Can I use this for portfolio-level analysis?

Yes, it includes specific patterns for weighted portfolio returns, portfolio volatility, marginal risk contribution, and risk parity weight optimization.

What specific metrics does this skill support?

It covers a wide range of metrics including standard deviation, Beta, VaR (Historical, Parametric, and Cornish-Fisher), CVaR, Drawdowns, Sharpe, Sortino, Calmar, and Omega ratios.

Is this suitable for real-time risk monitoring?

Absolutely, the Python implementation patterns are designed to be integrated into live trading systems, dashboards, or reporting pipelines for continuous risk assessment.

Does it handle non-normal return distributions?

Yes, it implements the Cornish-Fisher expansion to adjust Value at Risk calculations for skewness and kurtosis in non-normal return distributions.

Quantitative Risk Metrics Calculation

Name: Quantitative Risk Metrics Calculation
Author: duanbiao2000

byduanbiao2000

0•

Data Science & ML

Calculates comprehensive portfolio risk metrics and performance indicators for quantitative trading strategies.

This skill provides a specialized toolkit for quantitative analysts and traders to measure and monitor financial risk within their portfolios. It implements industry-standard calculations for volatility, tail risk (Value at Risk and Expected Shortfall), drawdown analysis, and risk-adjusted return ratios like Sharpe and Sortino. Whether building risk dashboards, implementing position limits, or performing performance attribution, this skill offers robust Python patterns for both individual asset and portfolio-level risk assessment, including advanced techniques like Cornish-Fisher expansions and risk parity weighting.

Key Features

01Tail risk measurement using historical, parametric, and Cornish-Fisher Value at Risk (VaR)

02Portfolio-level risk decomposition and marginal risk contribution analysis

03Advanced drawdown analysis including maximum drawdown, duration, and recovery tracking

040 GitHub stars

05Comprehensive risk-adjusted performance ratios including Sharpe, Sortino, Calmar, and Omega

06Optimization patterns for risk parity weighting and diversification ratios

Use Cases

01Implementing regulatory and internal risk limit reporting for investment portfolios

02Developing automated risk monitoring systems for algorithmic trading bots

03Backtesting trading strategies with detailed risk-adjusted performance reports

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add duanbiao2000/obsidiandoc26 risk-metrics-calculation

For use in Claude.ai and ChatGPT

Download Skill