volatility

You can generate volatility mean-reversion trading signals by feeding OHLCV data into a signal engine that computes rolling historical volatility, ranks it by percentile over a lookback window, and outputs long, short, or neutral positions based on configurable thresholds.

Historical volatility percentile ranking compares current rolling HV against past values over a lookback window. Low percentiles signal quiet regimes suitable for long entries, while high percentiles indicate volatile regimes for exits or shorting.

Yes, volatility mean-reversion signals apply to crypto assets using daily or higher-frequency OHLCV data. For cryptocurrencies, historical volatility annualization uses a factor of 365 to account for continuous trading.

You configure trading regime thresholds by setting percentile cutoffs in the signal engine. Low percentile thresholds trigger long positions in low-volatility regimes, while high percentile thresholds trigger short positions in high-volatility regimes.

No additional dependencies are required. The implementation supports Python Pandas-based computation for historical volatility calculation and includes a deterministic signal engine to generate trading outputs.

Volatility mean-reversion works for any asset class with OHLCV data, including stocks and crypto. The historical volatility annualization factor can be adjusted to suit the specific market being analyzed.