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.
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.
All readings on this track · 26 readings
- 1989Known-bias price streams for system evaluation
- 1989Constructing holdout-checked synthetic prices from chaos maps
- 1991Closing the feedback loop in auction market simulations
- 1994Evaluating eight-year election regimes
- 1995Constructing equity versus leverage with resampled trades
- 1995Stock leverage from compounded returns and margin cost
- 1996Constructing and optimizing volatility position-sizing profiles
- 1996Constructing fade rules from an empirical deviation histogram
- 1998Return-path volatility and the recovery burden
- 1999Monte Carlo checks beyond a single historical path
- 2002Monte Carlo equity paths from win rate and profit factor
- 2003Checking a small system edge with Monte Carlo simulation
- 2003Evaluate a mechanical trading system with a stop-loss order and Monte Carlo simulation
- 2005Evaluating momentum rank rotation under portfolio constraints
- 2009Bounded risk and rank rotation in portfolio construction
- 2010A calendar for walk-forward robustness and Monte Carlo evaluation
- 2012Treat the optimize view as an evaluation protocol
- 2013Entry, exit and abstention as one testable procedure
- 2014Evaluating stop and target bounds before fading large moves
- 2014Evaluating system exits when markets get noisier
- 2015Memory-bearing synthetics for dominant-cycle spectra
- 2015Constructing next-session implied-volatility forecasts by reordering closes
- 2016Mechanical systems as a gated factory
- 2017Four-sleeve Monte Carlo annual-loss bounds
- 2018When to retire a mechanical system with Monte Carlo drawdown limits
- 2020Treat mechanical system design as a revalidation lifecycle