quant-statistics

Perform statistical tests and models for quantitative finance using Python libraries.

Updated Apr 10, 2026

Search Tags:#statistics #regression #bootstrap #adf #garch #cointegration #quant

Ready to run

npx skills add https://github.com/ebrahim-sani/trading-automation --skill quant-statistics-ebrahim-sani

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: quant-statistics
Source: https://github.com/ebrahim-sani/trading-automation/tree/main/vibe-trading/agent/src/skills/quant-statistics
Command: npx skills add https://github.com/ebrahim-sani/trading-automation --skill quant-statistics-ebrahim-sani

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Quantitative traders need reliable statistical tests and models to validate strategies, assess risk, and ensure robustness, but implementing these methods manually is time‑consuming and error‑prone.

Core Features & Use Cases

ADF Unit‑Root Test: Determines stationarity of price or return series.
Cointegration Test & Pair‑Trading Signals: Identifies long‑run equilibrium relationships and generates hedge ratios.
GARCH Volatility Modeling: Fits and forecasts volatility for risk management.
Regression Diagnostics: Detects heteroskedasticity, autocorrelation, and multicollinearity.
Bootstrap Methods: Provides confidence intervals for metrics like Sharpe ratio.
Hypothesis‑Testing Framework: Applies multiple‑testing corrections and significance evaluation.

Quick Start

Ask the quant‑statistics skill to run an ADF test on your time‑series data and return a concise stationarity report.

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 for my price series?▼

To test stationarity, an ADF unit-root test evaluates whether your price or return series has a unit root. This test produces a concise stationarity report indicating if the data is stable for quantitative modeling.

Can I use Python to model volatility with GARCH for risk management?▼

Yes, you can use the GARCH volatility modeling feature to fit and forecast volatility. By applying the arch Python library, it models changing variance over time to produce forecasts used for financial risk management.

How does cointegration testing work for pair trading signals?▼

Cointegration testing identifies long-run equilibrium relationships between two price series. By evaluating this statistical equilibrium, it generates hedge ratios and pair-trading signals, allowing you to trade based on mean-reversion opportunities.

Do I need pandas and statsmodels to run regression diagnostics?▼

Yes, you need pandas, statsmodels, arch, and numpy to run regression diagnostics. These Python libraries execute the analysis that detects heteroskedasticity, autocorrelation, and multicollinearity in your quantitative models.

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

The best way to calculate confidence intervals for a Sharpe ratio is using bootstrap methods. Bootstrap resampling provides robust confidence intervals by repeatedly sampling your trading strategy returns to assess statistical significance.

Why does my trading strategy need multiple-testing corrections in hypothesis testing?▼

Your trading strategy needs multiple-testing corrections to avoid false positives during significance evaluation. Applying a hypothesis-testing framework adjusts p-values across multiple tests, ensuring robust statistical significance validation for your strategies.