What are the technical requirements for this skill?

The skill requires a Python environment with pandas, numpy, yfinance, and pandas_ta installed to process the financial data.

Which assets can this skill analyze?

It can analyze any asset ticker supported by Yahoo Finance, including equities, commodities (futures), forex pairs, and cryptocurrencies.

Does this skill provide buy or sell signals?

No, it is a risk management and regime detection tool designed to evaluate the reliability of your existing strategy's technical levels and suggest protective adjustments.

How does the skill calculate market volatility?

It uses a 14-day Exponential Moving Average (EMA) of the True Range, which is then normalized as a percentage of the current price to allow for cross-asset comparison.

What is a 'volatility-dominated squeeze' regime?

It is a market state where ATR% is significantly above its 3-year average (usually >2x), causing technical levels to fail as price movement is driven by forced flows like liquidations.

Market ATR Squeeze Detector

Name: Market ATR Squeeze Detector
Author: fatfingererr

byfatfingererr

Ciencia de Datos y ML

Analyzes asset volatility using normalized ATR% to identify high-risk 'squeeze' regimes and provide actionable risk management adjustments.

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This skill empowers Claude to perform macro-level volatility analysis by calculating 14-day EMA ATR normalized as a percentage of price. It compares current volatility against a 3-year rolling mean to classify market regimes into orderly, elevated, or squeeze states. When a squeeze is detected—where technical levels often fail due to reflexive flows like margin calls or gamma hedging—the skill provides specific guidance on adjusting stop-loss multipliers, scaling down positions, and switching timeframes to navigate turbulent markets safely.

Características Principales

Three-regime market classification (Orderly, Elevated, Squeeze)
Dynamic risk adjustments including stop-loss and position sizing
ATR% normalization for cross-asset volatility comparison
Quantitative reliability scoring for technical analysis levels
Automated visualization dashboard generation for market trends
0 GitHub stars

Casos de Uso

Dynamically adjusting position sizes based on historical volatility baselines
Filtering out false technical breakouts during high-volatility 'forced flows'
Scanning multi-asset portfolios to detect systemic volatility spikes

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Características Principales

Three-regime market classification (Orderly, Elevated, Squeeze)
Dynamic risk adjustments including stop-loss and position sizing
ATR% normalization for cross-asset volatility comparison
Quantitative reliability scoring for technical analysis levels
Automated visualization dashboard generation for market trends
0 GitHub stars

Casos de Uso

Dynamically adjusting position sizes based on historical volatility baselines
Filtering out false technical breakouts during high-volatility 'forced flows'
Scanning multi-asset portfolios to detect systemic volatility spikes