quant-statistics

Name: quant-statistics
Availability: InStock
Author: 20YN04

Perform ADF unit-root tests and GARCH modeling on financial time-series data.

Updated Jun 30, 2026

Search Tags:#statistics #trading #econometrics #garch #quantitative #timeseries

Ready to run

npx skills add https://github.com/20YN04/vibe-trading-macos --skill quant-statistics-20yn04

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: quant-statistics
Source: https://github.com/20YN04/vibe-trading-macos/tree/main/agent/src/skills/quant-statistics
Command: npx skills add https://github.com/20YN04/vibe-trading-macos --skill quant-statistics-20yn04

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires statsmodels, arch, pandas, numpy.

What problem does it solve?

This skill addresses the complexity of validating financial time-series data and modeling market volatility, preventing common pitfalls like spurious regressions and overfitting in trading strategies.

Core Features & Use Cases

Statistical Testing: Perform ADF unit-root tests, cointegration analysis, and Granger causality tests to ensure data stationarity and identify long-run equilibrium relationships.
Volatility Modeling: Implement GARCH(1,1) and its variants to forecast market volatility and assess risk persistence.
Regression Diagnostics: Validate model integrity through heteroskedasticity, autocorrelation, and multicollinearity checks to ensure robust factor research.

Quick Start

Use the quant-statistics skill to perform an ADF unit-root test on the provided price series data to determine if it is stationary.

Frequently Asked Questions about quant-statistics

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

FAQPage Schema

How do I test financial time-series data for stationarity before building a trading strategy?▼

Cointegration analysis identifies long-run equilibrium relationships between financial time-series. This skill applies cointegration tests to ensure data stationarity and generate robust pair-trading signals while avoiding spurious regression pitfalls.

How do I model market volatility using GARCH for risk assessment?▼

Regression diagnostics validate model integrity by checking for heteroskedasticity, autocorrelation, and multicollinearity in financial data. This skill applies these diagnostics to ensure robust factor research and prevent overfitting in trading strategies.

Can I use statsmodels and arch with pandas and numpy for econometric testing?▼

Yes, you can use statsmodels and arch with pandas and numpy for econometric testing. This skill requires these dependencies to execute unit-root tests, GARCH modeling, and regression diagnostics on financial time-series data for quantitative analysis.

What is the best way to identify long-run equilibrium relationships for pair-trading?▼

The best way to identify long-run equilibrium relationships for pair-trading is through cointegration analysis and Granger causality tests. This skill performs these statistical tests to validate strategy assumptions and generate pair-trading signals.

Why does my quantitative trading strategy suffer from spurious regressions and overfitting?▼

Spurious regressions and overfitting in trading strategies occur when financial time-series data is non-stationary or model integrity is compromised. This skill prevents these pitfalls by applying rigorous statistical testing and regression diagnostics to validate assumptions.