mcp-optimizer

# Portfolio Optimization - Usage Examples ## Description Portfolio optimization helps find the optimal distribution of investments across different assets to achieve the best risk-return ratio. ## Example Prompts for LLM ### Example 1: Basic Portfolio Optimization ``` Help me optimize my investment portfolio using MCP Optimizer. I have $100,000 to invest in the following assets: Assets: - Apple Stock: expected return 12%, risk (standard deviation) 18% - Government Bonds: expected return 6%, risk 3% - Tesla Stock: expected return 15%, risk 25% - Gold: expected return 8%, risk 12% Requirements: - Maximum acceptable portfolio risk: 15% - Minimum expected return: 10% Find the optimal allocation to minimize risk. ``` ### Example 2: Diversified Portfolio ``` Use MCP Optimizer to create a diversified portfolio. Budget: $50,000 Available assets: - US Stocks: return 14%, risk 20% - International Stocks: return 11%, risk 16% - Corporate Bonds: return 8%, risk 5% - Government Bonds: return 5%, risk 2% - Real Estate (REITs): return 9%, risk 10% - Commodities: return 7%, risk 15% Constraints: - No more than 30% in any single asset - Minimum 10% in government bonds - Maximum 40% in stocks (US + International) - Risk tolerance: 12% Maximize expected return while meeting constraints. ``` ### Example 3: Retirement Portfolio ``` Help create a conservative retirement portfolio with MCP Optimizer. Investment amount: $200,000 Investment horizon: 15 years until retirement Investment options: - Large Cap Stocks: return 10%, risk 15% - Treasury Bonds: return 6%, risk 3% - Corporate Bonds: return 8%, risk 6% - Bank CDs: return 4%, risk 1% - Index Funds: return 9%, risk 12% Requirements: - Maximum portfolio risk: 8% - Minimum 20% in risk-free assets (CDs + Treasury) - Maximum 40% in stocks - Target return: at least 7% Find optimal allocation to meet goals. ``` ### Example 4: Aggressive Growth Portfolio ``` Create an aggressive growth portfolio using MCP Optimizer. Capital: $30,000 Goal: maximum growth over 5 years Investment opportunities: - Tech Stocks: return 20%, risk 30% - Emerging Market Stocks: return 18%, risk 28% - Cryptocurrency: return 25%, risk 40% - Venture Capital Funds: return 22%, risk 35% - High-Yield Bonds: return 12%, risk 15% - Commodity Futures: return 15%, risk 25% Constraints: - Maximum portfolio risk: 25% - Maximum 20% in cryptocurrency - Minimum 10% in bonds for stability - No more than 25% in any single asset Maximize expected return. ``` ## Request Structure for MCP Optimizer ```python # Example function call result = optimize_portfolio( assets=[ {"name": "Asset1", "expected_return": 0.12, "risk": 0.18}, {"name": "Asset2", "expected_return": 0.08, "risk": 0.10}, {"name": "Asset3", "expected_return": 0.15, "risk": 0.25} ], objective="minimize_risk", # or "maximize_return" budget=100000, risk_tolerance=0.15, min_return=0.10, constraints={ "max_weight_per_asset": 0.30, "min_bonds": 0.20, "max_stocks": 0.60 } ) ``` ## Typical Activation Phrases - "Optimize my investment portfolio" - "Find optimal asset allocation" - "Minimize risk for given return" - "Maximize return with limited risk" - "Create a diversified portfolio" - "Help with investment distribution" ## Optimization Strategies 1. **Risk Minimization** - for a given target return 2. **Return Maximization** - with limited risk level 3. **Sharpe Ratio Maximization** - best risk-return ratio 4. **Balanced Portfolio** - equilibrium across all assets ## Applications Portfolio optimization is used for: - Personal investments - Retirement savings - Fund asset management - Corporate investments - Insurance reserves