Skip to main content
Track Robustness testing
24 / 51
Library

1999issue C051-4

Constant investment size in stock system evaluation

An equal-share fill can make a stock system's score follow the price level of a split-adjusted series instead of the percent move the rules captured. Treating constant investment size as an execution constraint inside walk-forward analysis, robustness testing, and system optimization keeps the score on entry, exit, and abstention logic.

  • Equal share counts on a two-dollar name and a hundred-dollar name make a one-dollar move post the same dollar result even though the percent moves differ by a factor of fifty.
  • Setting share count to a one-thousand-dollar stake divided by price makes a fifty-percent move post the same dollar result at two-dollar, twenty-dollar, and hundred-dollar prints.
  • Using a split-adjusted print in the constant-stake share formula overstates share count by the split ratio and understates the price change by the same ratio, so the dollar result is unchanged.
  • Stock-system tests should keep split-adjusted prices and replace the implicit constant-share fill with a constant investment size so the evaluated procedure matches how the signals would be sized in use.
Entries in this reading3 entries

The fill rule belongs in the procedure

Walk-forward analysis is the out-of-sample scoring of entry, exit, and abstention rules as one procedure, using rule inputs, market state, and execution constraints such as how large each fill is. System optimization is a search over rule inputs inside a test protocol. The size rule is an execution constraint that belongs inside that protocol.

Robustness testing checks whether an evaluated signal still stands when a test assumption changes, including whether share count is held fixed while prices span a wide range. An equal-share fill gives every signal the same number of shares regardless of the name's price or the date in the sample. Constant investment size keeps the dollar stake of each signal fixed, so share count equals that stake divided by the price in force at the signal.

Equal-share fills create a price-level artifact

Equal share counts on a two-dollar name and a hundred-dollar name make a one-dollar move post the same dollar result even though the percent moves differ by a factor of fifty. The same percent move under equal share counts produces a dollar result fifty times larger in the hundred-dollar name than in the two-dollar name.

That gap is a price-level artifact: a test result driven by where the sample sits in price, not by the percent move the rule captured.

Constant investment size holds the stake fixed

Setting share count to a one-thousand-dollar stake divided by price makes a fifty-percent move post the same dollar result at two-dollar, twenty-dollar, and hundred-dollar prints. The score then follows the percent move the signal captured rather than the print level of the name.

Split-adjusted prints and the share formula

A split-adjusted series is a price history scaled by later split ratios so the series does not drop by the split factor on the split date. A split-adjusted print equals the actual print divided by the cumulative split ratio. Two later two-for-one splits turn a twenty-dollar actual print into a five-dollar adjusted print.

Using the adjusted print in the constant-stake share formula overstates share count by the split ratio and understates the price change by the same ratio, so the product that is dollar result is unchanged.

The four-to-one split case

In the worked four-to-one split case, a twenty-percent rise produces a two-hundred-dollar result from actual past prices, adjusted past prices, and the later forty-dollar print when the stake is held at one thousand dollars.

A long-history crossover sample

On a long-history twelve-and-one-hundred-twenty-bar crossover, constant-share scoring treats an early low-price winner as a small per-share gain while constant-dollar scoring keeps the large percent gain visible. Constant-share scoring on split-adjusted history underweights early low-price years and overweights later high-price years, so the same later trade can dominate the total under one size rule and not the other.

Walmart daily price, July 1994 to October 1997

Walmart holds the low-to-mid $20s through 1996 and then climbs into the high $30s by late 1997. An equal-share fill would therefore score the later percent moves as several times more dollars than the same rules would have booked at the start of this window. Prices were read from the published daily chart scale.
Walmart holds the low-to-mid $20s through 1996 and then climbs into the high $30s by late 1997. An equal-share fill would therefore score the later percent moves as several times more dollars than the same rules would have booked at the start of this window. Prices were read from the published daily chart scale.Wal Mart Stores Inc · Daily · 1994-07-01T00:00:00.000Z to 1997-10-31T00:00:00.000Z

Sampled at labeled months and visible turning points and rounded to the nearest half-dollar; the raster is a daily series and does not support tick-level precision.

What the test should keep

Stock-system tests should keep split-adjusted prices and replace the implicit constant-share fill with a constant investment size so the evaluated procedure matches how the signals would be sized in use.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
24 of 51 in the Robustness testing track
20001-5 pp.Next on Robustness testingForcing optimization maps mechanical system failure boundariesThe same moving-average and rate-of-change rules can produce a smooth, rising equity line on one historical window and then lose money on a later unused window.
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
Also on Robustness testing5 readings