CF Plugin Financial Risk

Portfolio risk scoring calculates Value at Risk (VaR), CVaR, and Sharpe ratios using historical simulation and parametric calculations. This quantifies potential downside risk and evaluates risk-adjusted returns for your holdings.

Compliance reporting automates the generation of regulatory reports for frameworks like Basel III and Dodd-Frank. It structures your portfolio risk metrics and stress testing results to meet specific regulatory workflow requirements.

Machine learning anomaly detection identifies unusual trading patterns and price movements within your financial data. It automatically flags outliers in historical data to help pinpoint irregular market activities.

Stress testing assesses portfolio performance under adverse historical and hypothetical scenarios. By simulating severe market downturns, you can quantify potential losses and understand downside risks before they occur.

Market regime classification determines current market conditions such as bull or bear states. It analyzes historical data trends to categorize the market environment, enabling better risk assessment and portfolio adjustments.

Historical data is required to execute portfolio risk score calculations and historical simulations. You must provide adequate historical market data to accurately compute metrics like VaR, CVaR, and detect trading anomalies.