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2012issue C0932-35

Open-parameter construction of dominant-cycle baselines

A dominant-cycle construction is taught as an explicit forecast-style baseline. Sampling-interval, frequency, and instrument stay as open-parameter inputs so later observations can be compared with that baseline, instead of with a setting locked by historical-search.

  • A dominant-cycle construction takes ordered observations, a defined sampling-interval, and a lookback, then emits a forecast-style baseline for later comparison.
  • Historical-search over size, clock, and indicator thresholds is treated as a source of overfit, including when a walk-forward search is used.
  • Direction, sampling-interval, and traded pair can each stay as an open-parameter so the same rules can be compared across buy versus sell, high versus low frequency, and alternative pairs.
  • Structured-randomization of a nearby level is offered so no single historically perfect value is reused, as a precursor to later comparison.
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Current trackDominant cycle detection119 readings

An explicit baseline, not a locked setting

A dominant-cycle construction takes ordered observations, a defined sampling-interval, and a lookback, then emits a forecast-style baseline for later comparison. The archive describes building an automated execution model as complex and bias-prone, with only the order step fully automated.

Programming is said to be about 22% of a typical software project, so the scarce work is designing logic that can be expressed in any language.

Two construction flaws

Two stated construction flaws are that a historical result need not repeat and that the space of logic choices is too large to search casually. Size, clock, and indicator thresholds are listed as typical inputs, and historical-search over them is said to produce overfit, including when a walk-forward search is used.

A perfect fit to past observations is presented as a construction error because later conditions differ. A forward walk is described as still carrying a bias about unseen data.

Heavy parameterization as a testing aid

Heavy parameterization is presented as a testing aid, not as a method for locking optimized values. Construction guidance is to leave even operational settings such as error correction and open-order limits as variables so unused knobs remain available.

Direction, sampling-interval or clock, and traded pair can each be coded as an open-parameter. Buy versus sell, high versus low frequency, and alternative pairs can then be compared on the same rules.

Structured-randomization of nearby levels

A structured-randomization offset is offered so nearby variants can be compared after the fact, as a precursor to later automatic analysis. The archive illustrates the idea with a 10-unit stop moved by 10% or 50%, so no single historically perfect value is reused.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
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201314-21 pp.Next on Dominant cycle detectionUsing a second-term election to check a predeclared dominant-cycle forecastA long-wave rise that was already used to explain a second term can be retested in the price window after the vote.
All readings on this track · 31 readings
  1. 1982Cycle phase windows for chart signal filters
  2. 1987Constructing a cycle-scaled trend oscillator
  3. 1987Constructing a dominant-cycle grid from marked lows
  4. 1988Cycle lead from staggered exponential averages
  5. 1988Auditing the forty-month stock-price cycle
  6. 1989When long-wave dominant cycles cannot be disproved
  7. 1991Half-cycle average plot shift versus cycle attenuation
  8. 1991Half-cycle average contact as an amplitude-ratio test
  9. 1993Building a restoring-pull indicator from cycle frequency and volume
  10. 1995Regime filters for a dominant long wave
  11. 1995A cycle-tuned lead filter from bounded oscillators
  12. 1998Testable cycle rules instead of fear and greed
  13. 1999Nested Euro cycle timing as one checkable procedure
  14. 2002Constructing an instantaneous trendline from a dominant cycle
  15. 2002Half-cycle center of gravity oscillator from moving-average balance
  16. 2004Testing a locked forty-week cycle with a hold-or-sit-out rule
  17. 2005Nested timing bands for dominant-cycle confirmation
  18. 2005Dominant-cycle baselines versus policy-news narratives
  19. 2006Pairing a dominant-cycle horizon with trend and oscillators
  20. 2006A dominant-cycle split into a trend filter and residual Relative Strength Index
  21. 2007Construct a momentum difference from the dominant cycle
  22. 2007Naive dominant-cycle rules fail without crowd tests
  23. 2012Constructing a dominant-cycle forecast as a timing window
  24. 2012Open-parameter construction of dominant-cycle baselines
  25. 2013Using a second-term election to check a predeclared dominant-cycle forecast
  26. 2014Constructing a dominant-cycle forecast baseline
  27. 2014Quotient transform as an early-onset trend filter
  28. 2014Construct a trough-to-trough cycle map with the Detrended Price Oscillator
  29. 2015Dominant-cycle alignment before an earnings catalyst
  30. 2017Causal reverse exponential average for cycle and trend
  31. 2020Constructing a cycle-plus-trend oscillator from a one-wavelength chord
By fieldCycles and spectral analysis77 readingsBy year201281 readings
All 119 readings tagged Dominant cycle detection
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1982Cycle phase windows for chart signal filters1982Scale-free Commodity Channel Index construction1984How to construct a maximum-entropy cycle model1984Constructing maximum-entropy spectra for dominant-cycle forecasts1984Half-cycle differencing for momentum signals