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>
33 lines
1.0 KiB
Python
33 lines
1.0 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 InverseVolatilityStrategy(Strategy):
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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) -> pd.DataFrame:
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returns = data.pct_change()
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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 = 1.0 / vol
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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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