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quant-statistics

Analyze financial time-series data with ADF, cointegration, GARCH, and diagnostic tests.

Updated Apr 14, 2026
Search Tags:#diagnostics#time-series#bootstrap#adf#statistical-analysis#garch#cointegration
One-click install
npx skills add https://github.com/Liangwei-zhang/six-stock --skill quant-statistics-liangwei-zhang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quant-statistics
Source: https://github.com/Liangwei-zhang/six-stock/tree/main/Vibe-Trading/agent/src/skills/quant-statistics
Command: npx skills add https://github.com/Liangwei-zhang/six-stock --skill quant-statistics-liangwei-zhang

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Quantitative statistical methods are essential for validating investment hypotheses, detecting non-stationarity, understanding long-run relationships, and assessing risk. This skill provides a structured toolkit to perform time-series tests, volatility modeling, regression diagnostics, bootstrap inference, and hypothesis testing in a reproducible way.

Core Features & Use Cases

  • ADF Unit-Root Test for stationarity assessment and decision rules for modeling.
  • Cointegration testing and pair-trading support, including hedge ratio estimation and spread analysis.
  • GARCH family volatility modeling with diagnostics for heteroskedasticity and autocorrelation, plus bootstrap-based inference.
  • Regression diagnostics workflow including heteroskedasticity, autocorrelation, multicollinearity checks, and model validity.
  • Bootstrap methods for confidence intervals and significance testing of strategy returns.

Quick Start

Run a full quant-statistics analysis on a provided time-series dataset, including ADF, cointegration, GARCH, bootstrap, and diagnostic tests.

Frequently Asked Questions about quant-statistics

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

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

Apply the ADF unit-root test to assess time-series stationarity and establish decision rules for modeling financial data. This skill runs ADF alongside cointegration and diagnostic tests to validate your series before strategy development.

How can I run cointegration tests for pair trading strategies?▼

Run cointegration testing to support pair trading by estimating hedge ratios and analyzing spread dynamics. This skill provides a structured workflow to identify long-run relationships between asset price series for reproducible strategy research.

What's the best way to model volatility and perform bootstrap inference on financial series?▼

Apply GARCH family volatility modeling to detect heteroskedasticity and autocorrelation, then use bootstrap methods for inference. This skill integrates both to assess risk and test significance of strategy returns across financial asset classes.

How do I check regression diagnostics for heteroskedasticity and multicollinearity in factor research?▼

Run a regression diagnostics workflow to check for heteroskedasticity, autocorrelation, and multicollinearity to validate model validity. This skill applies these diagnostic tests to factor research and risk modeling workflows for financial series.

Can I use bootstrap methods to calculate confidence intervals for backtested strategy returns?▼

Yes, you can apply bootstrap methods to calculate confidence intervals and test the significance of strategy returns. This skill integrates bootstrap inference into backtesting workflows to ensure reproducible risk and performance assessment.

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