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2003issue C111-7

Checking a small system edge with Monte Carlo simulation

Monte Carlo simulation can turn random win-or-loss draws into a patterned distribution of possible system outcomes. A thin historical sample, or heavy system optimization to past data, can make that later distribution misleading.

  • Monte Carlo simulation can turn a sequence of random win-or-loss draws into a patterned distribution of possible system outcomes.
  • Short sequences can still produce consecutive identical outcomes, so 100 trades need not contain exactly 35 winners.
  • A Monte Carlo built on only a few historical trades is unreliable because the input sample is too thin.
  • Heavy optimization of rules to historical data can make later Monte Carlo output misleading regardless of the simulated distribution.
Entries in this reading3 entries
Current trackMonte Carlo simulation51 readingsRobustness testing80 readingsSystem optimization131 readings

From random draws to a distribution

Monte Carlo simulation can turn a sequence of random win-or-loss draws into a patterned distribution of possible system outcomes.

Short sequences can still produce consecutive identical outcomes even when a long-run win rate is known, so 100 trades need not contain exactly 35 winners.

Two hypothetical systems

A hypothetical system with 35% winners, a 2000 gain on wins, and a 1000 loss on losses produced an average 45% result across 50000 trials of 1000 trades. In that same simulation, 84% of paths at least broke even and wipeout occurred in only 35 of 50000 trials, or 0.07%.

A second hypothetical system averaged a 380% result over 50000 trials of 1000 trades, with 5% of trials losing money and a 1.66% risk of ruin.

Thin samples and later results

A Monte Carlo built on only a few historical trades is unreliable because the input sample is too thin.

Heavy optimization of rules to historical data can make later Monte Carlo output misleading regardless of the simulated distribution.

If live or later results are not better, or if market dynamics have changed since the original analysis, the procedure is to reevaluate other choices.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
12 of 26 in the Monte Carlo simulation track
20031-11 pp.Next on Monte Carlo simulationEvaluate a mechanical trading system with a stop-loss order and Monte Carlo simulationSpecify the mechanical trading system as a complete entry-and-exit rule set, such as buying or selling short on the next bar when a zigzag-trend state flips.
All readings on this track · 26 readings
  1. 1989Known-bias price streams for system evaluation
  2. 1989Constructing holdout-checked synthetic prices from chaos maps
  3. 1991Closing the feedback loop in auction market simulations
  4. 1994Evaluating eight-year election regimes
  5. 1995Constructing equity versus leverage with resampled trades
  6. 1995Stock leverage from compounded returns and margin cost
  7. 1996Constructing and optimizing volatility position-sizing profiles
  8. 1996Constructing fade rules from an empirical deviation histogram
  9. 1998Return-path volatility and the recovery burden
  10. 1999Monte Carlo checks beyond a single historical path
  11. 2002Monte Carlo equity paths from win rate and profit factor
  12. 2003Checking a small system edge with Monte Carlo simulation
  13. 2003Evaluate a mechanical trading system with a stop-loss order and Monte Carlo simulation
  14. 2005Evaluating momentum rank rotation under portfolio constraints
  15. 2009Bounded risk and rank rotation in portfolio construction
  16. 2010A calendar for walk-forward robustness and Monte Carlo evaluation
  17. 2012Treat the optimize view as an evaluation protocol
  18. 2013Entry, exit and abstention as one testable procedure
  19. 2014Evaluating stop and target bounds before fading large moves
  20. 2014Evaluating system exits when markets get noisier
  21. 2015Memory-bearing synthetics for dominant-cycle spectra
  22. 2015Constructing next-session implied-volatility forecasts by reordering closes
  23. 2016Mechanical systems as a gated factory
  24. 2017Four-sleeve Monte Carlo annual-loss bounds
  25. 2018When to retire a mechanical system with Monte Carlo drawdown limits
  26. 2020Treat mechanical system design as a revalidation lifecycle
By fieldSystem design203 readingsBy year2003164 readings
All 51 readings tagged Monte Carlo simulation
Also on Monte Carlo simulation5 readings
1989Known-bias price streams for system evaluation2002Monte Carlo equity paths from win rate and profit factor2010A calendar for walk-forward robustness and Monte Carlo evaluation1989Constructing holdout-checked synthetic prices from chaos maps1996Constructing and optimizing volatility position-sizing profiles