Given any set of stocks, PortOpt finds the allocation that maximises return per unit of risk — then proves it holds up in real market conditions.
Instead of staring at an empty text box, start with one of these pre-built scenarios. Each one demonstrates a real portfolio decision and shows you the math behind it.
Compare a concentrated NVDA bet against a Markowitz-optimized tech portfolio. See where concentration risk shows up in the drawdowns.
Run HRP optimization on an All Weather basket, then stress-test it against GFC, COVID, and the 2022 rate shock back to back.
Find the max-Sharpe frontier portfolio from the S&P 500 universe, backtest it walk-forward, and see if it survives transaction costs.
Four steps. No finance PhD required. The math is under the hood — you get the answer.
Type any stock tickers. PortOpt pulls 5 years of adjusted prices automatically via Yahoo Finance.
Pick from Markowitz mean-variance, HRP, CVaR minimization, risk parity, or robust optimization.
The efficient frontier shows every risk/return tradeoff. The star marks the max-Sharpe portfolio.
Run walk-forward backtests, Monte Carlo simulations, bootstrap Sharpe CIs, and crisis period replays.
The efficient frontier maps every possible portfolio in risk/return space. Each dot is a different allocation — the ★ is the one with the best Sharpe ratio.
Classic mean-variance optimization with covariance shrinkage. Traces the full efficient frontier.
Hierarchical Risk Parity uses clustering to build diversified portfolios that don't invert the covariance matrix.
Allocates capital so each asset contributes equally to total portfolio risk. Naturally defensive.
Minimizes Conditional Value-at-Risk — the expected loss in the worst 5% of scenarios.
Ellipsoidal uncertainty sets on expected returns. Finds portfolios that hold up even when your return estimates are wrong.
PortOpt is an open-source quantitative portfolio research tool. It's not financial advice — it's a rigorous framework for asking better questions about risk and return.