I’m a long-time Bitcoiner who kept running into the same question:

How much Bitcoin can I hold if I actually plan to live off my portfolio someday?

Most retirement simulators either ignore Bitcoin entirely or behave badly when you introduce high-volatility assets. I wanted a tool focused on sequence-of-returns risk, withdrawal sustainability, and how Bitcoin changes the distribution of outcomes — not price prediction.

So I built HODLPath, a Monte Carlo retirement simulator designed to stress-test portfolios that include Bitcoin.

URL: https://hodlpath.streamlit.app/
Questions and constructive feedback are welcome.

What HODLPath Does

• Simulates thousands of portfolio paths over multi-decade horizons
• Models correlated multi-asset returns
• Explicitly focuses on withdrawal risk, not terminal wealth
• Supports:
  • start-of-year vs end-of-year withdrawals
  • inflation-indexed or nominal spending
  • optional spending guardrails after drawdowns
  • annual rebalance or natural drift

Outputs include:

• success / ruin probabilities
• full wealth distributions (fan charts)
• spending path distributions
• downloadable PDF reports (including comparison exports)

Return Model (Important)

HODLPath uses a lognormal (log-return) model:

• Users enter expected return and volatility as annual arithmetic values (the intuitive inputs most people think in).
• Internally, these are converted into log-return parameters and sampled with a correlation matrix.
• This ensures:
  • portfolio values never go below zero
  • realistic long-horizon compounding
  • more appropriate behavior for volatile assets like Bitcoin

This is not a forecasting tool — it’s meant for decision clarity under uncertainty and sensitivity analysis.

Garbage in → garbage out still applies, and assumptions are intentionally explicit.

Who This Is For

HODLPath is aimed at:

• long-horizon investors
• early retirees / FIRE planners
• anyone modeling withdrawals with Bitcoin in the portfolio

It’s not investment advice — just a way to explore how fragile or robust different allocations are when you actually start spending.

Side note: I built this as a learning exercise using modern AI coding assistants, which turned out to be surprisingly effective for this kind of analytical tool — but all modeling choices and assumptions are explicit and user-controlled.