Does it support risk-adjusted performance metrics?

Absolutely. The skill can run OLS regressions to calculate alpha, beta, and R-squared, as well as bootstrap Sharpe ratios for better confidence estimation.

Can I use this to test if financial returns are normal?

Yes, it uses the Jarque-Bera test to analyze skewness and excess kurtosis, helping you determine if a return series deviates from a normal distribution.

What mathematical foundations are included?

It covers descriptive statistics, covariance estimation, linear regression, hypothesis testing (t-tests, F-tests), and resampling techniques like bootstrapping.

How does this skill improve portfolio optimization?

It implements the Ledoit-Wolf shrinkage estimator, which blends the sample covariance matrix with a structured target to produce more stable and reliable portfolio weights.

Statistics Fundamentals

Name: Statistics Fundamentals
Author: JoelLewis

byJoelLewis

•

데이터 과학 및 ML

Applies essential statistical methods to financial data for risk analysis, portfolio construction, and factor modeling.

The Statistics Fundamentals skill equips Claude with the quantitative rigor required for financial analysis, covering everything from basic descriptive statistics to advanced covariance estimation. It enables professionals to analyze return distributions, run CAPM regressions to identify alpha, and stabilize portfolio optimizations using Ledoit-Wolf shrinkage. Whether you are testing for normality with Jarque-Bera or estimating confidence intervals via bootstrap resampling, this skill provides the mathematical foundation necessary for building robust financial models and risk management frameworks.

주요 기능

0118 GitHub stars

02Advanced covariance matrix estimation using Ledoit-Wolf shrinkage for stable optimization.

03Comprehensive regression analysis for CAPM and multi-factor model evaluation.

04Automated calculation of annualized volatility and risk-adjusted return metrics.

05Robust normality testing including Jarque-Bera, skewness, and excess kurtosis analysis.

06Non-parametric bootstrap methods for estimating standard errors and confidence intervals.

사용 사례

01Generating stable covariance matrices for Mean-Variance Portfolio Optimization.

02Testing financial time-series data for fat tails and non-normal distribution patterns.

03Evaluating fund performance by calculating risk-adjusted alpha and market beta.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add joellewis/finance_skills statistics-fundamentals

For use in Claude.ai and ChatGPT

Download Skill