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FIRE Monte Carlo Simulator

Test your FIRE plan with Monte Carlo and rolling historical retirement simulations using stocks, bonds, cash, inflation, and flexible withdrawal strategies.

Retirement Plan

Starting portfolio

Starting portfolio

Allocation

Allocation
Portfolio mix
Stock geography

Withdrawals

Withdrawals
Advanced Settings
Annual fees
Extra cash flows

No extra withdrawals or income added.

Simulation Settings

Add extra withdrawal

Amount is per year. Use the same start and end year for a one-time item.

Interpret your FIRE simulation

The success rate is the share of simulated paths that do not run out of money before the selected duration ends. It is a stress test built from historical market and inflation years, not a forecast.

Inputs that matter most

  • Portfolio value: the investable balance at the start of retirement.
  • Stock, bond, and cash allocation: controls how each sampled market year affects the portfolio.
  • Stock geography: splits the stock sleeve between U.S. large caps and international stocks.
  • Annual spending: the first-year withdrawal before the chosen strategy adjusts it.
  • Withdrawal strategy: determines whether spending follows inflation, stays fixed, floats with the portfolio, or uses dynamic rules such as Guyton-Klinger, Vanguard dynamic spending, Endowment, or VPW.

Common mistakes

  • Treating a high success rate as a guarantee.
  • Ignoring taxes, fees, account location, and Social Security or pension income.
  • Using a spending number that excludes major irregular expenses.
  • Assuming rebalancing and spending behavior will be easy during deep drawdowns.

When this estimate can be misleading

The model can be misleading when future returns, inflation, taxes, fees, lifespan, or personal spending shocks differ materially from the historical samples.

Scenarios to try

  • Lower spending by 10% and rerun the simulation.
  • Compare annual rebalancing with drifting allocations.
  • Try 30, 40, and 50 year durations.
  • Compare inflation-adjusted withdrawals against dynamic spending rules.

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How to use this FIRE Monte Carlo simulator

Use this simulator to stress test a retirement or financial independence plan against historical market and inflation sequences.

  1. Enter your starting portfolio value, duration, asset allocation, and stock geography.
  2. Set annual spending and choose the withdrawal strategy you want to test.
  3. Open advanced settings to adjust rebalancing, fund fees, and extra income or withdrawals.
  4. Choose a simulation mode and outcome count, then run the simulation to review success rate, percentiles, failures, and balance paths.

FIRE Monte Carlo Simulator features

  • Run bootstrap Monte Carlo retirement simulations from historical annual data.
  • Run block bootstrap simulations using consecutive historical chunks.
  • Run historical rolling-period simulations for deterministic sequence testing.
  • Model U.S. stocks, international stocks, bonds, cash, and inflation.
  • Set stock, bond, and cash allocation targets.
  • Split the stock sleeve between U.S. and international stocks.
  • Choose inflation-adjusted, fixed, percent-of-portfolio, Guyton-Klinger, Vanguard dynamic spending, Endowment, or VPW withdrawals.
  • Add extra income or extra withdrawals with start and end years.
  • Adjust annual fees and rebalancing frequency.
  • Compare success rate, median ending balance, downside percentiles, and failure timing.
  • Review portfolio, spending, downside, and failure tabs.
  • Chart simulated balance paths and median trend in starting-year dollars.

How the Monte Carlo simulation works

The simulator resamples real annual return and inflation observations, then applies the selected withdrawal rule year by year.

Sampled portfolio return
R_{p,t} = w_s R_{s,t} + w_b R_{b,t} + w_c R_{c,t} - f

Each sampled year blends U.S. and international stock returns inside the stock sleeve, then combines stocks, U.S. bonds, and cash using the selected allocation weights before subtracting annual fees.

The historical sample includes U.S. stock, international stock, U.S. bond, cash, and CPI inflation observations. International stock observations begin in 1970, so simulations with any international stock allocation use the overlapping years where that field is available.

Bonds remain U.S.-only. The stock geography split blends U.S. and international stock returns inside the stock sleeve before the overall stock, bond, and cash allocation is applied.

Bootstrap Monte Carlo randomly samples one historical year at a time with replacement. Historical rolling periods use each actual consecutive window available for the selected duration, so higher durations produce fewer simulations. Block bootstrap randomly samples consecutive historical chunks, then stitches those blocks together with replacement.

Each simulated year starts with the planned spending and any extra cash flows. Withdrawals are taken before that year's sampled returns, annual fees are applied to each asset sleeve after returns, and inflation is applied at the end of the year before the next year's inflation-adjusted spending is calculated.

When annual rebalancing is on, the portfolio return is the weighted stock, bond, and cash return for that sampled year. When rebalancing is off, each sleeve grows independently, withdrawals are taken proportionally, and allocations drift.

Scheduled rebalancing happens after the year's return, fee, withdrawal, cash-flow, and inflation steps. If the portfolio is depleted, the simulation records the unpaid withdrawal as a negative balance so shortfalls remain visible instead of stopping at zero.

All chart values are displayed in starting-year dollars by deflating nominal balances by cumulative sampled inflation. If a path depletes, the line continues below zero to show cumulative unmet withdrawals.

The embedded U.S. stock, Treasury bond, Treasury bill, and inflation history follows New York University Stern's historical returns dataset. International developed-market returns use the MSCI index data, and CPI terminology follows the U.S. Bureau of Labor Statistics.

The flexible withdrawal options are simplified implementations informed by the Financial Planning Association's discussion of Guyton-Klinger decision rules and Vanguard's retirement-income principles.

FIRE Monte Carlo FAQ

Does this use real market data?
Yes. The simulator embeds annual stock, bond, cash, and inflation observations. Bonds remain U.S.-only; international stock observations are available beginning in 1970.
Why do results change each time?
Bootstrap and block bootstrap runs draw new random historical years or blocks with replacement, so the order and mix of returns and inflation changes. Historical rolling periods are deterministic.
What does percent of portfolio mean?
The first-year spending amount is divided by the starting portfolio value, then that percentage is withdrawn from the current portfolio each simulated year.

Read probability as a planning signal

A success rate is the share of modeled paths that finish under the selected rule. It is not the probability that one exact future will occur. Results depend on return data, inflation, fees, asset mix, withdrawal timing, and the chosen success threshold. Small input changes can create a large change over a long retirement.

Compare several plans instead of searching for a magic percentage. Try lower starting spending, a later retirement date, higher cash needs, and a poor early market sequence. The SEC's Investor.gov guidance explains that investments involve risk and that diversification cannot guarantee against loss.

Revisit the simulation as spending and account balances change. Taxes, health costs, Social Security choices, and account-access rules may need separate planning. Use the FIRE milestone calculator for a simpler savings target, then use this simulation to test how that target behaves across many return sequences.

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