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volatility

Identifies buy/sell/hold signals using historical volatility percentile ranking.

1|Updated Jun 23, 2026
Search Tags:#market analysis#financial analysis#trading strategies#mean reversion#volatility trading
One-click install
npx skills add https://github.com/644408071-design/Kokpop --skill volatility-644408071-design
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: volatility
Source: https://github.com/644408071-design/Kokpop/tree/main/agent/src/skills/volatility
Command: npx skills add https://github.com/644408071-design/Kokpop --skill volatility-644408071-design

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill identifies opportunities in trading based on volatility mean reversion strategies, helping traders make informed decisions in volatile markets.

Core Features & Use Cases

  • Volatility Mean Reversion: Trades are based on historical volatility percentile ranking, targeting mean reversion in low and high volatility regimes.
  • Signal Generation: Provides buy, sell, or hold signals based on volatility percentile positions.
  • Use Case: Traders can use this Skill to manage their portfolios by going long in low-volatility regimes and exiting or shorting in high-volatility regimes to capture market movements.

Quick Start

Activate the volatility skill and specify the asset to trade, e.g., 'volatility trade BTC-USDT'.

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 based on volatility mean reversion?▼

Volatility mean reversion strategies generate buy, sell, or hold signals by ranking historical volatility percentiles. You can identify trading opportunities by going long in low-volatility regimes and exiting or shorting in high-volatility regimes.

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

Historical volatility percentile ranking measures current volatility against its historical range to identify mean reversion opportunities. It targets extremes in volatility regimes, generating actionable signals for financial trading workflows.

Can I use volatility trading analysis with OHLCV data in pandas?▼

Yes, volatility trading analysis requires OHLCV data and uses pandas and numpy for data manipulation and statistical calculations. You can process historical price data to compute volatility percentiles.

How do I start trading BTC-USDT using volatility percentile signals?▼

Activate the volatility skill and specify your asset, such as 'volatility trade BTC-USDT'. It will analyze the OHLCV data and output buy, sell, or hold signals based on the asset's historical volatility percentile ranking.

Does volatility mean reversion work for short-term trading strategies?▼

Volatility mean reversion targets transitions between low and high volatility regimes. It is applicable to financial trading workflows involving OHLCV data where capturing market movements requires entering or exiting based on volatility extremes.

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