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2016issue C0132-37

Mechanical systems as a gated factory

A mechanical trading system is one written procedure for entry, exit and abstention. The archive treats design as a sequence that starts with goals, then discards candidates that cannot stay acceptable on a long test, pass walk-forward analysis and Monte Carlo simulation, sit through incubation, and join a book without doubling risk.

  • Mechanical trading remains emotional; the feeling attaches to following or ignoring a written rule set.
  • Vague aims such as large profits with small drawdowns are linked to extra rules and overfitting that fail later.
  • A single in-sample equity curve is not a finish line; walk-forward analysis and Monte Carlo simulation inspect whether the procedure still holds.
  • Incubation, system combination and a stop-trading rule keep live decisions from being left to mood.
Entries in this reading3 entries

Feelings move onto the rules

A mechanical trading system is a fully specified procedure that turns rule inputs, market state and execution constraints into entry, exit or abstention signals over the system's holding period. The archive presents mechanical trading as still emotional. The change is that feelings attach to following or ignoring a rule set, rather than disappearing once rules exist.

Once more than a handful of rule-based systems are live, tracking discretionary overrides becomes impractical. That limit is offered as a way to reduce ad hoc signal rejection. Each live system is supposed to carry a stop-trading rule: pre-set performance conditions that retire the system so retirement is not left to mood when results deteriorate.

Begin with written goals

The design sequence is described as seven to eight steps that begin with written goals, including targets for annual return and maximum drawdown. Vague aims such as large profits with small drawdowns are linked to extra rules, curve-fitting and over-optimization that fail to hold later. That pattern is overfitting: adding rules or free parameters until a historical test looks strong while the same procedure fails on later data.

Keep candidates as raw material

After goals are set, candidate ideas are treated as raw material and discarded if they do not remain acceptable over a test horizon typically given as five to ten years. Loading every rule onto the full history, optimizing all parameters and trading the best in-sample result is described as a common procedure that usually does not hold up.

Gate the procedure

Walk-forward analysis is a rolling out-of-sample check that re-estimates rules on earlier data and tests them on the next unseen segment so parameters are not locked to one historical sample. Monte Carlo simulation reshuffles an ordered trade or return sequence to produce a range of paths, especially drawdowns, instead of judging viability from one equity curve. The archive uses that reshuffling of trades to inspect drawdown outcomes that a single equity curve can understate, and it applies viability criteria at those simulation points.

Incubate, combine and watch for retirement

A candidate that looks viable is parked for later review. That pause is incubation: a planned wait during which the candidate is watched, with possible walk-forward reoptimization, and rechecked after about six months before it is treated as still intact.

Viable systems are then combined with others to avoid doubled risk and high correlation. System combination means running several mechanical procedures together across markets, styles or holding periods so risk is not concentrated in one curve. After combination, live procedures are monitored for retirement against their stop-trading rules.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
23 of 26 in the Monte Carlo simulation track
201716-19 pp.Next on Monte Carlo simulationFour-sleeve Monte Carlo annual-loss boundsA usable risk sample covers several market conditions and meets a trades-per-parameter floor so profit-factor and win-rate estimates are statistically meaningful.
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
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