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1997issue C021-3

Hidden horizon assumptions in dominant-cycle readings

A published letter asked why a power-spectrum peak should match a trader's requirements rather than how the peak is computed. The reply treated the lookback as a property of dynamic-range, treated bar size as interchangeable sampling, and required a cycle-mode versus trend-mode test. Editorial reading: write those choices down before treating one dominant-cycle as the series itself.

  • A power-spectrum peak names a dominant-cycle only after analysis-span, bar size, and dynamic-range have already been chosen.
  • A published letter asked for a working definition of dominance and why that peak should match a trader's timeframe rather than the computation that produced it.
  • The reply treated lookback as a property of the method's dynamic-range and treated daily, weekly, or hourly bars as interchangeable samples of the same observations.
  • Because the series can sit in trend-mode or cycle-mode, and because cycle-drift is expected, a measured wavelength is a current reading rather than the series itself.
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Spectral analysis24 readingsCurrent trackMaximum entropy spectrum analysis30 readingsDominant cycle detection119 readings

What the letter asked

A published letter argued that the cycle of practical interest is relative to a trader's style and preferred timeframe, which a mathematical detector does not automatically encode.

The same letter asked for the assumptions used when a technique names one cycle dominant, and for a working definition of that term. It tied dominance to a power-spectrum peak from earlier cycle writing, then asked why that peak should match a trader's requirements rather than how the peak is computed.

In the terms used here, a dominant-cycle is the single wavelength a detector treats as the strongest periodic component in a finite sample of ordered prices. Spectral-analysis is any decomposition of those observations into cycle-length or frequency components so a peak can be compared with a chosen sampling interval and horizon. The power-spectrum is a plot of strength versus cycle length, and the tallest peak is the operational stand-in for dominance.

Lookback, bar size, and dynamic-range

The reply stated that the lookback used to find a dominant-cycle is mainly a property of the measurement method's dynamic-range. Dynamic-range is the ratio of longest to shortest cycle a given detector can report without changing its internal parameters. Analysis-span is the lookback window that decides which cycle lengths can be resolved and how quickly a drifting cycle is noticed.

The reply stated that one maximum-entropy-spectrum cycle measurement remains valid across an eight-to-one span from 6 to 50 bars without adjusting internal parameters. Because the input is sampled observations, the reply stated that it matters little whether those bars are daily, weekly, or hourly.

The reply invoked amplitude-proportionality, the claim that a longer measured cycle tends to carry a larger price swing when the same method is applied at another sampling interval.

Mode first, then a wavelength

The reply treated the series as switching between a trend-mode and a cycle-mode, and treated identifying the current mode as necessary before choosing entries and exits. Trend-mode is a state the reply treats as non-oscillatory drift, so a cycle-timed rule would be the wrong instrument. Cycle-mode is a state the reply treats as oscillatory, so entries and exits would be keyed to a measured wavelength.

The reply stated that measured cycles appear, disappear, and drift from one length to another. Cycle-drift is that tendency inside the same series. The useful task, on the reply's account, is to track that change as it occurs.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
15 of 28 in the Maximum entropy spectrum analysis track
19971-5 pp.Next on Maximum entropy spectrum analysisWhen market cycles are absent more than presentTradable market cycles are estimated to be present only about 15% to 30% of the time, so a cycle method must detect presence rather than assume a standing wave.
All readings on this track · 28 readings
  1. 1984Constructing maximum-entropy spectra for dominant-cycle forecasts
  2. 1984How to construct a maximum-entropy cycle model
  3. 1984Constructing a maximum-entropy forecast from a chosen lookback
  4. 1985Constructing period-locked half-cycle and full-cycle averages
  5. 1986Why Fourier windows limit dominant-cycle resolution
  6. 1987Assembling short-lookback maximum-entropy cycle forecasts
  7. 1988Why a fitted dominant cycle is not a forecast
  8. 1989Evaluating commodity cycle personalities with spectral histograms
  9. 1989Evaluating next-session cycle forecasts with stops
  10. 1989Constructing cycle-aged volatility trailing stops
  11. 1990A channel signal-to-noise gate for dominant-cycle forecasts
  12. 1990Year-over-year dominant cycle personality audit
  13. 1991Cyclic entry from a locked dominant-cycle phase
  14. 1992Stationarity states on synchronized futures spectral contours
  15. 1997Hidden horizon assumptions in dominant-cycle readings
  16. 1997When market cycles are absent more than present
  17. 1997A spectral estimator that retunes indicators to the measured cycle
  18. 2000Constructing a Hilbert dominant cycle and a maximum-entropy refinement
  19. 2000Switch trend and cycle indicators after a half-cycle dwell test
  20. 2000Constructing a dominant-cycle squelch trend filter
  21. 2000Phasor displays for dominant-cycle construction
  22. 2002Low-lag trendline from elliptic and dominant-cycle notches
  23. 2004Spectral peaks are mode diagnostics, not forecasts
  24. 2004Compressive last-stage oscillator construction
  25. 2013Constructing trend failure curves from qualified-trend transitions
  26. 2014Lookback range, a two-lag smoother, and next-bar fills
  27. 2014Constructing a MESA stochastic with roofing and SuperSmoother filters
  28. 2016Constructing spectral heatmaps for dominant market cycles
By fieldCycles and spectral analysis77 readingsBy year199756 readings
All 30 readings tagged Maximum entropy spectrum analysis
Also on Maximum entropy spectrum analysis5 readings
1984How to construct a maximum-entropy cycle modelSpectral analysis1987Assembling short-lookback maximum-entropy cycle forecastsSpectral analysis1989Evaluating commodity cycle personalities with spectral histogramsSpectral analysis1990Year-over-year dominant cycle personality auditSpectral analysis1992Stationarity states on synchronized futures spectral contoursSpectral analysis