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Historical Stock Market Simulator

Run a portfolio plan through actual historical market periods — the 2008 financial crisis, the 2020 COVID crash, the 2022 bear market, and every recovery in between — instead of a single smoothed average-return assumption.

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Historical simulation vs. average-return assumptions

Many calculators apply one flat expected return — say, 7-10% a year — evenly across every year of a projection. That erases the actual lumpy, uneven path real markets take. This simulator instead replays actual monthly total returns starting from any real historical date the underlying data supports, so the bumps stay in.

The difference shows up most clearly once withdrawals or contributions enter the picture: a flat 8%-a-year assumption treats every year identically, while the real historical path might have delivered -35% one year and +40% the next on the way to a similar long-run average — and when that volatility lands relative to a withdrawal plan changes the outcome substantially.

What “every crash, every bull run” actually covers

Depending on how far back a chosen ticker's history extends, the available data can span the dot-com crash of 2000-2002, the 2008 financial crisis, the 2020 COVID crash, and the 2022 bear market — alongside the recoveries and bull runs between them. Testing a plan against several genuinely different market regimes, not just the friendliest one, is the point.

Why starting month matters as much as strategy

Two investors running an identical strategy, starting even a year or two apart, can end up with meaningfully different outcomes purely from timing. That's what the historical outcomes chart is built to show: every possible historical starting month tested side by side, not just one backtest run picked in advance.

Simulating a single fund vs. a full portfolio

Testing one broad fund alone — the VOO-only preset loaded above, for example — is a useful starting point for understanding how a single asset class behaved across history, without the added variable of an allocation mix. Building out a full multi-holding portfolio afterward shows how combining assets changes that picture, which is the more realistic test for an actual investment plan.

How this differs from a projection

A pure projection applies one assumed constant return going forward — useful for a rough estimate, but it can't show what an actual downturn does to a withdrawal plan mid-stream. This simulator falls back to a flat-rate projection only in the one case where there's no market data to test against: a 100%-cash plan, modeled as a constant-rate savings account rather than a historical backtest.

How to use this simulator

Pick a time period (5 years, 10 years, full history, or a custom start month), build a portfolio by searching any of over 14,000 tickers or starting from a preset, and set a spending or contribution plan. Then check the year-by-year outcomes chart to see how sensitive the results are to exactly when the plan started.

Frequently asked questions

What historical data does this simulator use?

Monthly, dividend-adjusted (total-return) price data covering more than 14,000 stocks and ETFs, so simulations reflect real reinvested dividends alongside price movement.

How far back does the data go?

It varies by ticker — some funds have decades of history, newer ones have less. Choosing “Full history” automatically uses the longest window every selected holding has data in common.

Can I test a specific crash, like 2008 or 2020?

Yes — use the Custom time-period option to start a simulation from a specific month, or click a red bar in the historical outcomes chart to jump straight to a starting year where the plan struggled.

Why do results differ so much by starting month?

Because the order returns happen in — not just their average — determines outcomes, especially once withdrawals are involved. A downturn in the first few years of a plan affects it more than the same downturn happening later, after growth has built up a larger cushion.

Is this the same as a Monte Carlo simulation?

No — Monte Carlo simulation generates randomized hypothetical return sequences from assumed statistics. This tool replays sequences of returns that actually happened historically. See the full comparison on the Monte Carlo vs. historical backtesting page.