volatility

# 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 ```bash pip install pandas numpy ``` ## Signal Convention - `1` = long (low-volatility regime), `-1` = short (high-volatility regime), `0` = stand aside