Monte Carlo simulation (reshuffled sessions)

A Monte Carlo simulation of a trading record reshuffles the order of the recorded trading days many times and replays each ordering against the account rules, producing a range of outcomes instead of the single path that actually happened. It shows how sensitive a result is to the sequence of days, not whether the strategy has an edge.

Senzoukria · Glossary · Updated September 2026

The idea

One equity curve is one ordering of the days you traded. Had the same days arrived in another order, the peak, the trough and the date of any rule breach would differ. Reshuffling the daily results and replaying each ordering answers the question 'how much of my drawdown was the order of the days?' without inventing any new day.

The output is a distribution: the worst drawdown of each ordering, the day an account rule would have been breached, whether a payout condition was reached. Percentiles of that distribution replace the single number from the real path.

Two ways to reshuffle

  • Shuffled daily: every day is independent. Losing streaks are broken up, so the simulation tends to understate clustered losses.
  • Shuffled in blocks: consecutive days are kept together. Streaks survive, but fewer blocks leave fewer distinct orderings. The block length changes the result and cannot be fully justified; moving it shows how sensitive the answer is.
  • Either way the sample is the sample. With too few sessions the orderings are near-identical and the percentages read as certainties when they are not.

In Senzoukria

The Prop firm simulation of the desktop has a section titled 'If the same days had come in another order', with the hint: the same trading days, reshuffled N times; it shows the range of plausible outcomes and does not rank strategies. It reports the chance of passing, the chance of a first payout, the share of paths blown before a payout and the median net cash profit under the configured rules.

The Drawdown zones panel offers the two methods, Shuffled daily and Shuffled in blocks, and labels the worst drawdown as an accident, common or routine depending on how often reshuffled paths reach it. A separate panel, 'Odds an account hits the max DD', replays the whole run with recurring account purchases and counts what happens to accounts. With fewer than twenty sessions the Drawdown zones panel warns that reshuffling produces near-identical paths and that the percentages read as certainties when they are not. When trades carry no excursion data and a rule follows open profit, the Prop firm simulation refuses to settle that rule rather than assume a price path.

Common mistakes

  • Reading the median outcome as a forecast. The simulation only reorders what already happened.
  • Comparing two strategies by their Monte Carlo pass rates. The tool measures sequence sensitivity, not edge.
  • Reshuffling trades instead of days when the account rules are daily. Intraday rules need the daily grain.
  • Forgetting that a daily-close simulation cannot see an intraday breach that recovered before the close.

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Frequently asked questions

Does reshuffling sessions create new market data?
No. Every path uses exactly the recorded days, only in a different order. Nothing about the strategy, the fills or the market changes. The method therefore cannot reveal a risk that never occurred in the sample; it can only show how the recorded days combine into better and worse sequences.
Why does the block length matter?
Losing days tend to cluster in real trading. Shuffling day by day breaks those clusters and produces smoother paths than reality. Keeping blocks of consecutive days preserves the clustering but reduces the number of distinct orderings. There is no correct block length; the desktop help text says to move it and watch how much the answer changes.
Is a Monte Carlo simulation the same as a permutation test?
They share the reshuffling mechanic but ask different questions. The Monte Carlo here reshuffles days to measure how outcomes depend on sequence under account rules. A permutation test reshuffles to build a null distribution and asks whether the observed result could have arisen by chance, producing a p-value.

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