1996issue C051-5
Rebuild the equity-path ratio before it ranks a designed system
A published system-performance ratio that fits a line through successive equity observations was later rewritten after the original implementation misapplied the intended statistical formula. Rebuild the equity-versus-time regression, confirm residual scale and slope standard error, and compute the equity-path ratio only at end-of-sample evaluation.
- A published ratio that fits a line through successive equity observations was later rewritten after the original implementation misapplied the intended statistical formula.
- The restated construction uses a covariance-form equity-path slope, a fitted intercept from mean equity and the mean observation index, residual scale from the residual total, and a slope standard error from the centered index weight.
- The equity-path ratio is the equity-path slope divided by the product of the slope standard error and the square root of the observation count, and it is computed only after the last calculation date of the series.
- Editorial: complete a metric robustness check, then use the ratio as a robustness gate only after a parameter search.
A ranking score with its own construction
A published system-performance ratio that fits a line through successive equity readings was later rewritten after the original implementation misapplied the intended statistical formula. The restated procedure starts from equity observations and ends with an equity-path ratio.
An equity observation is closed-trade net profit plus open-position profit recorded at each sampling step. The full ratio is computed only after the last calculation date of the series.
How the equity-versus-time line is rebuilt
The equity-path slope is the ordinary least-squares coefficient of recorded equity on a one-based observation index. The restated slope uses the covariance form: the sum of index times equity, minus the product of the summed index and summed equity divided by the observation count, all divided by the similarly centered sum of squared index values.
The fitted intercept is mean equity minus the equity-path slope times the mean observation index.
Residual scale and slope standard error
Each equity observation is compared with the value projected from that fitted intercept and equity-path slope. The squared gaps are summed to form the residual total. Residual scale is the square root of the residual total divided by the observation count minus two.
The centered index weight is the sum of squared differences between each observation index and half of one plus the observation count. That weight is the slope standard-error denominator. Slope standard error is residual scale divided by the square root of the centered index weight.
End-of-sample evaluation of the ratio
The equity-path ratio is the equity-path slope divided by the product of the slope standard error and the square root of the observation count. End-of-sample evaluation means computing that ratio once on the completed equity series rather than as a running intra-sample score.
All readings on this track · 51 readings
- 1986Degrees of freedom in trading system optimization
- 1988Walk-forward and neighborhood tests after optimization
- 1988Undisclosed rules block system robustness tests
- 1988Testing re-optimization calendars against random parameter controls
- 1989Binary search limits on multi-peak average grids
- 1989Parameter neighborhoods that survive a shift
- 1990Use profit mapping to keep a cycle and stop plateau
- 1990Why popular indicator optimization fails robustness
- 1991Retesting weighted indicator balances across horizons
- 1992Constructing forecast models with regression, walk-forward, and robustness
- 1992Diagnose regimes before you lock parameters
- 1992When stops change system timing
- 1993Walk-forward halt rules for forecast models
- 1994Walk-forward evaluation of genetic index rules
- 1995Input pruning as walk-forward system evaluation
- 1995Critiquing neural nets as incomplete trading systems
- 1996Rebuild the equity-path ratio before it ranks a designed system
- 1996Parameter grids can fit random walks
- 1996Walk-forward analysis belongs in the design of a mechanical trading system
- 1997When a holdout fails, discard the rule set
- 1997Test rewarded rule breaks before replacing the system
- 1997Walk-forward rules keep system research from rewriting live trades
- 1999Keep a channel-breakout to two lookbacks and test neighbor stability
- 1999Constant investment size in stock system evaluation
- 2000Forcing optimization maps mechanical system failure boundaries
- 2000Robust parameter selection with surface charts
- 2001A two-gate classroom test for a two-window momentum trend filter
- 2002How a two-sided continuation factor becomes a testable trend rule
- 2002Evaluating two-window trend intensity as a reversal rule
- 2003Discounting speculative bubbles in system robustness tests
- 2003Walk-forward evaluation of locked stochastic oscillator rules
- 2003Critiquing mechanical system design after extreme price regimes
- 2004Evaluating a two-window trend trigger
- 2005Grade backtested signals with holdouts and optimization plateaus
- 2006Reserved-sample evaluation of trading system design
- 2006Walk-forward critique of hindsight crossover systems
- 2008Condition-matched walk-forward evaluation for mechanical systems
- 2011Session-split evaluation of regular and overnight systems
- 2012Walk-forward evaluation as operator rehearsal
- 2013Two-window evaluation of mechanical trading systems
- 2013Walk-forward filter selection for repeated-median velocity
- 2014Walk-forward evaluation for fading-memory velocity systems
- 2015Test oscillator events before tuning rules
- 2016Walk-forward evaluation of a five-parameter parabolic stop-and-reversal
- 2016Walk-forward optimization without curve fitting
- 2017Optimization without overfitting in trend-system evaluation
- 2017Parameter stability is a better guide than a larger crossover grid
- 2018Point-in-time universes for system evaluation
- 2018Walk-forward robustness evaluation for optimized systems
- 2018Critiquing breakout systems through robustness tests
- 2018A critique of parameter fitting in system design