volatility

To generate historical volatility percentile signals, you apply a deterministic pipeline to OHLCV data that computes annualized standard deviation, ranks it across a lookback period, and outputs long, short, or neutral positions based on configurable low and high percentile thresholds.

Volatility mean-reversion is a strategy that assumes volatility fluctuations will return to an average level. It works by ranking historical volatility from OHLCV data over a lookback period to identify low-volatility regimes for long positions and high-volatility regimes for short or neutral positions.

Yes, you can apply this volatility strategy to crypto and futures markets. The pipeline processes any OHLCV data format across assets and markets to produce mean-reversion signals using adjustable historical volatility windows and percentile thresholds.

You configure mean-reversion signals by adjusting the hv_window for standard deviation calculation, the lookback period for percentile ranking, and low_pct and high_pct thresholds to define volatility regimes and determine long, short, or neutral position outputs.

The best way to identify volatility regimes is ranking annualized historical volatility across a configurable lookback period. This percentile-based ranking establishes deterministic thresholds that trigger long positions in low-volatility regimes and short or neutral positions in high-volatility regimes.

Historical volatility calculation requires annualization to standardize the standard deviation of returns over a specific window, allowing accurate percentile ranking and threshold-based signal comparison across different assets, markets, and timeframes.