Verified:GitHub

quant-statistics

Perform quantitative statistical tests and GARCH volatility modeling for financial time-series.

Updated Apr 12, 2026

Search Tags:#time-series #bootstrap #volatility #adf #garch #cointegration #regression-diagnostics

One-click install

npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill quant-statistics-daddyelonmusk69

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill: quant-statistics
Source: https://github.com/DaddyElonMusk69/motis-agent/tree/main/skills/finance/quant-statistics
Command: npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill quant-statistics-daddyelonmusk69

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Quantitative researchers and traders need reliable statistical tests and diagnostics to validate time-series assumptions, model volatility, and assess inference significance; ad hoc implementations across projects lead to inconsistent results and hidden biases.

Core Features & Use Cases

Stationarity & Cointegration Testing: ADF unit-root tests and Engle-Granger cointegration checks for pair trading and mean-reversion strategies.
Volatility Modeling: GARCH(1,1) and variant guidance for fitting, persistence analysis, and short-horizon volatility forecasts for risk management.
Regression Diagnostics & Inference: Heteroskedasticity and autocorrelation tests, VIF for multicollinearity, Newey-West fixes, and bootstrap-based confidence intervals for Sharpe and factor returns.
Use Case: Validate a pair-trading strategy by testing stationarity of spreads, estimating hedge ratios, fitting a GARCH model for risk limits, and bootstrapping Sharpe confidence intervals before deployment.

Quick Start

Run a stationarity test and GARCH fit on your daily returns series and return a concise markdown report with ADF/cointegration results, GARCH parameters and forecasts, regression diagnostics, and a bootstrap Sharpe confidence interval.

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 a pair trading strategy?▼

GARCH volatility modeling fits GARCH(1,1) parameters to daily returns, analyzing persistence and generating short-horizon volatility forecasts for risk management and risk limits.

What is the best way to calculate bootstrap confidence intervals for a Sharpe ratio?▼

Regression diagnostics detect heteroskedasticity and autocorrelation through dedicated tests, calculate VIF scores for multicollinearity, and apply Newey-West fixes to produce corrected inference and formatted summary tables.

Can I get ADF and Granger p-values formatted in a markdown report?▼

Yes, you can generate a concise markdown report containing ADF and Granger p-values, GARCH parameter estimates, regression diagnostics, and bootstrap confidence intervals from your daily returns series.

Does this statistical testing approach work for mean-reversion strategies?▼

Yes, this statistical testing approach works for mean-reversion strategies by validating time-series assumptions through stationarity testing of spreads and Engle-Granger cointegration checks specifically designed for pair-trading workflows.