volatility

Calculate annualized historical volatility and rolling percentile rank for trading signals.

Updated Jul 10, 2026

Search Tags:#finance #trading #volatility #ohlcv #quantitative #mean-reversion

Ready to run

npx skills add https://github.com/day18708433173-crypto/TradingAgents-Pro --skill volatility-day18708433173-crypto

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: volatility
Source: https://github.com/day18708433173-crypto/TradingAgents-Pro/tree/main/agent/src/skills/volatility
Command: npx skills add https://github.com/day18708433173-crypto/TradingAgents-Pro --skill volatility-day18708433173-crypto

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy.

What problem does it solve?

This skill addresses the challenge of identifying optimal entry and exit points in volatile markets by quantifying historical volatility regimes.

Core Features & Use Cases

Volatility Regime Detection: Calculates annualized historical volatility to identify low-volatility expansion phases and high-volatility contraction phases.
Percentile Ranking: Uses rolling lookback windows to rank current volatility against historical data, providing a statistical basis for trade signals.
Use Case: A trader can use this to automatically generate long signals when asset volatility is at a historical low, anticipating a breakout, or short signals when volatility is at a historical high, anticipating a mean-reverting contraction.

Quick Start

Use the volatility skill to analyze the historical volatility of the provided OHLCV data and generate trading signals based on the default 20-day window.

Frequently Asked Questions about volatility

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

FAQPage Schema

How do I calculate annualized historical volatility from OHLCV data?▼

You calculate annualized historical volatility from OHLCV data using pandas and numpy. This skill processes time-series data to compute statistical indicators and identify the current volatility regime.

What is a rolling percentile rank in mean-reversion trading?▼

A rolling percentile rank in mean-reversion trading compares current volatility against a historical lookback window. It generates long signals during low-volatility expansion phases and short signals during high-volatility contraction.

Can I generate trading signals for both stocks and cryptocurrencies?▼

Yes, you can generate trading signals for both stocks and cryptocurrencies. The skill applies quantitative mean-reversion analysis to any financial time-series OHLCV data.

How do I start generating mean-reversion signals with a 20-day window?▼

To start generating mean-reversion signals, provide your OHLCV data to the skill. It uses a default 20-day rolling window to calculate volatility percentile ranks and output trades.

Do I need pandas and numpy to process OHLCV data for quantitative analysis?▼

Yes, you need pandas and numpy to process OHLCV data for quantitative analysis. These dependencies are required to compute the statistical indicators for automated strategy execution.