores, eligible)
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n_valid = scores.notna().sum(axis=1)
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effective_n = n_valid.clip(upper=self.top_n)
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top_mask = rank <= effective_n.values.reshape(-1, 1)
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top_mask = top_mask & has_momentum # ensure we only pick momentum stocks
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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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warmup = self.mom_lookback + self.dip_window
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# Only keep rebalance-day signals, forward-fill between them
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rebal_mask = pd.Series(False, index=data.index)
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rebal_indices = range(warmup, len(data), self.rebal_freq)
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rebal_mask.iloc[list(rebal_indices)] = True
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# Zero out non-rebalance days then forward-fill
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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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