Free online Monte Carlo Simulator — model investment return uncertainty through thousands of random simulations. Visualize best/worst-case scenarios and probability distributions. Pure frontend computation, your data stays private. | No sign-up · Data never uploaded
Zero Dependencies · Works OfflineMonte Carlo simulation is a random sampling method widely used in financial risk assessment, retirement planning, project management, and scientific research. This tool simulates thousands of market scenarios to help you visualize the potential risk and return distribution of your investment portfolio.
Enter the initial amount you plan to invest. This serves as the starting point for all simulations. If you already have a portfolio, use its current market value.
This is the average annual return you expect. Historical reference: S&P 500 long-term average ~8-10%, bonds ~4-5%, savings ~2-3%. Use conservative estimates for more reliable results.
Volatility measures return uncertainty (standard deviation). Higher volatility means greater risk and wider outcome dispersion. Stocks typically 15-20%, bonds 3-5%, crypto can exceed 50-100%.
1,000 for quick preview, 5,000 for stable results, 10,000 for highest precision. More simulations take slightly longer but typically complete within seconds.
The chart shows multiple simulated paths and the final value distribution. The median represents the 50th percentile; P90 means only 10% of scenarios exceed this value; P10 means only 10% fall below it. Use these metrics for informed investment decisions.
Retirement Planning: With $500K in retirement savings, 7% expected return, 12% volatility, and a 30-year withdrawal period, Monte Carlo can estimate the probability of outliving your savings under various market conditions.
Portfolio Evaluation: Compare asset allocations (e.g., 60/40 stocks/bonds vs 80/20). Simulate each with their expected returns and volatilities to compare long-term return ranges and downside risks.
Education Fund Planning: Starting with $200K when a child is born, 6% expected return, 10% volatility — simulate to estimate the fund's possible range in 18 years, helping determine if current savings are sufficient.
Risk Assessment: Financial advisors use Monte Carlo to show clients "worst-case" possibilities. If an investment has a 5% probability of loss, clients can judge whether they can tolerate that risk level.
Named after the Monte Carlo Casino in Monaco, this technique was pioneered by mathematician Stanislaw Ulam at Los Alamos National Laboratory in the 1940s. The core principle: when a problem is too complex for deterministic mathematical solutions, use massive random sampling to estimate the probability distribution of outcomes. It's one of the most widely used risk assessment methods in finance.
This simulator assumes annual returns follow a log-normal distribution — the most common assumption in financial modeling. While real markets exhibit "fat tails" (extreme events occur more frequently than the normal distribution predicts), the log-normal model provides a reasonable first-order approximation.
A simple compound interest calculator tells you "if the return is exactly 8%, you'll have X dollars in 10 years." But markets fluctuate — returns vary year to year. Monte Carlo accounts for this uncertainty, telling you "there's a 50% chance you'll have more than Y, a 10% chance more than Z, and a 10% chance less than W." This is the information you actually need for decision-making.
This tool uses simplified mathematical models. Results are for reference only and do not constitute investment advice. Real markets are more complex than models. Past performance does not guarantee future results. Consult a professional financial advisor before investing.
Accuracy depends on two factors: simulation count and input parameters. 5,000 simulations are usually sufficient for stable results. More importantly, accuracy hinges on reasonable inputs — if your expected return or volatility estimates are off, results will be too. Use conservative parameter estimates.
Volatility varies by asset class. S&P 500 historical: ~15-18%, CSI 300: ~20-25%, global bonds: typically 3-5%. When unsure, use slightly higher volatility than historical data for more conservative estimates.
This is normal for Monte Carlo — each run uses random numbers to generate market paths. With enough simulations (5,000+), results between runs should be very close (usually within 1-2%). If you need fully reproducible results, note your parameters and run at least 5,000 simulations.
Completely. All calculations — including thousands of Monte Carlo simulations — run locally in your browser. Your investment amounts, return expectations, and other sensitive data never leave your device. The tool works offline too.
P90 (90th percentile): only 10% of simulated scenarios exceed this value — an optimistic outcome. P10 (10th percentile): only 10% of scenarios fall below this value — a pessimistic outcome. Together they show the full range of possible investment outcomes.