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2014issue C0316-20

Evaluating stop and target bounds before fading large moves

Lock a Stop-loss order of size V and a profit limit of size W before fading a large move. Expected value then assigns a long-run win probability of V/(W+V) and profit expectancy of zero, so the bounds can shrink drawdowns without changing overall profit expectancy.

  • A complete fade of a large move is specified before entry by pairing a Stop-loss order of size V with a profit limit of size W, so only those two bounded outcomes remain.
  • The long-run win probability is V/(W+V) and depends only on the risk-to-reward ratio V/W; a larger stop reduces the chance of being stopped out.
  • Substituting those probabilities into Expected value with fixed win W and loss V yields profit expectancy of zero, so stop and target sizes can shrink drawdowns without changing overall profit expectancy.
  • Large moves sit far above the largest pullback, present large reward-to-risk ratios, and must be sized as a distinct exposure class.
Entries in this reading3 entries

Bounds are locked before entry

A complete fade of a large move is specified before entry by pairing a Stop-loss order of size V with a profit limit of size W, so only those two bounded outcomes remain.

Win probability depends only on the ratio

With those fixed sizes, the long-run win probability of the fade is V/(W+V) and the complementary loss probability is W/(W+V).

That win probability depends only on the risk-to-reward ratio V/W: equal stop and target imply even odds, while a larger stop reduces the chance of being stopped out.

Profit expectancy stays at zero

Substituting those probabilities into Expected value with fixed win W and loss V yields profit expectancy of zero.

Because expectancy is unchanged, a trader can choose stop and target sizes that shrink drawdowns without changing overall profit expectancy.

Large moves as a distinct exposure class

Large moves are defined by height far above the largest pullback, so they present large reward-to-risk ratios and must be sized as a distinct exposure class.

The probability that a market move is at least n times as large as its largest pullback is exp(-n+1). As an illustration, almost two of 100 moves are at least five times that pullback.

Closed form without a path simulation

A stepwise Expected value argument, and its zero-step-size limit, recovers the same closed-form win and loss probabilities without requiring a path simulation.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
19 of 26 in the Monte Carlo simulation track
20146-7 pp.Next on Monte Carlo simulationEvaluating system exits when markets get noisierA 2 percent drop after a strong year is a reason to review indicators and exits, not a verdict for bulls or bears.
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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