Generate trading signals from historical volatility, a measure of past price fluctuations. Rank its current level against recent history, signal long positions at low levels, and exit or short at high levels while retaining positions between thresholds.
Volatility strategy. Trades mean reversion based on percentile ranking of historical volatility (HV). Suitable for any OHLCV data.
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Uses percentile ranking of historical volatility (HV) to capture volatility mean reversion: build positions in low-volatility regimes while waiting for volatility expansion, and exit or short in high-volatility regimes to capture contraction.
hv_window dayslookback days (0-100)low_pct → go long (volatility is low, waiting for expansion)high_pct → exit / go short (volatility is high, waiting for contraction)returns.rolling(hv_window).std() * sqrt(252) (annualized)hv.rolling(lookback).rank(pct=True) * 100| Parameter | Default | Description |
|---|---|---|
| hv_window | 20 | Historical volatility calculation window |
| lookback | 120 | Lookback period for percentile ranking |
| low_pct | 20.0 | Low-volatility threshold (percentile) |
| high_pct | 80.0 | High-volatility threshold (percentile) |
| annualize | 252 | Annualization factor (252 for China A-shares, 365 for crypto) |
fillna)annualize should be set to 365pip install pandas numpy
1 = long (low-volatility regime), -1 = short (high-volatility regime), 0 = stand asideNo reviews yet. Tell others what you thought.
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