volatility

Name: volatility
Availability: InStock
Author: hxhyyy

Calculate historical volatility and percentile rankings for OHLCV trading signals.

Updated Jul 8, 2026

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

Ready to run

npx skills add https://github.com/hxhyyy/Vibe-Trading --skill volatility-hxhyyy

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy.

What problem does it solve?

This skill addresses the difficulty of identifying optimal entry and exit points in volatile markets by automating the calculation of historical volatility and generating actionable trading signals.

Core Features & Use Cases

Volatility Analysis: Computes annualized historical volatility using rolling standard deviation of returns.
Percentile Ranking: Ranks current volatility against historical lookback periods to identify low-volatility (mean reversion) and high-volatility (contraction) regimes.
Use Case: A trader can use this to automatically generate long signals when asset volatility is at a historical low, anticipating a price expansion, or short signals when volatility is at a historical high.

Quick Start

Use the volatility skill to generate trading signals for the BTC-USDT pair using a 20-day window and 120-day lookback period.

Frequently Asked Questions about volatility

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

FAQPage Schema

How do I calculate historical volatility for mean reversion trading signals?▼

Calculate historical volatility using the rolling standard deviation of returns to identify mean reversion trading signals. This method ranks current volatility against historical lookback periods to pinpoint optimal long or short entry points.

What is volatility percentile ranking and how does it identify trading opportunities?▼

Volatility percentile ranking compares current volatility against a historical lookback period to identify market regimes. It generates long signals during low-volatility periods anticipating price expansion, and short signals during high-volatility periods.

Can I use pandas and numpy to automate volatility analysis on OHLCV datasets?▼

Yes, you can use pandas and numpy to automate volatility analysis on OHLCV datasets. These libraries handle rolling statistical computations and signal generation for crypto and equity markets to determine entry points.

Does quantitative analysis for crypto markets use the same volatility signals as equities?▼

Quantitative analysis for crypto markets applies the same volatility signals as equities. The historical volatility calculations and percentile rankings work across both asset classes using standard OHLCV financial datasets to generate trading signals.

What lookback window should I use for calculating annualized volatility?▼

Use a 20-day window for calculating rolling volatility and a 120-day lookback period for percentile rankings. These parameters identify low-volatility mean reversion regimes and high-volatility contraction periods to generate actionable trading signals.