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quant-statistics

Perform stationarity, cointegration, and GARCH volatility tests on financial data.

15|2|Updated May 1, 2026
Search Tags:#statistics#regression#time-series#bootstrap#volatility#cointegration
One-click install
npx skills add https://github.com/OpenSucker/OpenSucker --skill quant-statistics-opensucker
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quant-statistics
Source: https://github.com/OpenSucker/OpenSucker/tree/main/skills/vibe_skills/quant-statistics
Command: npx skills add https://github.com/OpenSucker/OpenSucker --skill quant-statistics-opensucker

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides quantitative researchers and traders with advanced statistical methods to analyze financial data, ensuring robust strategy development and risk assessment.

Core Features & Use Cases

  • Time-Series Testing: performs stationarity and cointegration tests to validate assumptions in trading models.
  • Volatility Modeling: fits GARCH models to estimate and forecast market volatility, aiding in risk management.
  • Regression Diagnostics: evaluates model residuals for heteroskedasticity and autocorrelation, improving model reliability.
  • Hypothesis Testing & Bootstrap: applies significance tests and bootstrap techniques to confirm the robustness of findings and strategies.
  • Use Case: For example, testing whether two stock prices are cointegrated or modeling volatility for options pricing.

Quick Start

Analyze a series of returns by fitting a GARCH model and performing stationarity tests to inform your trading signals.

Frequently Asked Questions about quant-statistics

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

FAQPage Schema
How do I test whether two stock prices are cointegrated for a trading model?▼

Stationarity testing determines if a time series has constant statistical properties over time by checking for unit roots. This Skill performs stationarity tests to validate assumptions and ensure your financial time-series modeling remains reliable.

How do I fit a GARCH model to estimate and forecast market volatility?▼

Volatility modeling fits GARCH models to financial return series to estimate and forecast market variance. This Skill uses the arch library to model volatility dynamics, aiding in options pricing and risk management.

How do I check regression residuals for heteroskedasticity and autocorrelation?▼

Regression diagnostics evaluate model residuals for heteroskedasticity and autocorrelation to detect violated assumptions. This Skill provides diagnostic tools to assess residual behavior, improving the reliability of your quantitative models.

Can I use bootstrap techniques to validate trading strategy robustness?▼

This Skill requires Python libraries statsmodels, arch, pandas, and numpy installed in your environment to function. It provides advanced statistical methods designed for quantitative researchers and traders analyzing financial data.

What is the best way to apply statistical testing for financial data analysis?▼

Python libraries statsmodels and arch provide the statistical testing and volatility modeling capabilities for financial data analysis. This Skill integrates these frameworks to deliver stationarity tests, cointegration analysis, and GARCH modeling.

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