{ "prompt_metadata": { "prompt_id": "Portfolio_Optimization_Proposal_v1", "prompt_version": "1.0", "creation_date": "YYYY-MM-DD", "description": "A prompt to generate a portfolio optimization proposal based on specified investor objectives, constraints, and a given asset universe.", "author": "Jules - AI Software Engineer" }, "report_specifications": { "report_title": "Portfolio Optimization Proposal for [Client Name/ID]", "client_name_id": "[Specify Client Name or ID, or 'Model Portfolio']", "proposal_date": "[Specify Date of Proposal, e.g., YYYY-MM-DD]", "target_audience": "Investment Advisors, Portfolio Managers, Retail Investors (with advisory context), Robo-advisors (as input)", "output_format": "Markdown with structured sections, including tables for asset allocation.", "tone_and_style": "Professional, analytical, client-focused, clearly explaining rationale and methodology." }, "input_parameters": { "section_id": "investor_profile_and_objectives", "section_title": "Investor Profile and Objectives", "instructions": "Define the investor's characteristics, goals, and constraints. This section would be filled with specific client data before running the prompt.", "key_considerations": [ { "parameter": "investment_objective", "description": "Primary goal of the portfolio.", "example_values": ["Capital Appreciation", "Wealth Preservation", "Income Generation", "Balanced Growth and Income", "Speculation"] }, { "parameter": "risk_tolerance", "description": "Investor's willingness and ability to take risk.", "example_values": ["Conservative", "Moderately Conservative", "Moderate", "Moderately Aggressive", "Aggressive"] }, { "parameter": "time_horizon", "description": "Expected duration of the investment.", "example_values": ["Short-term (0-2 years)", "Medium-term (3-7 years)", "Long-term (7+ years)"] }, { "parameter": "liquidity_needs", "description": "Anticipated need for cash withdrawals.", "example_values": ["Low", "Medium", "High", "Specific annual withdrawal %/amount"] }, { "parameter": "investment_constraints", "description": "Any restrictions on investments.", "example_values": ["ESG preferences", "Exclusion of specific sectors/assets (e.g., tobacco, weapons)", "Maximum allocation to a single asset/sector", "Tax considerations"] }, { "parameter": "current_portfolio_value", "description": "Current market value of assets to be optimized (if applicable).", "example_values": ["$USD Amount"] }, { "parameter": "asset_universe", "description": "List of investable assets or asset classes to consider for the optimized portfolio. Could be a list of tickers, asset class names, or reference to a predefined model portfolio (e.g., from `data/example_user_portfolio.json`).", "example_values": [ "['SPY', 'AGG', 'GLD', 'EFA', 'EEM']", "['US Large Cap Equity', 'Global Fixed Income', 'Real Estate', 'Commodities']", "Reference: `data/sp500_ai_overviews.jsonl` (for stock selection within S&P500)" ] }, { "parameter": "optimization_model_preference", "description": "Preferred optimization model or objective function.", "example_values": ["Mean-Variance Optimization (Maximize Sharpe Ratio)", "Risk Parity", "Minimum Variance", "Maximize Expected Return for a Given Risk Level", "Black-Litterman"] } ] }, "core_analysis_areas": [ { "section_id": "executive_summary_proposal", "section_title": "Executive Summary of Proposal", "instructions": "Provide a concise overview of the proposed optimized portfolio, its alignment with investor objectives, and key expected characteristics.", "key_considerations": [ "Restatement of key investor objectives and risk tolerance.", "Summary of the proposed asset allocation.", "Expected portfolio return and risk (e.g., standard deviation, Sharpe ratio) based on the optimization.", "Key changes from current allocation (if applicable).", "Brief rationale for the proposed strategy." ] }, { "section_id": "methodology_and_assumptions", "section_title": "Optimization Methodology and Assumptions", "instructions": "Describe the portfolio optimization approach used and any key assumptions made.", "key_considerations": [ "Description of the chosen optimization model (e.g., Mean-Variance, Black-Litterman).", "Source of expected returns, risk (volatility), and correlation estimates for asset classes/assets. (This might involve referencing data from `data/adam_market_baseline.json` or models from `core/analysis/`).", "Time period used for historical data if applicable.", "Any constraints applied during the optimization process (e.g., max/min weights per asset).", "Software or tools used for optimization (if relevant, e.g., Python libraries