Run 2,000 simulated market paths against your retirement plan. Returns your success probability, plus P10/P50/P90 final balance outcomes. Models sequence-of-returns risk, RMDs, and trad-vs-Roth tax drag honestly.
The 4% rule comes from one Monte Carlo run on US market data 1926–1995. It worked. That doesn't mean it'll work for YOUR portfolio, YOUR allocation, or the NEXT 30 years. A real Monte Carlo simulation runs 2,000 random market paths drawn from a return distribution and tells you how many of those paths leave you with money at the end.
This simulator is the real thing: 2,000 trials, seeded for reproducibility, with mean return + volatility you set explicitly. It models:
Output is a success probability (e.g. "78% of 2,000 trials survive 30 years"), plus the P10 (10th percentile = bad outcome), P50 (median), and P90 (lucky outcome) final balance distributions. The simulator is seeded — same inputs produce the same number every time — so you can iterate without the answer wobbling on you.
Step 1 — Enter your portfolio. Today's starting balance, the split between trad (pre-tax) and "other" (Roth + taxable + cash), and your current asset allocation. The trad share drives the ordinary-income tax drag on withdrawals.
Step 2 — Set your withdrawal. Annual amount in today's dollars (the simulator inflates internally). The 4% rule defaults to 4% × starting balance; you can set it higher or lower.
Step 3 — Set time horizon. Years to retirement + years in retirement. For a 65-year-old expecting to live to 95, that's 0 + 30 years. For a 50-year-old planning to retire at 65 and live to 95, that's 15 + 30 years.
Click "Run Simulation". The simulator runs 2,000 trials in your browser (it's all JavaScript, no server) and reports back in under a second. Same seed produces the same answer every time, so you can change one parameter at a time and see exactly what it does to your success probability.
Enter your portfolio, annual withdrawal, and time horizon. The simulator runs 2,000 random market paths and returns the percentage that survive, plus the distribution of final balances.
Run the simulation →It's the percentage of 2,000 simulated 30-year retirements where your portfolio survives until the end. 78% success means 1,560 of 2,000 trials ended with positive balance; the other 440 ran out of money at some point. 85%+ is generally considered safe; 95%+ is conservative; below 70% is risky enough that you should consider cutting withdrawals or working longer.
The risk that bad market years happen EARLY in your retirement, before your portfolio has had time to grow. Two retirements with identical 30-year average returns can have wildly different outcomes if one starts with the bad years and the other starts with the good ones. Monte Carlo captures this by simulating actual return sequences — some trials happen to draw bad years early, others late.
Because the tax treatment of withdrawals differs and that affects how much you really have to take to spend a given amount. A $50k spend from traditional needs $50k / (1 - 0.22) = ~$64k of gross withdrawal at the 22% bracket. From Roth, it's just $50k. The simulator runs the right tax rate per bucket so the success number reflects real cash-flow needs, not pre-tax.
Starting at age 73, the simulator computes your RMD each year as traditional balance divided by the IRS Uniform Lifetime Table divisor. The RMD is forced (you must take it whether you want to or not), taxed at the ordinary-income rate, and any excess over your annual need is moved to the 'other' bucket (taxable). This often makes RMDs feel like a problem in low-spend retirements — they force taxable income beyond what you need.
Mean return + standard deviation assumptions vary. This simulator defaults to 7% mean / 15% stdev (long-run US equity-heavy assumptions). Other calculators may default to 8/18, 6/12, or use historical-sample-of-real-data instead of normal-distribution sampling. Sequence assumptions and tax rates also vary. The simulator is seeded and reproducible — change one input at a time and you can see exactly what's driving the difference.