volatility

You can generate trading signals from OHLCV data by computing annualized historical volatility from rolling returns, ranking it against a lookback period, and outputting long, short, or neutral signals based on percentile thresholds.

A historical volatility percentile ranking strategy for mean reversion identifies market regimes by comparing current volatility to its rolling history, triggering long positions during low volatility and exits during high volatility.

Yes, you can backtest multiple assets by applying the historical volatility mean reversion strategy across multiple symbols, generating an independent integer signal series for each asset based on its specific OHLCV close prices.

You configure percentile thresholds by setting a rolling lookback window to rank the annualized historical volatility, mapping low percentile values to a long-biased regime and high percentile values to a short or exit regime.

Yes, you need pandas and numpy because the skill relies on these libraries to process OHLCV data, calculate rolling return standard deviations, and produce the final annualized historical volatility signal series.