Crypto Volatility Simulation Tool
Simulate hundreds of possible cryptocurrency price paths under very high volatility using a geometric Brownian motion model.
Explore long-tail outcomes, probabilities of doubling or crashing, and the distribution of future prices over your chosen time horizon.
The current spot price of the cryptocurrency you want to simulate (for example, BTC, ETH, or any altcoin).
Long-run expected annual return (before fees and slippage). This can be positive or negative. Monte Carlo paths will fluctuate around this drift.
Standard deviation of annual returns. Crypto often runs in the 60– 150%+ range, which creates huge long-tail outcomes.
How far into the future you want to simulate the price (in calendar days). The model uses daily steps internally.
Each simulation represents one possible price path. More simulations give smoother distributions but take longer to compute.
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How the Crypto Volatility Simulation Tool Works
The Crypto Volatility Simulation Tool is designed for traders and investors who want to understand how extreme price swings can affect future outcomes for cryptocurrencies. Rather than showing a single forecast line, it uses Monte Carlo simulation to generate thousands of possible price paths based on user-defined assumptions for drift (expected return) and volatility. This style of model is widely used in quantitative finance for options pricing, risk analysis, and portfolio stress-testing, and it is particularly well suited to crypto assets, which are known for large and frequent moves.
Under the hood, the tool uses a geometric Brownian motion (GBM) model, which treats returns as continuously compounded and ensures that prices remain non-negative. You enter the current price, an expected annual drift, an annualized volatility, a number of days into the future, and how many simulation paths to generate. The simulator then divides time into daily steps and repeatedly updates the price using random shocks drawn from a normal distribution. Each path represents one potential future market trajectory. After all the paths are generated, the tool collects the final prices, sorts them, and calculates key statistics such as the mean, minimum, maximum, median, and several percentiles.
In addition to summarizing the full distribution of final prices, the Crypto Volatility Simulation Tool also estimates the probability of certain long-tail outcomes, such as the chance that the price at horizon will be at least double the starting value or half or less of the starting value. These metrics can help highlight the asymmetry and risk inherent in highly volatile assets. For example, a coin with very high volatility and moderate drift may show both a meaningful chance of extreme upside and a non-trivial risk of deep drawdowns or effective collapse.
It is important to remember that this simulator does not predict the future or account for every real-world factor. It ignores order book dynamics, liquidity constraints, on-chain fundamentals, regulatory events, and macro shocks. The drift and volatility you enter are simplifications of a far more complex environment. As a result, this tool should be viewed as an educational sandbox and an intuition builder, not as a signal generator or guaranteed trading system. Traders may find it useful for exploring how different volatility regimes affect potential outcomes, but real-money decisions should always incorporate thorough research, risk management, and personal judgment.
Crypto Volatility Simulation Tool – FAQ
What does this crypto simulator actually show?
The simulator generates a distribution of possible future prices for a cryptocurrency over a chosen number of days. It reports the mean, minimum, maximum, and key percentiles of the final price, along with probabilities of large moves such as doubling or halving from the starting price.
Why use geometric Brownian motion (GBM) for crypto?
Geometric Brownian motion is a standard mathematical model for asset prices that ensures non-negative prices and captures compounding effects. While crypto markets are more complex than this simple model, GBM provides a widely recognized starting point for simulating high-volatility assets and exploring possible outcomes.
Does this tool predict real crypto prices?
No. The tool is not a prediction engine. It shows what could happen under specific assumptions for drift and volatility, but real markets may behave very differently. The outputs are intended for education and scenario analysis, not as trade recommendations or financial advice.
How should I choose drift and volatility values?
Some users base drift and volatility on historical data for a given coin, while others experiment with a range of scenarios to see how sensitive outcomes are to these inputs. For many large-cap cryptocurrencies, annual volatility values between 60% and 150% are common in practice. There is no single "correct" choice, so it can be useful to test both conservative and aggressive assumptions.