Apply quantitative statistical tests to financial time-series data.
npx skills add https://github.com/GGwujun/SigmX --skill quant-statistics-ggwujun
Please help me install this Agent Skill. Skill: quant-statistics Source: https://github.com/GGwujun/SigmX/tree/main/agent/src/skills/quant-statistics Command: npx skills add https://github.com/GGwujun/SigmX --skill quant-statistics-ggwujun
This skill provides a comprehensive toolkit of quantitative statistical methods for analyzing financial time-series data, enabling robust testing, modeling, and inference for strategy development.
Run this skill to perform ADF tests, cointegration analysis, and GARCH modeling on time-series data.
High-intent search queries and answers about installing and using this skill.
To test financial time-series data, you apply ADF unit-root tests and cointegration analysis to identify long-run relationships and stationarity in your pandas Series of returns.
Modeling conditional volatility is best achieved using GARCH family models and variants, which quantify and forecast conditional volatility for risk management and strategy sizing.
To run regression diagnostics in Python, apply tests for heteroskedasticity, autocorrelation, and multicollinearity, then use guidance on robust standard errors and model refinement for your factor data.
Yes, these quantitative statistical methods require specific Python libraries to function, specifically needing statsmodels and arch installed to perform the time-series testing and volatility modeling.
Yes, you can use the bootstrap and hypothesis-testing framework to evaluate significance and robustness across diverse financial series and factor data samples and scenarios.
hxhyyyPerform statistical tests on financial time-series data with statsmodels and arch.
DaddyElonMusk69Perform quantitative statistical tests and GARCH volatility modeling for financial time-series.
mkurmanProvide statistical modeling and diagnostics for Python econometrics and time-series analysis.
xjtulycEstimates VAR/VECM models with Granger causality, Johansen cointegration, and IRF/FEVD using Python statsmodels.
