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-aisa-group-skill-inject --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
Calculates portfolio risk metrics including Value at Risk, Expected Shortfall, drawdown analysis, and risk-adjusted performance measures. Extends to both core metrics for individual assets and portfolio-wide risk, with modules for rolling risk metrics and stress testing.
How it works
- Core Risk Metrics (Pattern 1): Implements calculations for volatility, downside deviation, beta, VaR (historical, parametric, and Cornish-Fisher), CVaR, drawdowns, max/average drawdown, drawdown duration, Sharpe, Sortino, Calmar, Omega, and information ratio. Provides a summary aggregating these metrics.
- Portfolio Risk (Pattern 2): Defines a PortfolioRisk class that computes portfolio return, portfolio volatility, marginal risk contribution, component risk, risk parity weights, correlation, diversification, tracking error, and conditional correlation. Uses covariance-based variance and an optimization routine (scipy.optimize.minimize) to derive risk-parity weights.
- Rolling Risk Metrics (Pattern 3): Defines RollingRiskMetrics with rolling volatility, rolling Sharpe, rolling VaR, rolling max drawdown, rolling beta, and volatility regime classification over a moving window.
- Stress Testing (Pattern 4): Defines StressTester with historical crisis scenarios and a method to run historical stress tests using crisis periods and optional portfolio weights to compute total return, max drawdown, worst day, and volatility during crisis.
When to use it
- Measuring portfolio risk
- Implementing risk limits
- Building risk dashboards
- Calculating risk-adjusted returns
- Setting position sizes
- Regulatory reporting
What it can touch
- Tools: claude-code
- Data interfaces: returns as pandas Series or DataFrame, optional benchmark_returns for information ratio
Caveats
- Requires numpy, pandas, scipy for full functionality (implied by code examples).
- Uses annualization factor 252 for returns/volatility scales.
- Risk parity relies on numerical optimization with bound constraints and may require scipy installation.
# 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: Series of periodic 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-aisa-group-skill-inject --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.