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.
33 lines
1.2 KiB
Python
33 lines
1.2 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 InverseVolatilityStrategy(StockStrategy):
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"""
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Risk parity via inverse-volatility weighting.
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Allocates capital inversely proportional to each asset's realized volatility
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over a rolling window. Assets with lower recent volatility receive larger
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allocations, equalizing the risk contribution of each position.
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This is a simple but robust baseline for risk-based portfolio construction.
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"""
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def __init__(self, vol_window: int = 20):
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self.vol_window = vol_window
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def generate_signals(self, data: pd.DataFrame, eligible: pd.DataFrame | None = None) -> pd.DataFrame:
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returns = data.pct_change(fill_method=None)
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vol = returns.rolling(self.vol_window).std()
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# Replace zero vol with NaN to avoid division by zero
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vol = vol.replace(0, np.nan)
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inv_vol = mask_scores((1.0 / vol).where(data.notna()), eligible)
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row_sums = inv_vol.sum(axis=1).replace(0, np.nan)
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signals = inv_vol.div(row_sums, axis=0).fillna(0.0)
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signals.iloc[:self.vol_window] = 0.0
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return signals.shift(1).fillna(0.0)
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