Forex glossary
Monte Carlo simulation
Monte Carlo simulation repeatedly samples scenarios from a chosen model. Strategy analysis can shuffle trades, resample them with replacement or model price changes to produce distributions of outcomes and drawdowns. Estimates are conditional on data and assumptions; they do not automatically cover every possible loss.
Illustrative example
How does it work in practice?
Assume three trade outcomes of +100, −80 and −80 USD. Reordering them preserves the total loss of 60 USD but changes the equity path; sampling with replacement can also change the final result.
A case to work through
Same set, different path
Hypothetical fixed cash outcomes are +60, −30 and −20.
- Every reordering of those three values totals +10.
- Sampling with replacement can select +60 three times, producing +180 in that sample.
What follows? Name the sampling method alongside the result distribution.
Example boundary: With equity-dependent sizing, reordering can change final results; the model must specify this.
Method changes sample properties
Text description of the diagram
- Shuffling: Reorders the same fixed outcomes without changing their sum.
- Sampling with replacement: Can repeat an outcome and change sample composition.
- Model assumptions: Position sizing and dependencies need specifying.
A useful distinction
Monte Carlo simulation / Backtesting
A backtest generates outcomes from historical rules; Monte Carlo examines alternatives conditional on the selected model and data.
Check your understanding
Identify what shuffling preserves when trade amounts are fixed.
Show the answer and explanation
+10
Shuffling unchanged values preserves their sum.
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Related terms
Sources and documentation
- SciPy – bootstrap
Resampling with replacement and sample-statistic distributions conditional on the data.
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The example illustrates the mechanism. Check current account conditions and instrument specifications with the provider.