Gahow Wang
149a00c458
- main.py: add IBKR-style tiered fee schedule (fee_base + fee_per_share), PIT universe support, and open-to-close execution improvements - metrics.py: add raw_summary helper for JSON-safe metric export - Misc strategy fixes: deprecation warnings, NaN handling Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
56 lines
2.2 KiB
Python
56 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 Strategy
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class DualMomentumStrategy(Strategy):
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"""
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Dual momentum with multi-timeframe confirmation.
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Unlike plain momentum (single 12-1m lookback), this requires agreement
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across THREE timeframes — short (1-3m), medium (3-6m), and long (6-12m).
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A stock must show positive returns on ALL three to qualify.
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This filters out stocks riding a single spike and favors sustained trends.
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Position count is variable: when few stocks pass all three filters,
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the strategy naturally goes to partial cash.
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"""
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def __init__(self, top_n: int = 20):
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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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# Three timeframe returns (each skipping most recent 5 days)
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skip = 5
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short_mom = data.shift(skip).pct_change(63 - skip) # ~3 month
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med_mom = data.shift(skip).pct_change(126 - skip) # ~6 month
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long_mom = data.shift(skip).pct_change(252 - skip) # ~12 month
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# All three must be positive (absolute momentum across timeframes)
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all_positive = (short_mom > 0) & (med_mom > 0) & (long_mom > 0)
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# Composite score: average percentile rank across timeframes
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short_rank = short_mom.rank(axis=1, pct=True, na_option="keep")
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med_rank = med_mom.rank(axis=1, pct=True, na_option="keep")
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long_rank = long_mom.rank(axis=1, pct=True, na_option="keep")
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composite = (short_rank + med_rank + long_rank) / 3
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# Only consider stocks passing absolute filter
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composite_filtered = composite.where(all_positive, np.nan)
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n_valid = composite_filtered.notna().sum(axis=1)
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enough = n_valid >= 1
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rank = composite_filtered.rank(axis=1, ascending=False, na_option="bottom")
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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)) & enough.values.reshape(-1, 1)
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top_mask = top_mask & all_positive
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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[:252] = 0.0
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return signals.shift(1).fillna(0.0)
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