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1989issue C041-6

Evaluating commodity cycle personalities with spectral histograms

A two-year maximum-entropy scan left each of 12 commodity contracts with its own cycle-length histogram. Editorial reading treats the accepted dominant-cycle peak as a contract-level score only after the 6-decibel clearance rate and the concentration of that histogram are both inspected.

  • A maximum-entropy spectral scan of short-term cycles on 12 commodity contracts over a two-year sample produced a distinct cycle-length histogram for each contract.
  • Contracts labeled as meats, grains, or metals did not necessarily share similar cycle histograms.
  • A reading was retained only when cyclic content reached at least 6 decibels, and contracts were then compared by how often that bar was cleared out of 451 chances and by the histogram peak taken as the dominant cycle.
  • The most cyclic contracts cleared the 6-decibel threshold on 30% to 40% of measured days and showed the most concentrated histograms. The S&P 500 histogram showed two peaks, at 9 days and at 14 days.
Entries in this reading3 entries
Spectral analysis24 readingsCurrent trackMaximum entropy spectrum analysis30 readingsDominant cycle detection119 readings

What the archive measured

A maximum-entropy spectral scan of short-term cycles on 12 commodity contracts over a two-year sample produced a distinct cycle-length histogram for each contract. Each daily price input was the midpoint of that day's high and low on a perpetual contract of 500 records.

After a 50-record warmup, dominant-cycle and cyclic-content values were recorded daily from record 50 through record 500, yielding 451 measurements. The maximum-entropy spectrum was the measurement engine for each lookback: a high-resolution spectral estimate taken from a short ordered price window.

Which readings were retained

A measurement was retained only when cyclic content, defined as a signal-to-noise ratio, reached at least 6 decibels. Energy with periods shorter than 5 days or longer than 40 days, including trend, was classified as noise. That noise band was excluded from the accepted cycle-length counts.

Spectral analysis, in this workflow, is the decomposition of an ordered price series into cycle-length energy so contracts can be compared by how often a length exceeds a noise floor.

How contracts were compared

Contracts were compared by how often the 6-decibel bar was cleared out of 451 chances and by the histogram peak taken as the dominant cycle, with a 2-day band around that peak. The dominant cycle is the cycle length at or nearest the accepted histogram peak after low-signal windows are removed.

In the summary table, the most cyclic contracts cleared the 6-decibel dominant-cycle threshold on 30% to 40% of measured days and also showed the most concentrated histograms.

Group labels and histogram shape

Contracts labeled as meats, grains, or metals did not necessarily share similar cycle histograms. A cycle personality is the recurring shape of a contract's accepted cycle-length histogram, including whether one peak or two dominate.

The S&P 500 histogram showed two peaks, at 9 days and at 14 days.

Editorial reading of the score

Editorial: clearance and concentration are separate checks. A contract that rarely cleared the 6-decibel bar does not give the same empirical score as one that cleared it on 30% to 40% of measured days and also sat in a tight histogram peak.

Editorial: because sector labels did not necessarily produce matching histograms, the archive comparison is read here as a contract-by-contract score, not as a group rule that can be copied from one meat, grain, or metal to another.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
8 of 28 in the Maximum entropy spectrum analysis track
19891-5 pp.Next on Maximum entropy spectrum analysisEvaluating next-session cycle forecasts with stopsNext-session direction came from the slope of a short projected path, not from a half-cycle hold at a peak or trough.
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 year198983 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 analysis1990Year-over-year dominant cycle personality auditSpectral analysis1992Stationarity states on synchronized futures spectral contoursSpectral analysis1997A spectral estimator that retunes indicators to the measured cycleSpectral analysis