Calculate VaR, CVaR, and maximum drawdown with Monte Carlo simulations.
npx skills add https://github.com/20YN04/vibe-trading-macos --skill risk-analysis-20yn04
Please help me install this Agent Skill. Skill: risk-analysis Source: https://github.com/20YN04/vibe-trading-macos/tree/main/agent/src/skills/risk-analysis Command: npx skills add https://github.com/20YN04/vibe-trading-macos --skill risk-analysis-20yn04
This Skill addresses the difficulty of accurately measuring financial risk and predicting portfolio performance under extreme market conditions, helping users move beyond simple volatility metrics.
Use the risk-analysis skill to calculate the 95 percent VaR and perform a stress test on the provided portfolio return series.
High-intent search queries and answers about installing and using this skill.
To calculate Value at Risk (VaR) and Expected Shortfall (CVaR), you apply statistical modeling to historical asset return series. These quantitative risk metrics measure potential portfolio losses and identify vulnerabilities beyond simple volatility.
To stress test your portfolio against historical market shocks, you apply historical scenario simulations to your asset return series. This evaluates portfolio resilience by modeling extreme market conditions like a financial crisis or liquidity dry-up.
Yes, you can run Monte Carlo simulations for financial risk analysis using Python libraries like numpy, pandas, and scipy. These dependencies execute the statistical modeling required to simulate asset returns and predict portfolio performance under extreme conditions.
Yes, this risk analysis approach detects fat-tail risks in asset returns by applying extreme value theory and statistical modeling. It identifies severe loss probabilities that standard volatility metrics often miss during sudden market shocks.
The best way to measure maximum drawdown for a trading portfolio is through quantitative analysis of asset return series. This calculates the maximum observed loss from a peak to a trough, highlighting portfolio vulnerabilities before they occur.
Yes, you need numpy, pandas, and scipy to perform Monte Carlo stress testing. These Python dependencies are required to execute the statistical modeling and extreme value theory analysis necessary for accurate financial risk measurement.
galeepFit statistical models and run diagnostics in Python with statsmodels.
archibateCalculate VaR, CVaR, Sharpe, Sortino, and drawdown from return series.
kmshihab7878Computes risk metrics, Monte Carlo portfolios and constrained SLSQP optimizations for trading strategies.
brainbytes-devForecast financial time series price movements using LSTM, Transformer, or CNN-1D deep learning models.
