skills.rest
hxhyyy
hxhyyyCOMMUNITY
Explore all skills by this author →
GitHub

quant-statistics

Perform statistical tests on financial time-series data with statsmodels and arch.

Updated Jul 8, 2026
Search Tags:#statistics#trading#econometrics#garch#quantitative#timeseries
One-click install
npx skills add https://github.com/hxhyyy/Vibe-Trading --skill quant-statistics-hxhyyy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quant-statistics
Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/quant-statistics
Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill quant-statistics-hxhyyy

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the complexity of validating financial time-series data and ensuring statistical rigor in trading strategy development, preventing common pitfalls like spurious regressions and overfitting.

Core Features & Use Cases

  • Time-Series Validation: Perform ADF unit-root tests, cointegration analysis, and Granger causality tests to verify data stationarity and predictive relationships.
  • Volatility Modeling: Implement GARCH(1,1) and its variants to forecast market volatility and assess risk.
  • Regression Diagnostics: Automatically check for heteroskedasticity, autocorrelation, and multicollinearity to ensure model reliability.
  • Use Case: Use this skill to test if two assets are cointegrated for a pair-trading strategy or to determine the statistical significance of a factor's Sharpe ratio using bootstrap methods.

Quick Start

Use the quant-statistics skill to perform an ADF unit-root test on the provided price series data to check for stationarity.

Frequently Asked Questions about quant-statistics

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

FAQPage Schema
How do I test if two assets are cointegrated for a pair trading strategy?▼

To validate predictive relationships for quantitative trading, perform Granger causality tests on financial time-series data. This verifies whether lagged values of one time series can statistically forecast another, ensuring robust model estimation.

How do I forecast market volatility using GARCH models in Python?▼

Implement GARCH(1,1) and its variants to forecast market volatility by fitting the arch library model to financial time-series data. This estimates conditional variance to assess market risk and predict future volatility patterns.

How can I check for heteroskedasticity and autocorrelation in regression diagnostics?▼

Check for heteroskedasticity and autocorrelation in regression diagnostics by automatically applying statistical tests via statsmodels. This ensures model reliability by detecting violations of ordinary least squares assumptions in your factor research regression.

Does this quantitative analysis skill support bootstrap methods for factor research?▼

Yes, this quantitative analysis skill supports bootstrap methods for factor research to determine the statistical significance of a factor's Sharpe ratio. It utilizes statsmodels to ensure robust model estimation and hypothesis testing.

Why does my trading strategy suffer from spurious regression overfitting?▼

Spurious regression and overfitting in trading strategies occur when financial time-series data lacks stationarity. Perform rigorous quantitative statistical analysis, such as ADF unit-root tests, to validate data properties and prevent these common pitfalls.

Related Skills

Explore More Data & Analytics Skills→
0xZKnw0xZKnw

seasonal

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

Community
Intermediate
tradermontytradermonty
2.6k

options-strategy-advisor

Simulate options strategies with Black-Scholes pricing and Greeks.

Community
Intermediate
ovachieverovachiever
52

statsmodels

Estimate regression and time-series models for statistical inference.

Community
Advanced
the-thinker0the-thinker0
6

probability-statistics

Quantify uncertainty in data-driven decisions using probability theory.

Community
Advanced