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