Monte Carlo Simulator

The Monte Carlo Simulator projects 20,000 possible price paths from now to a future expiration date and turns them into a probability distribution at that date.

Each path is a random walk seeded with the current ATM implied volatility and then bent by the current GEX surface: above the Flip point, paths get slightly compressed (positive-gamma drag); below it, paths get slightly amplified (negative-gamma drag). The strength of that bend is controlled by the GEX INFLUENCE setting.

What you see on screen:

• Path cloud — sample paths colored by where they end (green up, red down by default — see PALETTE).
• Right-edge density — the terminal price distribution at the chosen HORIZON.
• p10 / p50 / p90 labels — 80% of paths end between p10 and p90. p50 is the median (half the paths end above, half below).
• σ (sigma) — standard deviation of the terminal distribution, in dollars. Width of the cone.
• Skew — asymmetry of the terminal distribution. Negative skew (e.g. −0.55) means a longer downside tail — more paths drift down than up.
• Flip line (magenta dashed) — current Gamma Flip point, for reference.

How to use it:

• Will price stay above $X by expiry? — read the share of paths terminating above $X (look at p10 / p50 / p90 as anchors).
• Where's the centre of mass? — that's the median (p50); compare to current spot.
• Is the move symmetric or one-sided? — check the Skew readout. A strongly negative skew warns that the cone hides a fatter downside tail.
• How wide is the cone? — σ in dollars; a rough first-pass on expected-move size.

Important limits:

• Inputs (IV, GEX surface) are a snapshot now — the model doesn't anticipate new positioning, news, or vol regime changes.
• The cone widens with time. Short horizons are far more reliable than far-dated ones.
• Tail probabilities (paths far beyond p10/p90) are model-dependent. Treat them as orders of magnitude, not precise odds.

For a hands-on walkthrough — including how to use the hover plate to read tail probabilities for any price — see the blog post Monte Carlo Simulator: Forecasting Where BTC Might Land by Expiry.