Is it compatible with standard Python data libraries?

Absolutely. The implementation patterns are designed to work seamlessly with NumPy, Pandas, and SciPy, which are the industry standards for financial data analysis.

Can this skill help with portfolio rebalancing?

Yes, it includes patterns for Risk Parity weighting and diversification ratio analysis, which are used to determine optimal asset allocations based on risk contribution.

What types of Value at Risk (VaR) does this skill support?

The skill supports three major VaR methodologies: Historical VaR, Parametric (Normal) VaR, and Cornish-Fisher VaR, which adjusts for skewness and kurtosis in non-normal return distributions.

How does it handle downside risk specifically?

It implements several downside-focused metrics including Sortino ratio, Omega ratio, and Conditional VaR (Expected Shortfall) to capture tail risk that standard volatility metrics might miss.

Risk Metrics Calculation

Name: Risk Metrics Calculation
Author: GaitanS

byGaitanS

0•

Data Science & ML

Calculates comprehensive portfolio risk metrics and risk-adjusted return ratios for quantitative trading strategies.

This skill provides a specialized toolkit for quantitative analysts and traders to measure and manage portfolio risk within Claude Code. It implements core financial metrics including Value at Risk (VaR), Expected Shortfall (CVaR), detailed drawdown analysis, and a suite of risk-adjusted performance ratios such as Sharpe, Sortino, and Calmar. Beyond basic statistics, it provides implementation patterns for portfolio-level risk attribution, marginal risk contribution, and risk parity weighting, making it an essential component for developers building automated trading systems, risk dashboards, or strategy backtesting frameworks.

Key Features

01Suite of risk-adjusted performance ratios including Sharpe, Sortino, Calmar, and Omega.

02Portfolio risk attribution tools for calculating marginal and component risk contributions.

03Advanced VaR modeling including Historical, Parametric, and Cornish-Fisher methods.

04Diversification and correlation analysis for multi-asset portfolio management.

05Comprehensive drawdown analysis measuring depth, frequency, and recovery duration.

060 GitHub stars

Use Cases

01Implementing dynamic position sizing based on real-time volatility and risk limits.

02Building automated risk monitoring dashboards for live algorithmic trading.

03Generating detailed performance attribution reports for investment strategies.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add gaitans/ai-mancare risk-metrics-calculation

For use in Claude.ai and ChatGPT

Download Skill