Which specific risk metrics are included in this skill?

The skill includes volatility metrics, tail risk (VaR, CVaR), drawdown analysis (Max DD, duration), and risk-adjusted returns (Sharpe, Sortino, Calmar, Omega).

Does it support non-normal return distributions?

Yes, it includes the Cornish-Fisher expansion for Value at Risk, which accounts for skewness and kurtosis in non-normal return distributions.

How can this skill improve my trading bot development?

It provides the logic to implement automated stop-losses, dynamic position sizing based on volatility, and real-time monitoring of portfolio drawdowns.

Can I use this skill for portfolio-level risk management?

Yes, it provides patterns for portfolio-level calculations, including marginal risk contribution, component risk, and risk parity weight optimization.

Quantitative Risk Metrics

Name: Quantitative Risk Metrics
Author: Tahir-yamin

byTahir-yamin

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数据科学与机器学习

Calculates advanced portfolio risk metrics including VaR, CVaR, and risk-adjusted returns for quantitative trading and financial analysis.

关于

The Quantitative Risk Metrics skill is a comprehensive toolkit designed for financial engineers, quants, and developers building trading systems. It provides standardized implementation patterns for measuring volatility, tail risk, and capital preservation through metrics like Value at Risk (VaR), Expected Shortfall (CVaR), and detailed drawdown analysis. Whether you are building real-time risk dashboards, setting automated trading limits, or performing performance attribution for a backtest, this skill provides the mathematical rigor and Python patterns necessary to evaluate portfolio health and risk-adjusted performance effectively.

主要功能

3 GitHub stars
Detailed drawdown analysis including maximum drawdown, average drawdown, and recovery duration.
Statistical distribution analysis including skewness, kurtosis, and conditional correlation during stress periods.
Advanced performance ratios such as Sharpe, Sortino, Calmar, Omega, and Information Ratio.
Comprehensive tail risk measurement including Historical, Parametric, and Cornish-Fisher VaR.
Portfolio-level analytics covering marginal risk contribution and risk parity weighting.

使用场景

Generating regulatory and performance reports for investment portfolios and hedge fund backtests.
Implementing institutional-grade risk limits and position sizing logic in quantitative strategies.
Building automated risk monitoring systems and dashboards for algorithmic trading desks.

关于

主要功能

3 GitHub stars
Detailed drawdown analysis including maximum drawdown, average drawdown, and recovery duration.
Statistical distribution analysis including skewness, kurtosis, and conditional correlation during stress periods.
Advanced performance ratios such as Sharpe, Sortino, Calmar, Omega, and Information Ratio.
Comprehensive tail risk measurement including Historical, Parametric, and Cornish-Fisher VaR.
Portfolio-level analytics covering marginal risk contribution and risk parity weighting.

使用场景

Generating regulatory and performance reports for investment portfolios and hedge fund backtests.
Implementing institutional-grade risk limits and position sizing logic in quantitative strategies.
Building automated risk monitoring systems and dashboards for algorithmic trading desks.