volatility

Generate long, short, or neutral signals from historical volatility percentile rankings.

Updated May 15, 2026

Search Tags:#volatility #ohlcv #mean-reversion #percentile #trading-signal #hv

Ready to run

npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill volatility-philipcoller-777

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: volatility
Source: https://github.com/philipcoller-777/Vibe-Trading-TV2/tree/main/agent/src/skills/volatility
Command: npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill volatility-philipcoller-777

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Volatility strategy helps traders capture mean-reversion by using historical volatility percentile rankings to time entries and exits across assets and timeframes.

Core Features & Use Cases

HV calculation over a configurable window to measure volatility.
Percentile ranking of HV within a lookback period to identify low- and high-volatility regimes.
Signal generation for long/short/neutral positions across OHLCV data with per-asset thresholds.

Quick Start

Feed OHLCV data into the engine and generate signals to capture mean-reversion opportunities.

Frequently Asked Questions about volatility

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate trading signals from historical volatility percentile rankings?▼

To generate trading signals from historical volatility percentile rankings, feed OHLCV data into the engine to compute HV over a configurable window and rank it within a lookback period, producing long, short, or neutral positions based on mean-reversion regimes.

What is historical volatility percentile and how does it exploit mean-reversion?▼

Historical volatility percentile ranks current HV against a lookback period to identify low- and high-volatility regimes, generating mean-reversion signals that time entries and exits when volatility extremes are likely to revert.

Can I apply historical volatility signals across multiple assets using OHLCV data?▼

Yes, you can apply historical volatility signals across multiple assets by feeding OHLCV data into the engine, which calculates HV and generates per-asset signals using configurable thresholds for low and high volatility regimes.

What parameters do I need to configure for historical volatility percentile signal generation?▼

You need to configure hv_window for HV calculation, lookback for percentile ranking, low_pct and high_pct for regime thresholds, and annualize to adjust volatility scaling when generating trading signals from OHLCV data.

How do I set up OHLCV data for historical volatility calculation and mean-reversion signals?▼

Provide OHLCV data as input to the engine, then set the hv_window and lookback parameters to calculate historical volatility and rank its percentile, which outputs mean-reversion trading signals across your selected assets.

What are the limitations of using historical volatility percentile for trading signals?▼

Historical volatility percentile signals rely on mean-reversion regimes and may not perform well during trending markets, as the strategy assumes volatility extremes will revert rather than persist across the configured lookback window.