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quant-statistics

Perform quantitative statistical analysis on financial time series data.

Updated May 25, 2026
Search Tags:#hypothesis testing#financial analysis#statistical modeling#time-series analysis#volatility modeling
One-click install
npx skills add https://github.com/NigarumOvum/AutoTrading --skill quant-statistics-nigarumovum
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quant-statistics
Source: https://github.com/NigarumOvum/AutoTrading/tree/main/Vibe-Trading/agent/src/skills/quant-statistics
Command: npx skills add https://github.com/NigarumOvum/AutoTrading --skill quant-statistics-nigarumovum

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires statsmodels, pandas, numpy, scipy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides robust statistical analysis and modeling tools for financial time series data, solving the problem of identifying trends, relationships, and volatility patterns within financial markets.

Core Features & Use Cases

  • Time-Series Analysis: Conducts unit-root and cointegration tests for time-series data to determine stationarity and long-run equilibrium relationships.
  • Volatility Modeling: Implements GARCH volatility modeling for long-term and short-term volatility forecasting.
  • Regression Diagnostics: Detects heteroskedasticity, autocorrelation, and multicollinearity in regression models.
  • Bootstrap Methods: Offers nonparametric bootstrap for estimating confidence intervals and testing hypothesis.
  • Hypothesis Testing: Implements various statistical tests for hypothesis testing in financial datasets.
  • Use Case: A quantitative investor looking to automate statistical tests for factor performance or conduct pair trading could leverage this Skill.

Quick Start

To analyze the stationarity of a time series data, use the following command: analyze_stationarity series_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 time series stationarity for financial analysis?▼

Time series stationarity for financial analysis is tested using unit-root tests to verify if mean and variance remain constant over time. This Skill automates stationarity testing workflows using pandas and statsmodels to identify reliable trends.

Can I use statsmodels for GARCH volatility modeling?▼

Statsmodels supports GARCH volatility modeling to forecast long-term and short-term market volatility. This Skill implements GARCH modeling to identify and project volatility patterns within financial time series datasets.

What's the best way to detect heteroskedasticity and autocorrelation in a regression model?▼

Detecting heteroskedasticity and autocorrelation in a regression model is best achieved through dedicated regression diagnostics. This Skill performs these diagnostics alongside multicollinearity checks to validate financial model assumptions.

Do I need statistical knowledge to run cointegration analysis?▼

Statistical knowledge and financial markets expertise are required to run cointegration analysis. This Skill requires domain understanding to properly interpret long-run equilibrium relationships and apply the tests to pair trading strategies.

How does nonparametric bootstrap work for hypothesis testing?▼

Nonparametric bootstrap works for hypothesis testing by resampling datasets with replacement to estimate confidence intervals without assuming a specific distribution. This Skill offers bootstrap methods to test financial factor performance robustly.

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