risk-metrics-calculation
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.
Profile →npx skills add majiayu000/claude-skill-registry --skill risk-metrics-calculation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Implements a comprehensive risk metrics toolkit for portfolios, covering volatility, tail risk (VaR, CVaR), drawdown analysis, and risk-adjusted performance (Sharpe, Sortino, Calmar, Omega). Includes methods for both individual asset-style returns and portfolio-level calculations, with support for rolling metrics and stress testing.
How it works
Addresses three patterns:
- Core Risk Metrics: provides calculations for volatility, downside deviation, beta, VaR (historical, parametric, Cornish-Fisher), CVaR, drawdowns (and max/average), drawdown duration, and risk-adjusted measures (Sharpe, Sortino, Calmar, Omega) plus distribution statistics (skewness, kurtosis).
- Portfolio Risk: operates on a returns DataFrame with optional weights; computes portfolio return, portfolio volatility, marginal and component risk, risk-parity weights, correlation, diversification ratio, tracking error, and conditional correlations.
- Rolling Risk Metrics: outputs rolling volatility, rolling Sharpe, rolling VaR, rolling max drawdown, rolling beta, and volatility regime classification over a rolling window.
- Stress Testing: defines historical crisis scenarios and computes portfolio performance during those periods given crisis data and optional weights.
When to use it
Use when measuring portfolio risk, implementing risk limits, building risk dashboards, calculating risk-adjusted returns, setting position sizes, or regulatory reporting.
What it can touch
Uses the following components and libraries: import numpy as np, import pandas as pd, from scipy import stats, typing Dict, Optional, Tuple. It references a PortfolioRisk and RollingRiskMetrics style classes that operate on pandas DataFrame/Series objects and rely on a SciPy optimizer for risk-parity weights (scipy.optimize.minimize).
Caveats
License is MIT. The approach assumes input data in appropriate pandas structures (Series/DataFrame) and relies on fixed annualization factor 252. Some methods return 0 or inf in degenerate cases (e.g., zero volatility, zero downside) as specified in the implementations.
# 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 ``` ## Implementation ### Pattern 1: Core Risk Metrics ```python import numpy as np import pandas as pd from scipy import stats from typing import Dict, Optional, Tuple class RiskMetrics: """Core risk metric calculations.""" def __init__(self, returns: pd.Series, rf_rate: float = 0.02): """ Args: returns
- When to Use This Skill
- Core Concepts
- 1. Risk Metric Categories
- 2. Time Horizons
- Implementation
- Pattern 1: Core Risk Metrics
- Pattern 2: Portfolio Risk
- Pattern 3: Rolling Risk Metrics
- Pattern 4: Stress Testing
- Quick Reference
- Best Practices
- Do's
- Don'ts
- Resources
What does the risk-metrics-calculation skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill risk-metrics-calculation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.