detect-atr-squeeze-regime

Detect volatility-dominated squeeze regimes by comparing ATR% to a 3-year baseline.

3|1|Updated Jan 12, 2026

Search Tags:#Python #risk-management #volatility #backtest #regime-detection #ATR #squeeze

Ready to run

npx skills add https://github.com/fatfingererr/macro-skills --skill detect-atr-squeeze-regime

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: detect-atr-squeeze-regime
Source: https://github.com/fatfingererr/macro-skills/tree/main/skills/detect-atr-squeeze-regime
Command: npx skills add https://github.com/fatfingererr/macro-skills --skill detect-atr-squeeze-regime

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Detects whether an asset enters a volatility-dominated squeeze regime by comparing ATR% to a 3-year baseline.

Core Features & Use Cases

Regime classification: assigns regimes as orderly_market, elevated_volatility_trend, or volatility_dominated_squeeze based on ATR% and baseline ratio.
Actionable risk guidance: supplies recommended stop multiples, position scaling, and suitable timeframes and instruments per regime.
Single and batch analysis: supports quick checks for a single symbol, batch scanning across multiple assets, and backtests to assess performance.
Use Case: evaluate silver futures SI=F to determine if volatility is squeezing and tailor trading tactics accordingly.

Quick Start

Quick start: run a quick single-symbol check, then perform a full analysis or batch scan to identify squeeze opportunities across assets.

Frequently Asked Questions about detect-atr-squeeze-regime

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

FAQPage Schema

How do I detect volatility squeeze regimes using ATR in Python?▼

You can detect a volatility squeeze regime by calculating 14-day ATR with EMA smoothing and comparing ATR% against a 3-year rolling baseline. This classifies the asset into orderly, elevated, or volatility-dominated squeeze regimes to guide risk adjustments.

What is a volatility-dominated squeeze regime and how does ATR baseline ratio identify it?▼

A volatility-dominated squeeze regime occurs when ATR% significantly exceeds its 3-year baseline ratio. This mechanism identifies compressed volatility expansion, triggering reliability scoring and recommended stop multiples and position scaling.

Can I batch scan multiple assets for ATR squeeze regimes using yfinance data?▼

Yes, you can batch scan multiple assets for ATR squeeze regimes using yfinance data. The tool processes batch scans across symbols to classify regimes, score reliability, and output suitable timeframes and instruments per asset.

How do I adjust risk management and position sizing during an elevated volatility trend?▼

During an elevated volatility trend, adjust risk management by applying the recommended stop multiples and position scaling provided for that specific regime. The output supplies tailored risk-control guidance to optimize tactics accordingly.

Does this ATR regime detection approach work for historical backtesting?▼

Yes, this ATR regime detection approach works for historical backtesting. It assesses performance by applying regime classification and reliability scoring across historical data to determine volatility levels and validate risk adjustments.

What are the limitations of using a 3-year rolling baseline for regime detection?▼

Using a 3-year rolling baseline for regime detection limits sensitivity to shorter historical contexts. Assets without sufficient 3-year price history via yfinance cannot establish a reliable baseline, impacting regime classification and reliability scoring accuracy.