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2004issue C121-7

Evaluating a two-window trend trigger

A scaled oscillator from two adjacent windows of highs and lows can turn a trend stance into a fully specified long, short, and stay-flat procedure. The evaluation question is whether those same rules remain usable after the lookback, market, and sample period change.

  • Buy power and sell power come from two adjacent lookback windows of highs and lows, and the oscillator scales their difference by the average of those two ranges.
  • Large range overlap keeps the reading near the origin; small overlap in a directional move pushes it beyond a long or short trigger.
  • The same triggers become a mechanical trading system only when long, short, and stay-flat rules are specified together and tested as one object.
  • Robustness testing asks whether that directional outcome survives a change of lookback, market, or time slice, rather than whether one historical sample is treated as proof.
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Current trackRobustness testing58 readingsTrend filter137 readingsMechanical trading system158 readings

Two adjacent windows of highs and lows

The trend-trigger-factor is a scaled oscillator that compares buy power and sell power from two adjacent lookback windows of highs and lows, then divides their difference by the average of those two windows' ranges.

Buy power is the highest high of the nearer window minus the lowest low of the farther window. Sell power is the highest high of the farther window minus the lowest low of the nearer window. That denominator is the average of the two window ranges.

Range overlap and the trigger

Range overlap is how much the two summarized lookback bars occupy the same price territory. In a consolidation the two powers are similar in size and close to the average two-window range, so the gap stays small relative to that average and the oscillator typically remains near the origin.

When the two summarized bars have little overlap in an uptrend, buy power is large and sell power is small versus the average range, so the oscillator tends to move beyond a positive trigger. In a downtrend the same overlap logic reverses: sell power is large and buy power is small, and the absolute gap between the two powers is large, so the reading tends to move beyond a negative trigger.

One procedure and three robustness checks

A trend filter stays long only when the oscillator is above a positive trigger and short only when it is below a negative trigger. Adding the stay-flat case makes a mechanical trading system: a fully specified long, short, and stay-flat procedure whose entries, exits, and abstention rules can be tested as one object.

The archive presents the oscillator as usable alone or as confirmation of other signals. It treats robustness as three checks: across parameter values, across markets, and across time.

A historical multi-market hypothetical used continuous contracts and charged commission and slippage. That table reported mixed signed totals across groups. Among the lookbacks compared, historical usefulness was not the same from one window length to another.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
33 of 51 in the Robustness testing track
20051-3 pp.Next on Robustness testingGrade backtested signals with holdouts and optimization plateausHold unused historical segments out of the optimization search so finished rules can be checked on market conditions that did not choose the inputs.
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
By fieldTrend and mean reversion93 readingsBy year2004107 readings
All 58 readings tagged Robustness testing
Also on Robustness testing5 readings
2002Evaluating two-window trend intensity as a reversal rule2001A two-gate classroom test for a two-window momentum trend filter2013Walk-forward filter selection for repeated-median velocity1988Isolate nested formulas before judging signals1988Undisclosed rules block system robustness tests