risk-metrics-calculation

Use it when:: You're measuring portfolio risk, setting risk limits, or building a risk dashboard.
It does:: Defines each metric and when to use it, then its reference file implements them in Python with pandas and NumPy.
You get:: Risk code along these patterns; it adds no steps or files of its own.
Covers:: Volatility, beta, VaR (historical, parametric, Cornish-Fisher), CVaR, drawdowns, Sharpe/Sortino/Calmar/Omega, and historical, hypothetical and Monte Carlo stress tests.

TL;DR

A reference for portfolio risk measurement, not a workflow. The SKILL.md groups metrics into volatility, tail risk, drawdown and risk-adjusted categories and sets out do's and don'ts: use several metrics, don't lean on VaR alone, assume fat tails rather than normality, watch correlation in stress, and use long lookbacks. A reference file holds the Python: a RiskMetrics class (volatility, downside deviation, beta, three VaR methods, CVaR, drawdown series and durations, Sharpe, Sortino, Calmar, Omega, information ratio and a summary), a PortfolioRisk class (portfolio volatility, marginal and component risk contributions, risk-parity weights, diversification ratio, tracking error), rolling versions over a window, and a StressTester with named historical crises (2008, 2020 COVID, 2022 rate hikes, dot-com, flash crash), hypothetical shocks and Monte Carlo at elevated volatility.

Summarizes the skill at 4bde316.

npx skills add https://github.com/wshobson/agents --skill risk-metrics-calculation

skills.sh page Source on GitHub All skills from wshobson/agents

Weekly Downloads week to Nov 9, 2026

182

-8% in 8 weeks

Fewer weekly downloads than 63% of skills

Weekly values

Week to	Installs
Sep 21	198
Sep 28	220
Oct 5	294
Oct 12	236
Oct 19	196
Oct 26	203
Nov 2	188
Nov 9	182

Total Downloads on skills.sh

11,400

More downloads than 58% of skills

Repo stars wshobson/agents

42,334

More stars than 76% of repos

Age published Jan 20, 2026

9 months

Older than 82% of skills

Category	category:science Research reproduction, financial data, charts and diagrams.
Type	type:guidelines Knowledge and rules the agent applies while it works: best practices, design principles, API references.
Effect	effect:none Adds no side effects of its own. It shapes how the agent thinks, writes or reviews. Guidelines and routers are always effect:none, even when the task they support edits code.
Needs	needs:none Instructions only. Works in any agent that supports skills.
Publisher	by:community Everyone else. Unverified vendor claims default to community.
Topics	finance
Stack	stack:python

SKILL.md

From wshobson/agents at 4bde316. Checked daily, last changed Nov 6, 2026.

Frontmatter

name: risk-metrics-calculation
description: Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.

Risk Metrics Calculation

Comprehensive risk measurement toolkit for portfolio management, including Value at Risk, Expected Shortfall, and drawdown analysis.

When to Use This Skill

Measuring portfolio risk
Implementing risk limits
Building risk dashboards
Calculating risk-adjusted returns
Setting position sizes
Regulatory reporting

Core Concepts

1. Risk Metric Categories

Category	Metrics	Use Case
Volatility	Std Dev, Beta	General risk
Tail Risk	VaR, CVaR	Extreme losses
Drawdown	Max DD, Calmar	Capital preservation
Risk-Adjusted	Sharpe, Sortino	Performance

2. Time Horizons

Intraday:   Minute/hourly VaR for day traders
Daily:      Standard risk reporting
Weekly:     Rebalancing decisions
Monthly:    Performance attribution
Annual:     Strategic allocation

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Best Practices

Do's

Use multiple metrics - No single metric captures all risk
Consider tail risk - VaR isn't enough, use CVaR
Rolling analysis - Risk changes over time
Stress test - Historical and hypothetical
Document assumptions - Distribution, lookback, etc.

Don'ts

Don't rely on VaR alone - Underestimates tail risk
Don't assume normality - Returns are fat-tailed
Don't ignore correlation - Increases in stress
Don't use short lookbacks - Miss regime changes
Don't forget transaction costs - Affects realized risk