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.
40 lines
1.5 KiB
Python
40 lines
1.5 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 MomentumStrategy(StockStrategy):
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"""
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Classic cross-sectional momentum strategy (Jegadeesh & Titman, 1993).
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Ranks assets by their past (lookback - skip) day return, skipping the most
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recent `skip` days to avoid short-term mean reversion. Allocates equally
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among the top_n winners each period.
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Default parameters approximate the 12-1 month academic factor.
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"""
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def __init__(self, lookback: int = 252, skip: int = 21, top_n: int = 5):
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self.lookback = lookback # ~12 months
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self.skip = skip # ~1 month — skip to avoid reversal
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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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# Momentum: return from T-(lookback) to T-skip
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momentum = data.shift(self.skip).pct_change(self.lookback - self.skip, fill_method=None)
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momentum = momentum.where(data.notna())
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momentum = mask_scores(momentum, eligible)
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n_valid = momentum.notna().sum(axis=1)
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enough_data = n_valid >= self.top_n
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score_rank = mask_scores(momentum, eligible).rank(axis=1, ascending=False, na_option="bottom")
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top_mask = (score_rank <= self.top_n) & enough_data.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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signals.iloc[:self.lookback] = 0.0
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return signals.shift(1).fillna(0.0)
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