volatility-mean-reversion

Calculate historical volatility percentiles by computing annualized rolling return standard deviation, ranking values 0–100 over a lookback window, and mapping low tails to long, high tails to short, and mid-range to neutral.

Volatility mean reversion is the tendency for price fluctuation levels to return to an average. Percentile rankings detect regimes by comparing current historical volatility against a rolling lookback to signal long, short, or neutral positions.

Yes, you can generate trading signals from OHLCV cryptocurrency time series. The logic calculates historical volatility percentiles and supports crypto assets by applying 365-day annualization rather than the 252-day standard for equities.

Use pandas and numpy to compute rolling return standard deviation on an OHLCV close-price column, convert it to annualized historical volatility, and generate discrete long, short, or neutral strategy signals based on percentile thresholds.

Volatility regime detection offers an alternative to price direction for portfolio strategy by exploiting mean reversion. It identifies low and high volatility percentile tails to generate discrete signals, adapting dynamically to changing market conditions.

A limitation of using HV percentiles for trading signals is the reliance on a rolling lookback window, which delays detecting sudden regime shifts. It outputs discrete long, short, or neutral states without predicting price direction or magnitude.