Monte Carlo FIRE Simulator Calculator

Enter portfolio value, annual spending, expected return, volatility, and retirement years. Tool runs one thousand return path simulations using Box Muller normal random draws from your assumptions. FIRE planners often test one million dollar portfolio with forty thousand annual spending over thirty years. One thousand simulations default balances runtime and stability of success rate estimate. Sub ninety percent success warrants lower spending or higher capital. Sequence of returns risk in first five retirement years appears in left tail of simulation distribution even when median path succeeds comfortably. Review fifth percentile terminal portfolio value not only success rate percentage when judging whether plan survives left tail bad luck return paths in retirement. Fifth percentile terminal wealth outcome matters for conservative planners not only headline success rate percentage from simulation count. FIRE planners often test a $1M portfolio with $40,000 annual spending, 7% expected return, and 15% volatility over 30 years. The success rate shows how often random return paths survive without depletion.

Review success rate, ninety five percent safe withdrawal, percentile outcomes, and median trajectory chart. Sequence risk concentrates in first five retirement years: review fifth percentile outcome for worst plausible paths. Fifth percentile outcome shows stress path for conservative planners. Median path is not guaranteed personal outcome. Increasing simulation count to ten thousand reduces noise in success rate estimate marginally versus one thousand default for speed. Lower annual spending input by ten percent and rerun simulations to see nonlinear improvement in success rate when sequence risk dominates early retirement years. Fifth percentile outcome shows stress path for conservative planners reviewing left tail retirement scenarios not only median success. Ten percent spending reduction rerun often improves success rate nonlinearly when early retirement sequence risk dominates simulation outcomes. Review both success rate percentage and fifth percentile terminal wealth when judging plan resilience against left tail bad luck return paths. Success probability threshold ninety percent common planning standard implies one in ten failure rate acceptable for retiree comfortable reducing discretionary spend during poor paths. Fat tailed return distribution assumption versus normal increases failure rate at same average return because severe drawdowns occur more frequently in empirical equity data. Social security claiming age delay increases guaranteed income floor reducing required portfolio withdrawal rate in Monte Carlo input improving success without higher nest egg target. Guardrail spending rule reduces withdrawal after poor portfolio year and allows increase after strong year keeping real spending smoother than fixed nominal withdrawal that ignores portfolio balance feedback loop in retirement decumulation policy statement for client financial plan deliverable package. Sequence of returns risk matters most in the first five retirement years. A bear market early in withdrawal can permanently impair sustainability even if long run average returns look fine.

In left tail simulation paths, success probability threshold of ninety percent is a common planning standard and implies one in ten failure rate acceptable for retiree comfortable reducing discretionary spend during poor paths. Fat tailed return distribution assumption versus normal increases failure rate at same average return because severe drawdowns occur more frequently in empirical equity data. Social security claiming age delay increases guaranteed income floor reducing required portfolio withdrawal rate in Monte Carlo input improving success without higher nest egg target. Guardrail spending rule reduces withdrawal after poor portfolio year and allows increase after strong year keeping real spending smoother than fixed nominal withdrawal that ignores portfolio balance feedback loop in retirement decumulation policy statement for client financial plan deliverable package. Sequence of returns risk matters most in the first five retirement years. A bear market early in withdrawal can permanently impair sustainability even if long run average returns look fine.

Use Monte Carlo FIRE Simulator whenever inputs change: after market moves, new contributions, or revised personal assumptions. Bookmark the page for quick reruns without installing software.

Example scenario for Monte Carlo FIRE Simulator: $1, $40,000, 7%. Enter those values above to reproduce the walkthrough described in How it works.

Adjust one input at a time to see sensitivity. Monte Carlo FIRE Simulator updates instantly so you can stress test optimistic and conservative assumptions before acting.

Reach for Monte Carlo FIRE Simulator when run statistical return scenarios to estimate safe withdrawal success probability.. It suits quick what if analysis before trades, allocation changes, or plan updates.

Pair with related tools when the decision spans taxes, liquidity, or multi year projections beyond what one formula captures.

Copying outputs without checking input units or stale market prices is a frequent error with Monte Carlo FIRE Simulator. Confirm tickers, percentages, and dates before acting.

Running a single baseline scenario ignores tail risks. Stress test with conservative inputs and compare against related tools listed below when the decision is material.

Normal return draws with seeded RNG for reproducibility. Binary search finds ninety five percent safe withdrawal. Seeded RNG reproducible. Log normal alternative not shown. Seeded reproducible RNG. Log normal alternative may fit equity better than normal draws. Rerun simulations after major life or market assumption changes at least annually in written financial plan review. Educational estimates only not personalized advice consult qualified professional before major financial decisions. Educational estimates only not personalized advice consult qualified professional before major financial decisions. Educational estimates only not personalized advice consult qualified professional before major financial decisions. Log normal returns may fit equity data better than normal. This model uses normal draws for transparency and speed. Monte Carlo FIRE Simulator does not replace personalized advice. Fees, slippage, account specific rules, and behavioral constraints may change real world outcomes.

Compare variable spending with Dynamic SWR Calculator and historical paths with Sequence of Returns on portfolios. Use those calculators when monte carlo fire simulator alone does not capture the full decision.

Internal links on portfolios.tools help you chain calculators: run Monte Carlo FIRE Simulator first, then validate edge cases with a specialized tool from the related section below.