How to Use the Crypto Runway Calculator: Step-by-Step Walkthrough
Published 2026-01-22
Start by picking an allocation preset near the top of the tool. 'Bitcoin-Heavy' models an 80/20 Bitcoin-to-altcoin style split with comparatively lower blended volatility. 'Diversified' spreads exposure more evenly, and 'Altcoin-Heavy' models a portfolio weighted toward smaller, historically more volatile tokens. Each preset changes the blended volatility multiplier used in the simulation.
Next, enter your starting investment and, if applicable, a monthly contribution amount along with how many years you plan to keep contributing before you start withdrawing — this is your accumulation phase. Then enter how many years you expect to draw income and how much per month you want to withdraw, in today's dollars, along with an assumed inflation rate to adjust future withdrawals upward over time.
Click 'Run 1,000 Simulations.' The tool immediately computes results and draws a fan chart showing the 10th percentile, median, and general spread of outcomes across every year of your combined timeline. A wide gap between the red (10th percentile) and blue (90th percentile) lines signals high uncertainty — that's normal and expected for crypto, not a bug in the simulation.
Read the success rate first. Financial planners commonly treat 85%+ as a relatively comfortable success rate for a traditional retirement plan; because crypto's volatility is higher, many realistic crypto scenarios will land lower than that, especially with aggressive withdrawal amounts. If your result is low, try reducing the monthly withdrawal, shortening the withdrawal period, or extending the accumulation phase, then re-run the simulation to see how the outcome shifts.
Frequently Asked Questions
What does the shaded band on the chart mean?
It represents the range between the 10th percentile (worst-case-ish) and 90th percentile (best-case-ish) outcomes across all 1,000 simulated paths for each year of your timeline.
Why do my results change every time I click 'Run Simulation'?
Each run generates a new set of 1,000 randomized paths, so results will vary slightly run to run — this is expected behavior for any Monte Carlo simulation and reflects genuine statistical variance, not an error.