Feature · Monte Carlo simulations

Plan for a range of outcomes, not a single line.

2,000 simulated paths over 30 years. See the 10th, 50th, and 90th percentile outcomes for your real portfolio. Sequence-of-returns risk is real — your forecast should reflect that.

Monte Carlo portfolio simulation with confidence band

Why a single "expected return" is dangerous

Most retirement calculators show one line: "at 7% annual return, you'll have $X." Real markets don't deliver 7% every year — they deliver +28%, -18%, +14%, +6%, -22%, etc. The order of those returns matters more than the average. Infnits shows you the full distribution of what could happen.

How our Monte Carlo works

For each holding, we simulate 2,000 paths using historical volatility and dividend behavior. Stocks model price + dividend separately. ETFs use look-through. We then aggregate to your full portfolio and show 10th / 50th / 90th percentile outcomes at each year horizon.

Sequence-of-returns risk, modeled

Two retirees with identical portfolios and identical average returns can end up with very different outcomes if their early years differ. Drawing income from a portfolio in a -20% year sets you back permanently. Infnits flags this risk explicitly and shows when your withdrawals stress the model.

In practice

A real portfolio simulated 30 years out

Take a $500k portfolio invested 70% in equities (VOO + SCHD) and 30% in bonds, withdrawing $30k/yr inflation-adjusted. The Monte Carlo results across 2,000 paths:

90th percentile (best)$3.8M after 30 years
50th percentile (median)$1.4M after 30 years
10th percentile (worst)$340k after 30 years
Probability of running out~7%
Recommended withdrawal cap~$25k/yr (4.0% real)

2,000 paths

Real distribution of outcomes, not a single line. See your 10th, 50th, and 90th percentile portfolio at every year horizon.

Sequence-of-returns aware

Models early-year drawdowns explicitly — the kind that tank a withdrawal-phase portfolio even when average returns look fine.

Per-holding behavior

Stocks, dividend ETFs, and bonds each modeled with their own volatility and dividend characteristics, then aggregated.

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