A comprehensive toolkit for calculating essential portfolio risk metrics including Value at Risk (VaR), Expected Shortfall, and risk-adjusted return ratios.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install risk-metrics-calculation
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install risk-metrics-calculation using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Risk Metrics Calculation?
The Risk Metrics Calculation skill provides developers and quantitative analysts with a robust framework for measuring and monitoring financial risk. By integrating this skill via Openclaw Skills, users can automate the computation of volatility, tail risk, and drawdown statistics across various time horizons. It moves beyond basic standard deviation to offer sophisticated measures like Cornish-Fisher VaR and Conditional VaR (CVaR), ensuring a more accurate representation of potential losses in non-normal market conditions.
This implementation is designed for modularity, allowing for individual asset analysis or full portfolio-level risk decomposition. Whether you are building a risk dashboard or a systematic trading agent, these Openclaw Skills offer the mathematical precision required for modern capital preservation and performance attribution.
Risk Metrics Calculation Use Cases
- Measuring real-time portfolio risk and volatility.
- Implementing automated risk limits for trading agents.
- Building comprehensive financial risk dashboards and monitoring systems.
- Calculating risk-adjusted returns for performance benchmarking and attribution.
- Generating regulatory reports for Value at Risk and Expected Shortfall.
- Determining optimal position sizes based on historical drawdowns and volatility.
How Risk Metrics Calculation Works
- Input historical return series for assets or a full portfolio DataFrame into the calculation engine.
- Select the desired risk category, such as Volatility, Tail Risk, or Risk-Adjusted performance metrics.
- Execute specific methods like var_historical or sharpe_ratio to process the underlying return data.
- Analyze portfolio-level contributions to understand marginal risk across different holdings and assets.
- Apply rolling windows or stress test scenarios to evaluate how risk profiles evolve under different market regimes and crises.
Risk Metrics Calculation Setup
To get started with these financial Openclaw Skills, ensure your environment has the necessary quantitative libraries installed.
pip install numpy pandas scipy
Initialize the RiskMetrics class with a pandas Series of returns to begin calculating metrics immediately within your AI agent workflow.
Risk Metrics Calculation Data Schema & Taxonomy
The skill organizes data primarily through specialized Python classes and return types. The following table describes the output structure for a typical risk summary generated by these Openclaw Skills:
| Metric | Type | Description |
|---|---|---|
| var_95_historical | float | Value at Risk at 95% confidence level using historical simulation. |
| cvar_95 | float | Expected Shortfall (Average loss beyond the 95% VaR threshold). |
| sharpe_ratio | float | Annualized risk-adjusted excess return over the risk-free rate. |
| max_drawdown | float | The largest peak-to-trough decline in portfolio value. |
| volatility | float | Annualized standard deviation of returns for general risk assessment. |
Risk Metrics Calculation Advanced Features
- Cornish-Fisher expansion for non-normal distribution VaR calculations to account for skewness and kurtosis.
- Risk parity weighting algorithms designed to create portfolios with equal risk contribution from all assets.
- Multi-scenario historical stress testing covering periods like the 2008 Financial Crisis and the 2020 COVID crash.
- Rolling window analysis to identify shifting volatility regimes and time-varying risk profiles.
- Monte Carlo stress simulations with adjustable volatility multipliers for forward-looking risk assessment.
SKILL.md
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