volatility

Name: volatility
Availability: InStock
Author: OpenSucker

Analyze historical market volatility to generate mean reversion trading signals.

15|2|Updated May 1, 2026

Search Tags:#market analysis #trading #volatility #quantitative #signal generation

Ready to run

npx skills add https://github.com/OpenSucker/OpenSucker --skill volatility-opensucker

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: volatility
Source: https://github.com/OpenSucker/OpenSucker/tree/main/skills/vibe_skills/volatility
Command: npx skills add https://github.com/OpenSucker/OpenSucker --skill volatility-opensucker

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, and includes scripts (resource) components.

What problem does it solve?

This Skill allows traders and analysts to identify market regimes by analyzing historical volatility levels, aiding in timing entries and exits to enhance profitability.

Core Features & Use Cases

Volatility Regime Detection: Calculates and ranks historical volatility to identify low or high volatility periods.
Mean Reversion Trading Signals: Generates buy or sell signals based on volatility percentile thresholds.
Use Case: An investor can detect low-volatility phases signaling potential breakout opportunities, and apply this to any OHLCV data for cryptocurrencies or stocks.

Quick Start

Use the volatility strategy to analyze your OHLCV data and generate trading signals based on historical volatility percentiles.

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 from historical volatility?▼

To generate mean reversion trading signals from historical volatility, this Skill calculates and ranks volatility percentiles on your market data to identify threshold breaches, outputting buy or sell triggers.

What is volatility regime detection and how does it apply to OHLCV data?▼

Volatility regime detection analyzes historical volatility levels on OHLCV data to identify high or low variance periods, enabling traders to time market entries and exits based on established volatility thresholds.

Can I use pandas and numpy for quantitative market analysis on cryptocurrency data?▼

Yes, you can use pandas and numpy for quantitative market analysis on cryptocurrency data, as this Skill leverages these Python libraries to calculate volatility metrics and rank trading signals across various assets.

Does this volatility analysis approach work across different assets and data intervals?▼

Yes, this volatility analysis works across different assets and data intervals, applying uniform percentile thresholds to historical market data to detect mean reversion opportunities for stocks or cryptocurrencies.

What is the best way to identify low volatility phases for potential breakout trading?▼

The best way to identify low volatility phases for potential breakout trading is by calculating historical volatility percentiles to detect regime shifts, signaling when an asset is primed for a breakout.

Do I need historical market data to calculate volatility thresholds for trading signals?▼

Yes, you need historical market data, specifically OHLCV data, to calculate volatility thresholds, as the Skill ranks historical variance to establish percentile cutoffs for generating buy or sell signals.