the-volatility-analyst

Analyze volatility regimes and surface metrics to forecast breakout probabilities.

13|3|Updated Mar 31, 2026

Search Tags:#forecast #volatility #atr #hurst #vol-regime #bollinger #surfaces

Ready to run

npx skills add https://github.com/cubexch/ai-fund --skill the-volatility-analyst

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: the-volatility-analyst
Source: https://github.com/cubexch/ai-fund/tree/main/skills/volatility-analyst
Command: npx skills add https://github.com/cubexch/ai-fund --skill the-volatility-analyst

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps traders understand volatility regimes and analyze vol surfaces to forecast breakout probabilities, enabling risk management and timely tactical decisions.

Core Features & Use Cases

Regime classification (low / normal / high / extreme) across multiple windows
Vol surface analysis and cross-asset comparisons
Bollinger squeeze detection and breakout probability estimation
ATR and Hurst exponent calculations for regime characterization
Vol-of-vol and cross-asset context for diversification and hedging
Self-review and performance tracking of vol-based signals

Quick Start

Ask for a near-term volatility analysis across multiple windows to determine the current vol regime.

Frequently Asked Questions about the-volatility-analyst

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

FAQPage Schema

How do I forecast volatility regimes and anticipate market breakouts?▼

Forecast volatility regimes by calculating realized volatility across multiple windows and classifying the market into low, normal, high, or extreme states to estimate breakout probabilities for risk management.

What is a Bollinger squeeze and how does it predict breakout probability?▼

A Bollinger squeeze indicates compressed price bands signaling low volatility. Detecting this squeeze alongside regime classification helps estimate the probability of an imminent directional breakout without predicting price direction.

How do I use the Hurst exponent and ATR for volatility regime characterization?▼

Calculate the Hurst exponent to identify trending versus mean-reverting behavior and ATR to gauge average price range, combining both metrics to accurately characterize the current volatility regime across assets.

Can I perform cross-asset volatility surface analysis across different timeframes?▼

Yes, you can perform cross-asset volatility surface analysis across multiple timeframes. This compares vol-of-vol and regime states between assets to inform diversification strategies and tactical hedging decisions.

Does this volatility analysis predict price direction for trading signals?▼

No, this volatility analysis explicitly does not predict price direction. It focuses on forecasting breakout probabilities and volatility regime shifts to inform risk management, hedging, and tactical trading decisions.

What is the best way to analyze near-term volatility before entering a trade?▼

Analyze near-term volatility by requesting a multi-window review of 7d, 14d, 30d, and 90d realized volatility to determine the current regime and assess potential risk exposure before executing trades.