risk-metrics-calculation

To calculate VaR and CVaR for a multi-asset portfolio, load your periodic returns into a pandas Series and use the RiskMetrics class. Calling the summary method outputs a comprehensive risk report including tail risk metrics.

Sharpe, Sortino, and Calmar ratios are risk-adjusted return metrics measuring portfolio performance per unit of risk. This skill computes all three simultaneously alongside volatility and drawdown measures for consistent performance attribution analysis.

You can compute maximum drawdown and risk-adjusted returns for single-asset investments by passing periodic returns into the skill. It processes single-asset inputs identically to multi-asset portfolios for continuous risk monitoring.

Yes, the portfolio risk metrics calculation supports stress-testing and rolling windows through extension. The base implementation leverages numpy, pandas, and scipy for statistical calculations that can be expanded for dynamic risk monitoring.

Yes, you can use pandas and numpy outputs for regulatory reporting and risk budgeting workflows. The skill consolidates volatility, tail risk, and drawdown metrics into a single framework for consistent risk analysis across finance operations.

The best way to consolidate fragmented portfolio risk assessments is running the metrics summary function. It unifies volatility, tail risk, drawdown, and risk-adjusted measures into one comprehensive report for consistent analysis.