volatility

name	volatility
description	Volatility strategy. Trades mean reversion based on percentile ranking of historical volatility (HV). Suitable for any OHLCV data.
category	strategy

Volatility Strategy

Purpose

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.

Signal Logic

1. Compute HV: annualized standard deviation of returns over the past hv_window days
2. Percentile ranking: percentile position of HV within the past lookback days (0-100)
3. Signal generation:
   • Percentile < low_pct → go long (volatility is low, waiting for expansion)
   • Percentile > high_pct → exit / go short (volatility is high, waiting for contraction)
   • Middle region → keep the current position

Key Implementation Details

• HV = returns.rolling(hv_window).std() * sqrt(252) (annualized)
• Percentile = hv.rolling(lookback).rank(pct=True) * 100
• For cryptocurrencies, use 365 instead of 252 as the annualization factor

Parameters

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)

Common Pitfalls

• Before the lookback window is filled, there is not enough data to compute percentiles, so the signal should be 0 (fillna)
• Volatility is not direction. Going long in low-volatility regimes does not guarantee price appreciation; it only means volatility expansion is statistically more likely
• Cryptocurrencies trade 7x24, so annualize should be set to 365

Dependencies

pip install pandas numpy

Signal Convention

• 1 = long (low-volatility regime), -1 = short (high-volatility regime), 0 = stand aside