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2011issue C0560

Evaluating fixed oscillators against a moving dominant cycle

Cycle heat maps and a dominant-cycle trace show that the estimated period is not constant, so a Relative Strength Index left on a fixed lookback can fall out of alignment with the spectral period estimate.

  • Cycle heat maps show that the number of periods used in the estimate is not constant through the sample.
  • Period variation is more conspicuous on the dominant-cycle trace and the sinewave readout than on the heat map alone.
  • The measured cycle length can hold for more than a single session and then reset on a multi-day to roughly two-week cadence.
  • An oscillator kept at a fixed lookback after the dominant cycle has already shifted is no longer aligned with the spectral period estimate.
Entries in this reading3 entries
Current trackSpectral analysis63 readingsDominant cycle detection180 readingsRelative Strength Index261 readings

A moving period, not a fixed window

The dominant cycle is the period length that currently accounts for the strongest regular fluctuation in an ordered price, volume, or breadth series. Spectral analysis is a frequency-domain reading of a market series that estimates which cycle lengths are active instead of assuming a constant window.

A cycle heat map is a time-versus-period intensity plot showing which lookback lengths remain active as the sample advances. Cycle heat maps show that the number of periods used in the estimate is not constant through the sample.

Cycle drift is the tendency of the estimated dominant period to stay put for more than one session and then reset over several days to a couple of weeks. The measured cycle length can hold for more than a single session and then reset on a multi-day to roughly two-week cadence.

Where the shift is easier to see

Period variation is more conspicuous on the dominant-cycle trace and the sinewave readout than on the heat map alone. The sinewave readout is a pair of phase-offset waves derived from the current cycle estimate, used to visualize turning structure once a period is selected.

A dual sinewave display can be split across two subcharts and then merged so both waves share a single pane. Trend vigor is a directional-intensity pane that can be drawn in histogram form so persistent up-phases and down-phases separate more clearly than on a single line. Drawing trend vigor as a histogram-style pane makes up-phases and down-phases easier to distinguish than a line-only view.

The same spectral toolkit can appear on different charting hosts with incomplete feature parity, including cases where a heat map is absent but other cycle indicators remain.

What a fixed Relative Strength Index still answers

Relative Strength Index is defined as an oscillator used to locate stretched readings and divergences, and therefore inherits whatever period the analyst supplies. It is a bounded oscillator built from recent up and down closes and commonly read for stretch and divergence. Its lookback is a chosen period, not an automatic cycle estimate.

An oscillator kept at a fixed lookback after the dominant cycle has already shifted is no longer aligned with the spectral period estimate.

Editorial interpretation: adaptive lookback means recalculating an oscillator window from the latest dominant-cycle estimate rather than leaving the period fixed. The archive workflow is a check on that alignment, not a claim that either setting outperforms the other.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
14 of 21 in the Spectral analysis track
201155-59 pp.Next on Spectral analysisA construction checklist for a dominant-cycle sinewaveDetrend the series, then use spectral analysis to measure the dominant cycle so later work follows that period rather than a fixed lookback.
All readings on this track · 21 readings
  1. 1982Spectrum-first construction of moving-average trend filters
  2. 1990Crowd questions that switch cycle regimes
  3. 1993Antithetic twins to cancel origin-linked forecast bias
  4. 1994Constructing frequency response from a three-stage filter cascade
  5. 1994Bench-testing smoothers with cycle phase and power
  6. 1995Constructing minimum tradable cycles from spectral noise
  7. 1995Spectral whipsaw limits for a price-crossing average
  8. 1996Constructing market phase from dominant-cycle components
  9. 2000Constructing a Hilbert squelch dominant-cycle gate
  10. 2007When cycle mode fails: regime gates for spectral trades
  11. 2008Constructing singular-spectrum forecast baselines
  12. 2008Stacking a midband cycle gate on a short-memory trend filter
  13. 2011Engineering spectral cycle detection and moving-average harmony
  14. 2011Evaluating fixed oscillators against a moving dominant cycle
  15. 2011A construction checklist for a dominant-cycle sinewave
  16. 2012Assembling a market-mode heatmap from successive band-pass filters
  17. 2012Adaptive trend detection from dominant-cycle spectra
  18. 2015Constructing price paths from cycles and support
  19. 2018Oscillator distribution shape for mean-reversion filters
  20. 2020Constructing a cosine-correlation cycle and phasor trend filter
  21. 2020Constructing a correlation-cycle market-state engine
By fieldCycles and spectral analysis129 readingsBy year2011131 readings
All 63 readings tagged Spectral analysis
Also on Spectral analysis5 readings
1984How to construct a maximum-entropy cycle model1984Building moving-window spectral cycle forecasts1987Assembling short-lookback maximum-entropy cycle forecasts1987Evaluating angular cycle overlays on independent stock swings1989Constructing analog cycle signatures from price lows