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Simulation · Monte Carlo Simulator

Test your prediction plan with Monte Carlo simulation

One possible estimated return cannot show the range of ways a plan might perform. Permutify's Monte Carlo Simulator repeatedly samples possible results for your configured plan, helping you inspect estimated profit frequency, returns, and downside alongside the individual cards.

Open Permutify to use Monte Carlo Simulator

Pro feature

Seeing how the plan behaves across many possible worlds.

Profit, return, and downside estimates from repeated runs.

What Monte Carlo Simulator does

Runs randomized simulations to estimate profit probability, expected return, downside risk, and card performance.

Example: compare an original plan with a trimmed version

Start with a generated card set and run the simulation using your current allocations. Record the estimated returns and downside. Then remove a group of cards, review the redistributed allocations, and simulate the revised plan. Compare both the upside and the unfavorable outcomes; a higher maximum estimated return can coexist with a worse estimated chance of recovering the total allocation.

How to use Monte Carlo Simulator

Open Permutify and find Monte Carlo Simulator in Simulation.

  • Open Monte Carlo Simulator.
  • Choose the number of runs.
  • Start the simulation once your plan has enough outcomes.

What to check before relying on the result

Simulation results reflect the configured odds, confidence assumptions, and sampling model. More runs can reduce sampling noise, but cannot correct inaccurate inputs or missing relationships between outcomes. An estimated profit rate is not a historical backtest result or a guarantee about the next plan.

Is Monte Carlo simulation the same as backtesting?

No. Simulation generates possible results under a model. Backtesting evaluates a method against recorded historical outcomes. Use each for the question it can answer, and do not present simulated results as realized returns.

What should I inspect besides expected return?

Review estimated profit probability, downside risk, and card performance together. A favorable average can hide a wide range of outcomes or dependence on a few high-return cards.