← Back to Blog

ETF Monte Carlo Simulator

4/19/2026

etfmontecarloetfindex

One of my favorite tools on this site is the ETF Monte Carlo simulator: pegler.eu/etf_montecarlo.

The core idea is to replace the "single expected return" mindset with a probability-based view. Real markets do not move in a straight line, so the simulator generates many possible futures from historical monthly return behavior. Instead of one final portfolio value, you get a range of outcomes with probabilities attached.

The workflow is:

  1. Load historical monthly return data.
  2. Fit a statistical distribution (for example normal, Weibull, or generalized normal) to the observed return pattern.
  3. Draw random monthly returns from that fitted distribution across many simulation runs.
  4. Apply fee-adjusted compounding over the chosen horizon.
  5. Add initial investment and monthly installments to each path.
  6. Analyze the final value distribution.

This is exactly what the online tool orchestrates. You can set dataset, currency, fit distribution, years, monthly installment, initial investment, annual fee, and number of runs. The resulting analysis is useful because it highlights not just a median scenario, but also downside and upside regions. That helps frame questions like: "How wide is the uncertainty band?" and "How sensitive is my plan to bad sequences of returns?"

Under the hood, calculations come from my Python library emloan on GitHub. The simulation fits return distributions, generates log-return paths, converts them into growth factors, applies annual fees in monthly steps, and aggregates contributions into a final value per simulation.

Available Data

For now I have the following datasets available:

  • ACWI Standard (Large+Mid Cap)
  • ACWI SMALL CAP Small Cap
  • EM (EMERGING MARKETS) Standard (Large+Mid Cap)
  • LBMA Gold Monthly Average

Each index is available in multiple currency versions (USD and EUR). This makes it easier to compare how portfolio outcomes can differ not only by asset class, but also by reporting currency.

Possible Next Steps

  • Extend the analysis with additional KPIs (for example drawdown-focused and tail-risk metrics).
  • Add more datasets to broaden the historical and asset-class coverage.
  • Run Monte Carlo simulations for full multi-asset portfolios instead of single-index setups.

It is not financial advice, but it is a practical way to build intuition for long-term ETF investing under uncertainty.

ETF Monte Carlo Simulator — Emanuel Pegler