Can I use this for multi-asset portfolio analysis?

Yes, it includes a PortfolioRisk class designed to handle asset weightings, covariance matrices, marginal risk contributions, and diversification ratios.

Does it support real-time or rolling risk monitoring?

The skill includes patterns for rolling risk metrics, allowing you to track how volatility, VaR, and other indicators evolve over time-based windows.

Which Value at Risk (VaR) methods are supported by this skill?

The skill provides patterns for Historical, Parametric (Normal), and Cornish-Fisher VaR, the latter of which accounts for skewness and kurtosis in non-normal return distributions.

What risk-adjusted performance metrics are included?

It includes standard metrics like the Sharpe and Sortino ratios, as well as the Calmar ratio for drawdown-relative performance and the Omega ratio for overall gain/loss probability.

Are the calculations compatible with standard Python libraries?

Absolutely. The implementation patterns are built using NumPy, Pandas, and SciPy, ensuring seamless integration with existing data science and financial pipelines.

Risk Metrics Calculation

Name: Risk Metrics Calculation
Author: ccf

byccf

데이터 과학 및 ML

Calculates comprehensive portfolio risk metrics and performance indicators to ensure robust capital preservation and institutional-grade risk management.

소개

This skill provides a specialized toolkit for quantitative traders and financial developers to measure market risk and performance directly within Claude Code. It automates complex statistical calculations including Value at Risk (VaR), Expected Shortfall (CVaR), and various risk-adjusted ratios such as Sharpe, Sortino, and Calmar. By providing implementation patterns for tail risk analysis, drawdown tracking, and portfolio-level risk parity, it enables developers to build sophisticated risk dashboards and implement strict risk limits for trading systems.

주요 기능

Comprehensive drawdown metrics with max drawdown, duration, and recovery tracking.
Rolling window calculation patterns for time-varying risk and volatility monitoring.
Tail risk analysis including Historical, Parametric, and Cornish-Fisher VaR/CVaR.
0 GitHub stars
Portfolio-level risk attribution and risk parity weighting algorithms.
Risk-adjusted performance ratios including Sharpe, Sortino, Calmar, and Omega.

사용 사례

Implementing dynamic position sizing based on real-time volatility and risk limits.
Performing deep performance attribution and backtesting for investment strategies.
Building automated risk reporting dashboards for quantitative trading systems.

소개

주요 기능

Comprehensive drawdown metrics with max drawdown, duration, and recovery tracking.
Rolling window calculation patterns for time-varying risk and volatility monitoring.
Tail risk analysis including Historical, Parametric, and Cornish-Fisher VaR/CVaR.
0 GitHub stars
Portfolio-level risk attribution and risk parity weighting algorithms.
Risk-adjusted performance ratios including Sharpe, Sortino, Calmar, and Omega.

사용 사례

Implementing dynamic position sizing based on real-time volatility and risk limits.
Performing deep performance attribution and backtesting for investment strategies.
Building automated risk reporting dashboards for quantitative trading systems.