portfolio-management

Purpose

This skill enables the AI to manage investment portfolios using quantitative models, calculate risk metrics like Value at Risk (VaR), and apply optimization algorithms such as mean-variance optimization. It processes portfolio data to generate actionable insights, supporting decisions in finance by integrating with data sources and executing trades based on predefined strategies.

When to Use

Use this skill for tasks involving portfolio rebalancing, risk assessment, or performance analysis in financial contexts. Apply it when handling user queries about investment strategies, such as diversifying assets or responding to market volatility. Ideal for scenarios with real-time data feeds or when optimizing allocations under constraints like budget limits.

Key Capabilities

Quantitative Models: Implement models like CAPM or Black-Litterman; e.g., calculate expected returns with claw portfolio model capm --assets AAPL,GOOG.
Risk Metrics: Compute VaR or Sharpe ratio; use API endpoint GET /api/portfolios/risk/var?confidence=0.95 to get 95% VaR for a portfolio.
Optimization Algorithms: Run mean-variance optimization; configure via JSON file: {"assets": ["AAPL", "MSFT"], "weights": [0.5, 0.5]}.
Data Integration: Pull market data from external APIs; supports formats like CSV or JSON for portfolio inputs.
Performance Tracking: Generate reports on portfolio returns; e.g., claw portfolio track --period monthly --metric sharpe.

Usage Patterns

Always initialize with authentication via $PORTFOLIO_API_KEY environment variable. For CLI usage, pipe data inputs directly; e.g., start with claw portfolio load --file portfolio.json then chain commands like claw portfolio optimize --risk-level high. In API patterns, use POST requests for modifications and GET for queries; handle asynchronous operations by polling endpoints. For scripts, wrap in try-catch blocks to manage API failures, and use config files for reusable parameters like asset lists.