like PyPortfolioOpt, internal simulation tools)." ] }, { "section_id": "proposed_optimized_portfolio", "section_title": "Proposed Optimized Portfolio Allocation", "instructions": "Detail the asset allocation of the optimized portfolio.", "key_considerations": [ "Table showing: Asset Class/Asset, Proposed Weight (%), Ticker/Identifier (if applicable).", "Visual representation (e.g., pie chart) of the allocation.", "Breakdown by geography, sector, or other relevant dimensions if applicable." ] }, { "section_id": "portfolio_characteristics_expected_performance", "section_title": "Portfolio Characteristics and Expected Performance", "instructions": "Analyze the expected risk and return characteristics of the optimized portfolio.", "key_considerations": [ "Projected annualized return.", "Projected annualized volatility (standard deviation).", "Projected Sharpe Ratio (or other relevant risk-adjusted return metric).", "Comparison of these metrics to a relevant benchmark or current portfolio (if applicable).", "Potential downside risk (e.g., VaR, CVaR, max drawdown based on backtesting or simulation if performed).", "Stress testing results against specific market scenarios (Optional, could reference `prompts/market_shock_scenario_analysis.json` logic)." ] }, { "section_id": "alignment_with_investor_objectives", "section_title": "Alignment with Investor Objectives", "instructions": "Explain how the proposed portfolio meets the investor's stated goals and constraints.", "key_considerations": [ "How the allocation addresses the primary investment objective (e.g., growth, income).", "How the risk level of the portfolio aligns with the investor's risk tolerance.", "Consideration of time horizon and liquidity needs.", "Adherence to any specific investment constraints (ESG, exclusions)." ] }, { "section_id": "implementation_recommendations", "section_title": "Implementation Recommendations", "instructions": "Suggest steps for implementing the proposed portfolio.", "key_considerations": [ "Specific investment vehicles to use (e.g., ETFs, mutual funds, individual stocks/bonds).", "Rebalancing strategy (frequency, thresholds).", "Tax implications of reallocating (if applicable, high-level).", "Phased implementation plan if significant changes from current portfolio." ] }, { "section_id": "risks_and_limitations", "section_title": "Risks and Limitations", "instructions": "Clearly outline the risks associated with the proposed portfolio and the limitations of the optimization process.", "key_considerations": [ "General market risks.", "Specific risks related to the chosen asset classes or strategy.", "Limitations of historical data in predicting future performance.", "Sensitivity of the optimal portfolio to input assumptions (expected returns, correlations).", "Statement that past performance is not indicative of future results." ] }, { "section_id": "monitoring_and_review", "section_title": "Monitoring and Review", "instructions": "Outline how the portfolio's performance and alignment with objectives will be monitored.", "key_considerations": [ "Recommended frequency of portfolio reviews.", "Key metrics to track.", "Conditions under which a portfolio review or re-optimization might be triggered (e.g., significant market changes, changes in investor circumstances)." ] } ], "data_requirements": [ "Investor-specific data (objectives, risk tolerance, constraints - as defined in `input_parameters`).", "Historical market data for the asset universe (prices, returns, volatility, correlations).", "Capital market assumptions (CMAs) for expected returns, risk, and correlations if using forward-looking inputs.", "Details of existing portfolio holdings if re-optimizing an existing portfolio.", "Information on available investment vehicles (ETFs, mutual funds) for implementation." ], "expert_guidance_notes": [ "The 'input_parameters' section is crucial and would need to be populated with actual client data before the prompt is executed by an AI.", "Ensure that all projections (return, risk) are clearly labeled as estimates and are subject to uncertainty.", "The level of detail in implementation recommendations may vary based on whether the AI is purely analytical or also has an advisory/execution role.", "If using specific optimization algorithms, it can be helpful to briefly explain the rationale for choosing that algorithm in the methodology section.", "Compliance and regulatory disclosures are critical in real-world client proposals and should be added as per jurisdictional requirements (outside the scope of this AI generation prompt but important context)." ] }