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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.
Entries in this reading3 entries

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.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
17 of 51 in the Robustness testing track
19961-4 pp.Next on Robustness testingParameter grids can fit random walksSystem-optimization that keeps the single best in-sample set shows a close fit to one preselected window, not that the rules captured lasting price dynamics.
All readings on this track · 51 readings
  1. 1986Degrees of freedom in trading system optimization
  2. 1988Walk-forward and neighborhood tests after optimization
  3. 1988Undisclosed rules block system robustness tests
  4. 1988Testing re-optimization calendars against random parameter controls
  5. 1989Binary search limits on multi-peak average grids
  6. 1989Parameter neighborhoods that survive a shift
  7. 1990Use profit mapping to keep a cycle and stop plateau
  8. 1990Why popular indicator optimization fails robustness
  9. 1991Retesting weighted indicator balances across horizons
  10. 1992Constructing forecast models with regression, walk-forward, and robustness
  11. 1992Diagnose regimes before you lock parameters
  12. 1992When stops change system timing
  13. 1993Walk-forward halt rules for forecast models
  14. 1994Walk-forward evaluation of genetic index rules
  15. 1995Input pruning as walk-forward system evaluation
  16. 1995Critiquing neural nets as incomplete trading systems
  17. 1996Rebuild the equity-path ratio before it ranks a designed system
  18. 1996Parameter grids can fit random walks
  19. 1996Walk-forward analysis belongs in the design of a mechanical trading system
  20. 1997When a holdout fails, discard the rule set
  21. 1997Test rewarded rule breaks before replacing the system
  22. 1997Walk-forward rules keep system research from rewriting live trades
  23. 1999Keep a channel-breakout to two lookbacks and test neighbor stability
  24. 1999Constant investment size in stock system evaluation
  25. 2000Forcing optimization maps mechanical system failure boundaries
  26. 2000Robust parameter selection with surface charts
  27. 2001A two-gate classroom test for a two-window momentum trend filter
  28. 2002How a two-sided continuation factor becomes a testable trend rule
  29. 2002Evaluating two-window trend intensity as a reversal rule
  30. 2003Discounting speculative bubbles in system robustness tests
  31. 2003Walk-forward evaluation of locked stochastic oscillator rules
  32. 2003Critiquing mechanical system design after extreme price regimes
  33. 2004Evaluating a two-window trend trigger
  34. 2005Grade backtested signals with holdouts and optimization plateaus
  35. 2006Reserved-sample evaluation of trading system design
  36. 2006Walk-forward critique of hindsight crossover systems
  37. 2008Condition-matched walk-forward evaluation for mechanical systems
  38. 2011Session-split evaluation of regular and overnight systems
  39. 2012Walk-forward evaluation as operator rehearsal
  40. 2013Two-window evaluation of mechanical trading systems
  41. 2013Walk-forward filter selection for repeated-median velocity
  42. 2014Walk-forward evaluation for fading-memory velocity systems
  43. 2015Test oscillator events before tuning rules
  44. 2016Walk-forward evaluation of a five-parameter parabolic stop-and-reversal
  45. 2016Walk-forward optimization without curve fitting
  46. 2017Optimization without overfitting in trend-system evaluation
  47. 2017Parameter stability is a better guide than a larger crossover grid
  48. 2018Point-in-time universes for system evaluation
  49. 2018Walk-forward robustness evaluation for optimized systems
  50. 2018Critiquing breakout systems through robustness tests
  51. 2018A critique of parameter fitting in system design
All 58 readings tagged Robustness testing
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