Matlab Optimize Portfolio

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.

If the user is already attempting a manual solver approach, acknowledge their work, then show how the Portfolio object achieves the same result with less code and fewer pitfalls.

Step 1: Identify the Formulation

Determine which problem the user is trying to solve:

Consult the relevant formulation file for problem-specific guidance and methods.

Step 2: Determine What Data the User Has

Ask (if not clear) whether they have:

• A matrix of historical asset returns (or prices that need converting)
• Pre-computed mean returns (mu) and covariance matrix (Sigma)
• A risk-free rate (relevant for Sharpe ratio; defaults to 0 if unspecified)

Step 3: Create the Portfolio Object

See reference-core.md for all creation patterns. The most common:

From return statistics:

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p = Portfolio('AssetMean', mu, 'AssetCovar', Sigma);

From historical returns:

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p = Portfolio;
p = setAssetMoments(p, mean(returns)', cov(returns));

Step 4: Set Constraints

Always set constraints. At minimum, use default constraints (fully invested, long-only):

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p = setDefaultConstraints(p);

For other constraint types (bounds, groups, turnover, one-way turnover, cardinality), see reference-core.md.

Step 5: Solve

Use the method appropriate to the formulation (see the formulation file). Common patterns:

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wMinVar = estimateFrontierLimits(p, 'Min');       % minimum-variance
wTarget = estimateFrontierByReturn(p, targetRet); % target-return
wSharpe = estimateMaxSharpeRatio(p);              % max Sharpe ratio
wFrontier = estimateFrontier(p, 20);              % efficient frontier

Step 6: Analyze and Visualize

Use built-in methods for portfolio statistics — never compute them manually:

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portRisk = estimatePortRisk(p, w);
portRet  = estimatePortReturn(p, w);
[risk, ret] = estimatePortMoments(p, w);

Always use plotFrontier as the primary frontier visualization — add custom annotations (special portfolios, CAL line) with hold on/hold off afterward:

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wFrontier = estimateFrontier(p, 20);
plotFrontier(p, wFrontier);
hold on
[risk, ret] = estimatePortMoments(p, wSpecial);
plot(risk, ret, 'r*', 'MarkerSize', 12);
hold off

Common Pitfalls

1. Manual solver usage — quadprog/fmincon for portfolio problems is error-prone; Portfolio handles it.
2. Missing constraints — A Portfolio without constraints is underdetermined.
3. Redundant solves — Pass weights to plotFrontier, not a number of portfolios, if you already solved.
4. Manual risk/return formulas — Use estimatePortRisk, estimatePortReturn, estimatePortMoments.
5. Cardinality/semicontinuous constraints — When calling estimateMaxSharpeRatio, specify 'Method','iterative' (MATLAB auto-selects with a warning if omitted, but explicit is cleaner). Frontier methods (estimateFrontier, estimateFrontierLimits, estimateFrontierByReturn) auto-detect these constraints and select the mixed-integer solver internally — do NOT pass 'Method','iterative' to them.

Tone

Be direct and practical. Show working MATLAB code. If the user provides data, use their actual data. If not, use a small illustrative example so they can see the pattern and adapt.

Reference Materials

• reference-core.md — Portfolio setup, constraints API, visualization (shared across formulations)
• formulation-mean-variance.md — Min-variance, target-return, efficient frontier
• formulation-max-sharpe.md — Max Sharpe ratio / tangency portfolio
• examples.md — Complete runnable examples for all formulations

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