What can a trading-journal Monte Carlo simulation actually tell me?
By Chart Reporter Reviewed 2 min read
The short answer
A journal bootstrap simulation explores sequences built from observed eligible outcomes under stated assumptions. Chart Reporter's Monte Carlo simulator resamples eligible R values with replacement. Its model proportions describe those simulations, not guaranteed probabilities of future returns or broker-account survival.
Understand the ingredients
The current simulator runs 2,000 seeded bootstrap sequences from eligible observed R multiples. Sampling with replacement means an observed outcome can appear more than once in a simulated sequence while another does not appear. The seed supports reproducibility for the same inputs.
That model explores the observed outcome distribution and its resampling assumptions. It cannot include a loss type, market regime or execution failure absent from the inputs simply because the chart looks comprehensive. A narrow historical sample remains a narrow foundation even when thousands of sequences are drawn from it.
Keep the units and assumptions attached
Reported drawdown is in R, not automatically a percentage of broker equity. Converting it into account risk would require a separate sizing and capital model. Also inspect which records supplied valid R and whether the calculation mixed net-money R with price-R fallback values.
Resampling individual outcomes can break historical clustering and dependence. If losses tend to cluster around one market condition, a simple bootstrap may not preserve that mechanism. It is useful to ask what the model assumes about sequence and whether those assumptions fit the question being investigated.
Use simulation to formulate checks
The output can help you explore how a selected sample behaves under alternative resampled sequences. Compare that with the observed path and investigate which outcomes dominate the spread. Keep the input filters and eligible count with the result so it can be reproduced.
Do not treat a favourable model proportion as permission to increase risk or as proof that a losing streak cannot exceed the modelled range. New conditions can produce new outcomes. A useful report states the model, sample, units and known omissions, then identifies what additional evidence would challenge its conclusions.
Sources and further reading
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