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quant-statistics

Perform time-series, volatility, and hypothesis tests with Python statistical libraries.

Updated Apr 19, 2026
Search Tags:#time-series#bootstrap#hypothesis-testing#statistical-analysis#garch#cointegration#regression-diagnostics
One-click install
npx skills add https://github.com/ajithkumar31082004-bit/Vibe-Trading --skill quant-statistics-ajithkumar31082004-bit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quant-statistics
Source: https://github.com/ajithkumar31082004-bit/Vibe-Trading/tree/main/Vibe-Trading-main/agent/src/skills/quant-statistics
Command: npx skills add https://github.com/ajithkumar31082004-bit/Vibe-Trading --skill quant-statistics-ajithkumar31082004-bit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill consolidates advanced quantitative-statistical methods for time-series testing, volatility modeling, and statistical inference to support strategy research and factor development.

Core Features & Use Cases

  • Time-series tests (ADF, cointegration) for assessing stationarity and long-run relationships in price data.
  • GARCH-family volatility modeling and regression diagnostics (heteroskedasticity, autocorrelation) with bootstrap options.
  • Hypothesis testing, bootstrap inference, and multiple-testing corrections to validate strategy signals and risk metrics.

Quick Start

Run a basic ADF test on a given series and interpret the result to decide on differencing or cointegration steps.

Frequently Asked Questions about quant-statistics

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

FAQPage Schema
How do I test for cointegration and stationarity in financial time-series data?▼

Cointegration and stationarity in financial time-series data are tested using ADF tests to determine if differencing is needed and identify long-run relationships. This Skill applies ADF and cointegration tests directly to price series for strategy research.

Can I model volatility with GARCH and run regression diagnostics in Python?▼

GARCH volatility modeling and regression diagnostics can be run in Python using statsmodels and arch libraries. This Skill provides reproducible code templates for heteroskedasticity, autocorrelation checks, and GARCH-family modeling.

What is the best way to validate trading strategy signals using statistical inference?▼

Validating trading strategy signals using statistical inference is best achieved through hypothesis testing and bootstrap inference. This Skill applies multiple-testing corrections to rigorously validate strategy signals and risk metrics.

Does Granger causality testing work for identifying predictive relationships in price data?▼

Granger causality testing works for identifying predictive relationships in price data by measuring whether one time-series can forecast another. This Skill includes Granger causality alongside ADF and cointegration for comprehensive time-series analysis.

Do I need specific Python libraries to perform advanced quantitative statistical analysis?▼

Specific Python libraries are required to perform advanced quantitative statistical analysis, specifically statsmodels and arch for modeling, plus numpy and pandas for data handling. This Skill depends on these libraries to execute its reproducible code templates.

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