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quant-statistics

Run ADF, cointegration, and Granger causality tests on financial time series.

2|Updated May 13, 2026
Search Tags:#regression#financial analysis#time-series#statistical modeling#volatility
One-click install
npx skills add https://github.com/thanhtai040805/AI_Invest --skill quant-statistics-thanhtai040805
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quant-statistics
Source: https://github.com/thanhtai040805/AI_Invest/tree/main/ai-engine/app/domain/services/quant/skills_data/quant-statistics
Command: npx skills add https://github.com/thanhtai040805/AI_Invest --skill quant-statistics-thanhtai040805

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a comprehensive toolkit for quantitative analysis in financial data, allowing users to perform a variety of statistical tests and modeling.

Core Features & Use Cases

  • Statistical Tests: ADF unit-root test, cointegration test, Granger causality test, etc.
  • Volatility Modeling: GARCH volatility modeling and variants.
  • Regression Diagnostics: Heteroskedasticity and autocorrelation tests, multicollinearity detection.
  • Bootstrap Methods: Nonparametric bootstrap for statistical inference.
  • Hypothesis Testing Framework: A quick reference for common statistical tests and considerations for multiple testing problems.
  • Use Case: Utilize this Skill to assess stationarity of financial series, detect cointegration relationships, and model volatility in return series for risk management.

Quick Start

Execute the ADF test on the series 'stock_returns' using the 'adf_test' script.

Frequently Asked Questions about quant-statistics

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

FAQPage Schema
How do I run a GARCH volatility model on a financial time series?▼

To model volatility in return series, this Skill applies GARCH variants using statsmodels for statistical computations and model fitting on financial data. It is designed for risk management and strategy development.

What statistical tests are used to detect cointegration in financial data?▼

Cointegration detection in financial data relies on statistical tests provided by this Skill to identify long-term relationships between time series. It uses statsmodels to perform cointegration analysis for quantitative research.

How do I check for stationarity in time-series data using Python?▼

To assess stationarity of financial series, this Skill employs the ADF unit-root test using the 'adf_test' script. It leverages pandas and statsmodels to execute the test and evaluate the time-series data properties.

Can I use this Skill for regression diagnostics like heteroskedasticity and multicollinearity?▼

Yes, this Skill supports regression diagnostics by detecting heteroskedasticity, autocorrelation, and multicollinearity in financial data. It utilizes Python libraries to fit models and run the necessary diagnostic tests.

Does this Skill support nonparametric bootstrap methods for statistical inference?▼

This Skill includes nonparametric bootstrap methods for statistical inference within its framework. It allows users to perform resampling on quantitative financial data to estimate the sampling distribution of statistics.

What Python libraries are required to perform statistical modeling with this Skill?▼

Performing statistical modeling with this Skill requires the Python libraries statsmodels, pandas, and numpy. These dependencies are essential for executing the advanced statistical computations and data manipulations.

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