The Free Portfolio Optimizer That Uses J.P. Morgan Data

Why Forward-Looking Assumptions Matter

Most free portfolio tools — and there are plenty of them — use historical returns as inputs. You paste in a few ETF tickers, they pull 10 years of price data, and you get an “optimal” portfolio based on what happened in the past.

The problem is obvious: past returns are a poor guide to future returns. A US large-cap index returned roughly 13% annually over the past decade. Nobody with a straight face would forecast that for the next decade — not with the S&P 500 trading at 20\u201325x forward earnings and a Shiller CAPE above 30.

Forward-looking capital market assumptions (CMAs) solve this. They're built by teams of economists, strategists, and portfolio managers who decompose expected returns into fundamental building blocks: growth, yields, inflation, valuations, and capital flows. J.P. Morgan's LTCMA is the industry standard — now in its 30th annual edition, produced by over 100 investment professionals across the firm.

What Portfolio Lab Does

Portfolio Lab takes J.P. Morgan's forward-looking data and makes it actionable. You get the same assumption set that institutional allocators use, wrapped in tools that let you build, optimise, simulate, and stress-test portfolios.

Who It's For

Independent financial advisors

If you're a solo RIA or small advisory firm, you need institutional-quality tools without institutional costs. Portfolio Lab gives you the same forward-looking data and optimisation methods that the wirehouses use — except it's free and you can show clients exactly how their portfolio was constructed.

Self-directed investors

If you manage your own portfolio and want to move beyond “I read that 60/40 is good”, Portfolio Lab lets you see exactly how your allocation scores on risk-adjusted return, run Monte Carlo simulations on your retirement plan, and understand whether your portfolio is actually diversified or just holds a lot of correlated assets.

CFA candidates and finance students

Mean-variance optimisation, Black-Litterman, risk parity, and HRP are core portfolio theory concepts. Portfolio Lab lets you see them in action with real data — not textbook examples with two assets and a correlation of 0.5.

How It Compares to Alternatives

vs Portfolio Visualizer ($39/month)

Portfolio Visualizer is the most well-known portfolio analysis tool and recently moved most features behind a paywall. Portfolio Lab offers five optimisation methods (PV has three), forward-looking J.P. Morgan assumptions (PV uses historical returns by default), and Monte Carlo with fan charts — all for free.

vs Kwanti ($195+/month)

Kwanti is built for advisors and has excellent PDF report generation. Portfolio Lab is roughly 100% cheaper (free vs $2,340/year) and offers methods Kwanti doesn't — including Black-Litterman and HRP. Kwanti has a better report editor; Portfolio Lab has better optimisation depth.

vs doing it in Excel

You can absolutely build a mean-variance optimizer in Excel. Many people have. But you'll spend weeks getting the covariance matrix right, debugging your solver constraints, and building charts. Portfolio Lab does it in seconds with a UI that doesn't require you to maintain a spreadsheet.

Why It's Free

The short answer: because making it free is the fastest way to build an audience of people who care about portfolio construction.

The longer answer: the portfolio optimisation tools market is fragmented. Expensive institutional platforms on one side, basic free calculators on the other. The gap in the middle — professional-grade tools at accessible prices — is where the opportunity is. By making the core tool free, Portfolio Lab can reach the advisors, investors, and students who would never pay $2,000/year for software but who genuinely need better tools than a backtesting calculator with historical averages.

Premium features (PDF client reports, white-label branding, and consulting services) will come later for those who want them. The core optimisation, simulation, and backtesting engine will stay free.

Getting Started

Portfolio Lab requires a free account (email and password — no credit card, no trial expiry). Once you're in:

1. Pick an optimisation method — Max Sharpe is a good default. It finds the allocation with the highest risk-adjusted return.
2. Review the assumptions — The Returns tab shows J.P. Morgan's expected return for each asset class. Override any assumption you disagree with.
3. Run the optimizer — You'll get an optimal allocation, expected return, volatility, and Sharpe ratio.
4. Simulate forward — Push the optimised weights to Monte Carlo to see the range of outcomes over your investment horizon.
5. Backtest backward — Test against 23 years of real market data to see how the allocation would have performed.

The whole process takes about five minutes. You'll leave with a portfolio that's been optimised using institutional data, stress-tested with Monte Carlo simulation, and validated against two decades of real returns.