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quant-statistics

Perform ADF and cointegration tests on financial time-series data.

1|Updated Jun 23, 2026
Search Tags:#data analysis#time-series#quantitative finance#volatility#regression diagnostics
One-click install
npx skills add https://github.com/644408071-design/Kokpop --skill quant-statistics-644408071-design
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quant-statistics
Source: https://github.com/644408071-design/Kokpop/tree/main/agent/src/skills/quant-statistics
Command: npx skills add https://github.com/644408071-design/Kokpop --skill quant-statistics-644408071-design

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides powerful quantitative tools for analyzing financial data, including time-series testing, volatility modeling, regression diagnostics, and hypothesis testing.

Core Features & Use Cases

  • Time-Series Tests: Perform ADF unit-root and cointegration tests to ensure data stationarity.
  • Volatility Modeling: Utilize GARCH models to model and forecast volatility.
  • Regression Diagnostics: Diagnose issues like heteroskedasticity and autocorrelation.
  • Use Case: Imagine you need to test the stationarity of a stock price time series before building a trading strategy. Use this Skill to conduct the ADF test and determine if the series is stationary.

Quick Start

Execute the adf_test script with the 'stock_prices.csv' file to check the stationarity of the stock prices.

Frequently Asked Questions about quant-statistics

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

FAQPage Schema
How do I test the stationarity of financial time-series data before building a trading strategy?▼

To test time-series stationarity for a trading strategy, execute the ADF unit-root test script with a CSV file like 'stock_prices.csv' to determine if the financial series is stationary.

Can I model and forecast volatility using GARCH with pandas and statsmodels?▼

Yes, you can model and forecast volatility using GARCH models, leveraging pandas, statsmodels, and numpy to perform the quantitative calculations required for financial time-series analysis.

How do I run regression diagnostics for heteroskedasticity and autocorrelation in financial data?▼

Run regression diagnostics to identify heteroskedasticity and autocorrelation issues in financial data using built-in statistical tests provided by the statsmodels dependency.

What is the best way to perform cointegration tests on stock price time series?▼

The best way to perform cointegration tests on stock price time series is using dedicated quantitative analysis scripts that evaluate long-term equilibrium relationships between financial assets.

Do I need numpy and statsmodels installed to run quantitative finance analysis scripts?▼

Yes, you need pandas, statsmodels, and numpy installed as dependencies to run the quantitative analysis scripts for time-series testing, volatility modeling, and regression diagnostics.

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