TL;DR

A Monte Carlo simulation models uncertainty by running many random scenarios to see the range of possible outcomes. Instead of predicting a single price path, it generates hundreds or thousands of possible futures (each with random trade sizes, directions, and timing) to show the probability distribution of outcomes like market cap, price, and pool state after launch.

How It Works

The name comes from the Monte Carlo Casino in Monaco, and the concept is exactly what it sounds like: instead of trying to predict the future with a single calculation, you roll the dice many times and see what happens.

In a token launch context, nobody knows exactly what will happen after the pool goes live. Will the first trade be a $500 buy or a $5,000 sell? Will there be 5 trades in the first hour or 50? Monte Carlo simulation acknowledges this uncertainty by generating random sequences of trades and computing the resulting pool state after each one.

The Token Launch Simulator implements this with a 25-transaction sequence. After the pool is created and the founder buy executes, the simulator generates 25 random trades. Each trade is randomly a buy or sell, with a size drawn from the configured participant range (small, medium, or large participants). The simulation tracks the pool reserves, spot price, and market cap after each transaction.

The key feature is the seed: a number that initializes the random generator. The simulator uses a Mulberry32 seeded PRNG (pseudo-random number generator), which means the same seed always produces the identical sequence of trades. This is critical for fair comparisons: if you want to see how a 70/30 split performs differently from an 80/20 split, you can run both with the same seed. Any difference in the outcome is purely from the parameter change.

To get a fuller picture, you’d run multiple seeds. One seed might produce a favorable sequence (mostly buyers), while another might produce a crash scenario (several large sells in a row). The spread of outcomes across seeds tells you how robust your configuration is: does it hold up under various market conditions, or does it only work in the best case?

The fundamental insight of Monte Carlo simulation is that the average outcome is less important than the distribution. Your launch might have a median outcome of 2x market cap growth, but if 10% of scenarios result in the pool being drained below viability, that tail risk matters more than the median.

Try It Yourself

Run the Monte Carlo simulator: after setting up your pool parameters in the Token Launch Simulator, run the 25-transaction simulation. Change the seed to see different scenarios. Switch between small, medium, and large participant profiles to see how trade size distribution affects outcomes. Try the Token Launch Simulator →

Related Concepts

• Constant Product AMM: The formula applied to each simulated trade to update pool state
• Price Impact: What each simulated trade causes in the pool, varying by size and pool state
• Liquidity: Determines how resilient the pool is to the random trade sequence
• Slippage: The cost each simulated trader experiences, tracked per transaction
• Spot Price: Tracked after each simulated trade to show the price trajectory

Frequently Asked Questions

What is a Monte Carlo simulation?

A Monte Carlo simulation is a computational technique that uses randomness to model uncertain outcomes. Instead of calculating a single deterministic result, it runs many random trials, each with different inputs drawn from probability distributions, and aggregates the results to show the range and likelihood of different outcomes. It’s named after the Monte Carlo Casino in Monaco, referencing the element of chance.

How does the Token Launch Simulator use Monte Carlo?

The Token Launch Simulator runs a 25-transaction Monte Carlo simulation after the pool is created and the founder buy is executed. Each transaction is randomly a buy or sell, with a random size drawn from the configured participant range. A seeded pseudo-random number generator ensures the same seed produces the same sequence, so you can reproduce and compare specific scenarios.

What is a simulation seed?

A seed is a starting number for the random number generator. Using the same seed produces the exact same sequence of “random” transactions every time. You can compare two configurations fairly: change only the liquidity split while keeping the same seed, and any differences in outcome are due to the parameter change, not different randomness. Changing the seed gives you a different scenario.

Why use Monte Carlo instead of a single forecast?

Single forecasts give a false sense of precision. The actual market after a token launch involves unpredictable buyer and seller behavior. Monte Carlo acknowledges this uncertainty by showing a range of outcomes. By running multiple seeds, you can see how your launch configuration performs across different market conditions, identifying whether your liquidity is robust or fragile.

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