skills.rest
0xZKnw
0xZKnwCOMMUNITY
Explore all skills by this author →
Website𝕏@0xZKnwGitHub

quant-statistics

Perform stationarity, cointegration, GARCH, and regression diagnostics on financial time-series data.

Updated Jun 30, 2026
Search Tags:#statistics#trading#econometrics#garch#quantitative#cointegration
One-click install
npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill quant-statistics-0xzknw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quant-statistics
Source: https://github.com/0xZKnw/vibe-trading-tap/tree/main/agent/src/skills/quant-statistics
Command: npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill quant-statistics-0xzknw

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires statsmodels, arch, pandas, numpy.

What problem does it solve?

This skill addresses the complexity of validating financial models by providing rigorous statistical testing for stationarity, cointegration, volatility, and regression diagnostics.

Core Features & Use Cases

  • Time-Series Analysis: Perform ADF unit-root tests and Engle-Granger cointegration tests to identify mean-reverting pairs for statistical arbitrage.
  • Volatility Modeling: Utilize GARCH(1,1) and its variants to forecast market volatility and assess risk persistence.
  • Regression Diagnostics: Automatically detect heteroskedasticity, autocorrelation, and multicollinearity to ensure the reliability of factor research and strategy backtests.

Quick Start

Use the quant-statistics skill to perform an ADF unit-root test on the provided price series data to determine if it is stationary.

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 to validate statistical arbitrage trading strategies?▼

To validate statistical arbitrage strategies, cointegration testing applies the Engle-Granger test to financial time-series data. It identifies mean-reverting pairs by checking if non-stationary price series share a long-term equilibrium, ensuring robust pair selection.

What is the best way to model and forecast market volatility using GARCH in Python?▼

Modeling market volatility with GARCH(1,1) utilizes the arch library to assess risk persistence and forecast future variance in financial time-series data. It helps quantify changing volatility patterns for robust investment decision-making.

How do I check stationarity in financial time-series data before running regressions?▼

Checking stationarity in financial time-series data requires an ADF unit-root test to determine if the series is stationary. This prevents spurious regressions by ensuring the data's statistical properties do not change over time.

Does statsmodels support regression diagnostics for heteroskedasticity and autocorrelation?▼

Yes, statsmodels supports regression diagnostics by automatically detecting heteroskedasticity, autocorrelation, and multicollinearity. These diagnostics ensure the reliability of factor research and strategy backtests by validating model assumptions.

When should I not use standard OLS regression for quantitative trading factor research?▼

Standard OLS regression should not be used for factor research when time-series data exhibits heteroskedasticity or autocorrelation. Applying OLS without diagnostics leads to unreliable statistical inference and invalid investment decisions.

Related Skills

Explore More Finance & Accounting Skills→
0xZKnw0xZKnw

seasonal

Generate trading signals from historical month-of-year and day-of-week effects.

Community
Intermediate
DruidssDruidss
2

btc-momentum-analyzer

Analyze BTC momentum across eight timeframes using MACD-based signals.

Community
Advanced
DaddyElonMusk69DaddyElonMusk69

quant-statistics

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

Community
Intermediate
IndustrialIndustrial

Reinforcing Feedback Loop Analysis

Trace circular causation and analyze amplification dynamics in system feedback loops.

Community
Intermediate