Efficient Frontier

The idea that won Markowitz a Nobel Prize: mixing imperfectly correlated assets can cut risk without cutting return. Trace the frontier and find the sweet spot.

Efficient FrontierLive

Markowitz portfolio theory

Controls

Presets

Asset A return

Asset A volatility

Asset B return

Asset B volatility

Correlation

Combining two assets traces a curved frontier of risk versus return. Because they are not perfectly correlated, some mixes have lower risk than either asset alone — the power of diversification. The pink point is the minimum-variance portfolio. Educational tool, not investment advice.

▶ Run in Python

Data Inspector

Min-var weight A0%

Min-var return0.0%

Min-var volatility0.0%

Governing equation

σ_{p}^{2} = w^{2} σ_{A}^{2} + (1 - w)^{2} σ_{B}^{2} + 2 w (1 - w) ρ σ_{A} σ_{B}, ρ = 0.20, w^{*} = 0%

Reading this result: The assets differ sharply in volatility, so the minimum-variance blend leans heavily toward the calmer one to hold total risk down.

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How it works

Modern Portfolio Theory plots portfolios in risk-return space. As you vary the weights of two assets, their combinations sweep out a curved frontier; because the assets are not perfectly correlated, some mixes carry less risk than either asset alone. The leftmost point is the minimum-variance portfolio. Educational tool, not investment advice.

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Frequently asked questions

Is this efficient frontier portfolio tool really free?▾

Yes. Efficient Frontier runs entirely in your browser using your device's own compute, so local use is free forever. You only pay Compute Tokens if you scale a job to the cloud.

Do I need to install anything?▾

No. Everything runs client-side in a modern browser — no downloads, no license, no account required to start.

Can I save or share my simulation?▾

Create a free account to save projects, and use a shareable embed or minted DOI to publish a live, interactive version anywhere.

How accurate are the results?▾

The solver uses established numerical methods, but results are for research and educational purposes and should be validated against experiment or professional review before you rely on them.