volatility

Historical volatility is calculated by computing the annualized standard deviation of returns from OHLCV data. This Skill uses numpy and pandas to compute statistical metrics and generate percentile rankings for quantitative trading signals.

Volatility mean reversion is a quantitative trading strategy that identifies low-volatility regimes for long positions and high-volatility regimes for short positions. It relies on percentile rankings of historical volatility to signal entry and exit points.

Trading signals are generated by setting low and high percentile thresholds, such as 20.0 and 80.0. The Skill flags long positions when historical volatility drops below the low percentile and short positions when it exceeds the high percentile.

Yes, this Skill requires pandas and numpy to perform statistical computations on OHLCV data. These libraries handle return calculations, historical volatility annualization, and percentile-based signal generation.

Key parameters include the historical volatility window, lookback period, low and high percentile thresholds, and annualization factor. A typical setup uses a 20-day window, 120-day lookback, 20.0 low percentile, 80.0 high percentile, and 252 annualize factor.

Volatility mean reversion strategies are not suitable during sustained trending markets where volatility continuously expands without reverting. The approach relies on the assumption that historical volatility will eventually return to its historical mean.