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 to 25 times 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. (Curious how their numbers compare to other providers? We pulled data from 16 firms to see where they agree and disagree.)

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, optimize, 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 optimization 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. If you're considering adding Bitcoin, our analysis of how much Bitcoin belongs in a portfolio uses the same optimizer to find the data-driven answer.

CFA candidates and finance students

Mean-variance optimization, 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 ($30/month for Basic)

Portfolio Visualizer is the most well-known portfolio analysis tool, and its free tier is genuinely usable — subject to a 15-asset limit and shorter history. Where Portfolio Lab differs: forward-looking J.P. Morgan assumptions as the default inputs (PV optimizes on historical returns by default), five optimization methods with no paywall anywhere, and Monte Carlo with fan charts — all free. Prices checked 16 August 2026.

vs Kwanti (priced for advisory firms)

Kwanti is built for advisors, priced per seat at levels that only make sense inside an advisory practice, and has excellent PDF report generation. Portfolio Lab is free and offers methods Kwanti doesn't — including Black-Litterman and HRP. Kwanti has a better report editor; Portfolio Lab has better optimization 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 optimization 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 optimization, 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 optimization 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 optimized 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 optimized using institutional data, stress-tested with Monte Carlo simulation, and validated against two decades of real returns.