Compute annualized historical volatility and rank it within a lookback window to generate position signals.
npx skills add https://github.com/GGwujun/SigmX --skill volatility-ggwujun
Please help me install this Agent Skill. Skill: volatility Source: https://github.com/GGwujun/SigmX/tree/main/agent/src/skills/volatility Command: npx skills add https://github.com/GGwujun/SigmX --skill volatility-ggwujun
Volatility regime detection and automated trading signals to exploit mean-reversion, helping traders enter during low-volatility regimes and exit or short during high-volatility regimes.
Command an AI assistant to run the Volatility Strategy on your OHLCV dataset with hv_window=20, lookback=120, low_pct=20, high_pct=80, and annualize=252.
High-intent search queries and answers about installing and using this skill.
Mean-reversion trading signals are generated by computing annualized historical volatility, ranking it within a lookback window, and outputting long, short, or neutral positions based on low and high percentile thresholds.
Historical volatility signals work directly on cryptocurrency OHLCV data, as well as equities and futures, applying the same volatility regime ranking logic across different asset classes.
Automating volatility regime detection involves applying an annualized historical volatility calculation with a set lookback window and percentile thresholds to multiple OHLCV datasets, producing actionable entry and exit signals.
Historical volatility parameters for a backtesting framework are configured using hv_window, lookback, low_pct, high_pct, and annualize settings to calibrate volatility ranking and signal generation.
This volatility strategy operates with no specific dependencies required, processing OHLCV data directly to produce mean-reversion signals for integration into existing backtesting or live trading systems.
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