volatility

Generate integer trading signals from historical volatility percentile rankings.

Updated Apr 10, 2026

Search Tags:#python #pandas #trading #volatility #signal #mean-reversion

Ready to run

npx skills add https://github.com/ebrahim-sani/trading-automation --skill volatility-ebrahim-sani

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: volatility
Source: https://github.com/ebrahim-sani/trading-automation/tree/main/vibe-trading/agent/src/skills/volatility
Command: npx skills add https://github.com/ebrahim-sani/trading-automation --skill volatility-ebrahim-sani

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Traders need a systematic way to exploit volatility mean‑reversion, identifying low‑volatility regimes to go long and high‑volatility regimes to short, without relying on price direction alone.

Core Features & Use Cases

Compute historical volatility (HV) and rank it percentiles over a lookback window.
Generate long, short, or neutral signals based on configurable low/high percentile thresholds.
Adjustable parameters for window lengths, lookback periods, and annualization factor for equities vs crypto.

Quick Start

Ask the volatility skill to generate buy and sell signals for BTC‑USDT using the default 20‑day HV window and 120‑day lookback.

Frequently Asked Questions about volatility

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

FAQPage Schema

How do I generate mean-reversion trading signals using historical volatility percentiles?▼

Mean-reversion trading signals are generated by computing rolling historical volatility over a configurable window, ranking it percentiles over a lookback period, and outputting integer signals (1, -1, 0) to trigger long, short, or neutral positions.

Can I use pandas and numpy to compute volatility signals for cryptocurrency OHLCV data?▼

Yes, pandas and numpy compute rolling volatility and percentile rankings for cryptocurrency OHLCV data, allowing you to apply adjustable annualization factors and window lengths to generate trading signals for assets like BTC-USDT.

What's the best way to identify low and high volatility regimes for long and short positions?▼

The best way to identify volatility regimes is ranking historical volatility into percentiles over a 120-day lookback, using configurable low and high percentile thresholds to systematically trigger long positions in low-volatility regimes and short positions in high-volatility regimes.

How do I configure annualization factors and window lengths for stock and futures volatility?▼

You configure annualization factors and window lengths by adjusting the parameters for the rolling historical volatility calculation, applying distinct annualization settings for equities versus crypto to accurately percentile-rank OHLCV time series.

Why does my volatility percentile signal output return 0 instead of 1 or -1?▼

A volatility percentile signal returns 0 when current historical volatility falls between your configured low and high percentile thresholds, indicating a neutral market regime where no mean-reversion long or short position is triggered.

Does the volatility signal approach work without relying on price direction alone?▼

Yes, the volatility signal approach works without relying on price direction alone by exploiting volatility mean-reversion, systematically identifying low-volatility regimes to go long and high-volatility regimes to short based solely on historical volatility percentiles.