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JacobHsu
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quant-statistics

Run ADF, cointegration, GARCH, and regression diagnostics on financial time-series data.

Updated Apr 9, 2026
Search Tags:#finance#time-series#bootstrapping#garch#cointegration#regression-diagnostics#quantitative-statistics
One-click install
npx skills add https://github.com/JacobHsu/vibe-trading-agent --skill quant-statistics-jacobhsu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quant-statistics
Source: https://github.com/JacobHsu/vibe-trading-agent/tree/main/agent/src/skills/quant-statistics
Command: npx skills add https://github.com/JacobHsu/vibe-trading-agent --skill quant-statistics-jacobhsu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Quantitative strategies often fail because their statistical foundations are unchecked, leaving non-stationary series, heteroskedastic residuals, autocorrelation, or mis-specified volatility models to drive spurious results; this skill centralizes the statistical discipline needed to prove whether a signal can be trusted.

Core Features & Use Cases

  • Stationarity & Cointegration: Runs ADF and Engle-Granger tests, computes hedge ratios, and tracks z-score thresholds so that pair trading and factor research rely on stable long-run relationships.
  • Volatility & Risk Modeling: Fits GARCH(1,1) (with nods to EGARCH/GJR variants) and delivers persistence, long-run volatility, and short-horizon forecasts to guide risk limits for equity, crypto, and futures strategies.
  • Regression Diagnostics & Inference: Provides heteroskedasticity, autocorrelation, multicollinearity, and bootstrap routines plus multiple-testing guidance so that models are statistically sound before deployment.
  • Use Case: A researcher validating a new macro factor portfolio can use this skill to confirm stationarity, correct for heteroskedastic residuals, and justify Sharpe ratio confidence via bootstrap.

Quick Start

Run the quant-statistics skill to evaluate stationarity, volatility, and regression diagnostics for your strategy data.

Frequently Asked Questions about quant-statistics

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

FAQPage Schema
How do I test financial time-series stationarity for pair trading?▼

Run ADF and Engle-Granger cointegration tests to evaluate financial time-series stationarity, compute hedge ratios, and track z-score thresholds to validate stable long-run pair trading relationships.

Can I fit GARCH models for crypto volatility forecasting?▼

Yes, fit GARCH(1,1) with EGARCH and GJR variants for crypto volatility forecasting to extract persistence, long-run volatility, and short-horizon risk limit guidance.

What is the best way to correct heteroskedasticity in quant regression models?▼

Run regression diagnostics including heteroskedasticity, autocorrelation, and multicollinearity tests to identify issues and apply bootstrap routines for corrected quantitative inference.

How do I validate Sharpe ratio confidence using bootstrap?▼

Apply bootstrap routines to compute Sharpe ratio confidence intervals and significance thresholds, ensuring macro factor portfolio returns are statistically reproducible before deployment.

Does this statistical testing support equity and macro datasets?▼

Yes, statistical diagnostics support equity, crypto, and macro datasets by evaluating stationarity, volatility properties, and regression soundness across these financial asset classes.

Why does my quantitative strategy show spurious results?▼

Spurious quantitative results often stem from unchecked non-stationary series, heteroskedastic residuals, or autocorrelation, which rigorous statistical diagnostics can detect and correct.

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