What specific financial metrics can this skill calculate?

This skill calculates a wide range of metrics including Volatility, Beta, Value at Risk (VaR), Conditional VaR (CVaR), Sharpe, Sortino, Calmar, and Omega ratios, as well as detailed drawdown statistics.

Does this skill support multi-asset portfolio analysis?

Yes, it includes specific patterns for portfolio-level risk, including marginal risk contribution, component risk, and automated risk parity weight calculations.

How does it handle non-normal return distributions?

The skill implements the Cornish-Fisher expansion for VaR calculations, which adjusts for skewness and kurtosis in return data to provide more accurate tail risk estimates.

Which Python libraries are required for these calculations?

The implementation patterns rely on standard quantitative libraries including NumPy, Pandas, and SciPy for statistical calculations and data manipulation.

Portfolio Risk Metrics Calculation

Name: Portfolio Risk Metrics Calculation
Author: drgaciw

bydrgaciw

Data Science & ML

Calculates comprehensive financial risk metrics including VaR, CVaR, Sharpe, and Sortino ratios for quantitative portfolio management.

About

The Risk Metrics Calculation skill provides a specialized toolkit for quantitative analysts and traders to measure, monitor, and manage portfolio risk with high precision. It implements industry-standard mathematical patterns for volatility, tail risk (Value at Risk and Expected Shortfall), and drawdown analysis. This skill is ideal for developers building risk dashboards, backtesting trading strategies, or ensuring regulatory compliance, offering out-of-the-box implementations for risk-adjusted performance ratios and portfolio-level diversification analysis using Python, Pandas, and SciPy.

Key Features

Detailed Drawdown Analysis (Max Drawdown, Duration, and Recovery tracking)
Advanced Tail Risk Metrics (Historical, Parametric, and Cornish-Fisher VaR/CVaR)
Portfolio Risk Decomposition (Marginal risk contribution and Risk Parity weighting)
0 GitHub stars
Comprehensive Risk-Adjusted Ratios (Sharpe, Sortino, Calmar, and Omega)
Rolling Window Analytics for dynamic risk monitoring over time

Use Cases

Building automated risk dashboards and monitoring systems for live trading
Performing historical backtest performance and risk attribution analysis
Implementing risk-based position sizing and regulatory compliance reporting

About

Key Features

Detailed Drawdown Analysis (Max Drawdown, Duration, and Recovery tracking)
Advanced Tail Risk Metrics (Historical, Parametric, and Cornish-Fisher VaR/CVaR)
Portfolio Risk Decomposition (Marginal risk contribution and Risk Parity weighting)
0 GitHub stars
Comprehensive Risk-Adjusted Ratios (Sharpe, Sortino, Calmar, and Omega)
Rolling Window Analytics for dynamic risk monitoring over time

Use Cases

Building automated risk dashboards and monitoring systems for live trading
Performing historical backtest performance and risk attribution analysis
Implementing risk-based position sizing and regulatory compliance reporting