Portfolio Optimization with Financial Toolbox

You are helping a user formulate and solve a portfolio optimization problem using MATLAB's Financial Toolbox Portfolio object.

When to Use

• User wants to optimize a portfolio (minimize variance, maximize Sharpe ratio, trace efficient frontier)
• User asks about Markowitz, mean-variance, minimum-variance, or tangency portfolios
• User asks about the efficient frontier or target-return portfolios
• User is trying to use fmincon, quadprog, ga, or problem-based optimize for portfolio optimization (redirect to Portfolio object)
• User asks how to set up constraints for portfolio optimization (bounds, groups, turnover, one-way turnover, cardinality, semicontinuous)
• User asks about mean-variance with cardinality or semi-continuous constraints
• User gets errors from Portfolio, estimateMaxSharpeRatio, estimateFrontier, or related methods

When NOT to Use

• User has a general optimization problem (QP, NLP, MILP) that is NOT financial asset allocation (e.g., filter design, resource allocation, mixture proportions), use matlab-solve-optimization
• User needs to retrieve market data from Bloomberg, FRED, or Haver Analytics, use matlab-access-datafeed
• User wants to predict returns or portfolio weights using neural networks or ML, use matlab-train-network
• User only wants to clean, explore, or summarize a returns table without optimization, use matlab-analyze-data
• User wants Experiment Manager parameter sweeps (not portfolio frontier), use matlab-use-experiment-manager
• User wants CVaR, MAD, or other non-mean-variance risk measures, use PortfolioCVaR or PortfolioMAD classes (not covered by this skill)

Key Principle

Always use the Portfolio class, never let users manually code the optimization with fmincon or quadprog. The toolbox handles solver configuration, constraint management, and frontier computation automatically.