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>
38 lines
1.4 KiB
Python
38 lines
1.4 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 MomentumStrategy(Strategy):
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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) -> 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)
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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 = momentum.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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