How do I simulate a Monte-Carlo forecast?

Open this page and use the live FIRE Calculator tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.

Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.

Can I use my own numbers?

Absolutely — every input is adjustable, and with data import you can drive a Monte-Carlo forecast from your own measurements.

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FIRE Calculator for a Monte-Carlo forecast

Simulate a Monte-Carlo forecast live in your browser. This runs the real FIRE Calculator solver — adjust the inputs, watch it respond instantly, and export the result. No install, no account.

For:Students Researchers Educators Engineers Hobbyists & Makers K-12 Students First Responders

Compound Interest

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Compound Interest & InvestingLive

the eighth wonder

Controls

Presets

Starting amount ($)

Monthly contribution ($)

Annual return

Years

Compounding means earning returns on your past returns, so growth accelerates over time. The gap between the balance line and your total contributions is pure compound interest — and it widens dramatically in the final years. Educational tool, not investment advice.

▶ Run in Python

Data Inspector

Final balance$691,150

Total contributed$190,000

Interest earned$501,150

Governing equation

A = 10, 000 (1 + \frac{0.070}{12})^{360} + 500 \cdot \frac{( 1 + \frac{0.070}{12} ) ^{360} - 1}{0.00583} = $691, 150

Reading this result: Rule of 72: at 7.0% a year, money roughly doubles every 10.3 years — about 2 doublings across your 30-year horizon. Each $500/mo contribution then earns returns on its own past returns, so the balance curve pulls away from your flat contribution line. Because compounding is exponential, most of the $501,150 in interest is created in the final years — time in the market matters far more than the amount. Educational tool, not investment advice.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Monte Carlo

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Monte Carlo Price SimulationLive

geometric Brownian motion

Controls

Presets

Initial price S₀

Expected return μ

Volatility σ

Horizon (years)

Geometric Brownian motion models a price with constant drift and random volatility — the assumption behind Black-Scholes. Running hundreds of simulated paths reveals the full distribution of outcomes, not just an average. Educational tool, not investment advice.

▶ Run in Python

Data Inspector

Median outcome$0

5th percentile$0

95th percentile$0

Governing equation

S_{t + Δ t} = S_{t} e^{(0.07 - \frac{0. 2 ^{2}}{2}) Δ t + 0.2 Δ t Z}, \tilde{S}_{1} \approx $0

Reading this result: Median outcome is about $0, just under the drift-only $107 — GBM compounds in log-space, so volatility always pulls the typical path below the average.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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About this simulation

The full FIRE Calculator tool models a Monte-Carlo forecast with the same numerics engineers and scientists use — running entirely client-side. Change any parameter and the result updates in real time, so you can build intuition, check a design, or teach the concept without spreadsheets or installs.

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Other ways to simulate a Monte-Carlo forecast

Black-Scholes for a Monte-Carlo forecast Option Payoff for a Monte-Carlo forecast Monte Carlo (GBM) for a Monte-Carlo forecast Efficient Frontier for a Monte-Carlo forecast Value at Risk for a Monte-Carlo forecast Bond Pricing for a Monte-Carlo forecast

Frequently asked questions

How do I simulate a Monte-Carlo forecast?: Open this page and use the live FIRE Calculator tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.
Is it free?: Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.
Can I use my own numbers?: Absolutely — every input is adjustable, and with data import you can drive a Monte-Carlo forecast from your own measurements.