Monte Carlo Investment Simulator
Simulate thousands of random return paths to see a range of possible outcomes for your investment. Educational only, not trading or investment advice.
Quick answer
A Monte Carlo projection simulates many random monthly return paths. Each month value = value x e^(monthly log return) + contribution, with volatility scaled by sqrt(12). Results show percentiles of final value.
Monte Carlo Investment Simulator
Expected arithmetic return per year.
Standard deviation of yearly returns.
Same seed, same results.
Median Final Value
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10th Percentile
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90th Percentile
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25th – 75th Percentile
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Total Contributed
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Chance of Reaching Target
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Educational only. This is not trading or investment advice, and simulated results are not predictions.
Percentiles by Year
| Year | 10th (₹) | 25th (₹) | Median (₹) | 75th (₹) | 90th (₹) |
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Assumptions & method
- Monthly log returns are independent and identically distributed (i.i.d.) normal draws, generated with the Box-Muller transform from a seeded mulberry32 generator.
- Annual log-return volatility σ = your volatility input; monthly σ = σ ÷ √12. Monthly mean log return = (ln(1 + expected return) − σ²/2) ÷ 12, so the expected arithmetic return matches your input.
- Each month: value = value × e^(monthly log return) + contribution. Contributions are added at the end of each month, after that month's return.
- Percentiles use linear interpolation between sorted simulated values. Probability of reaching the target = share of paths ending at or above it.
- Not modelled: fat tails, volatility clustering, serial correlation, fees, taxes, inflation, withdrawals or changes in contributions. The model is educational and not trading or investment advice.
Results are estimates based on the figures you enter. They are not guarantees and not financial advice.
Why look at a range instead of one number?
A fixed-return projection assumes every year earns the same, which never happens. A Monte Carlo simulation draws many random monthly returns using your expected return and volatility, applies your monthly contributions, and records where each path ends. The spread of those end values shows how much luck matters: the median is the typical outcome, while the 10th and 90th percentiles mark pessimistic and optimistic cases. The probability of reaching a target shows how often the simulated paths got there. Results depend on the inputs and on simple assumptions, so use them to compare scenarios, not to predict.