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1982issue C061-6

Critiquing Fourier, Elliott, and seasonal commodity models

Cycle work belongs on the desk as a disposable baseline. Fourier layers, Elliott-wave templates, and seasonal-cycle calendars earn a place only when each layer can be isolated, scored against later prices, and retired after the market's structure changes.

  • Treat a commodity chart as a wave-package of stacked cycles, then isolate each layer before using it as a forecast baseline.
  • Fourier analysis is a spectral split for dominant seasonal periodicities, not a claim about physics harmonics.
  • An Elliott-wave count and a seasonal-cycle calendar are imposed shapes that stay only if later prices still support them.
  • No model remains best for long, so coefficients and variables must be revised, and a model that is not fully understood is overmathematization.
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Current trackFourier analysis41 readingsSeasonal analysis93 readingsElliott wave analysis165 readings

A chart as a wave-package

The archive treats a commodity price chart as a wave-package, meaning stacked dominant cycles rather than a single trend path. Fourier analysis is the spectral split used to pull those dominant commodity cycles out of the stacked series. It is named as a tool for isolating dominant seasonal periodicities rather than physics harmonics.

An Elliott-wave template on the stack

One imposed wave-package shape is an Elliott-wave count of five bull-market extrema followed by three bear-market extrema. The same template is restated as three upward peaks then two downward peaks. The count forces the stacked series into that advance-and-decline pattern.

Seasonal-cycle calendars

A seasonal-cycle is a calendar-linked tendency treated as one dominant wave. Seasonal calendars are given for several markets, including April peaks in metals and construction materials, midsummer peaks in meat, and harvest lows in October and November for corn, September and October for soybeans, and May and June for wheat.

Gold is assigned a seasonal-cycle that tops in the first quarter and bottoms in the second during bull markets. In bear markets a phase shift moves the bottom to the third quarter. Additional cycles of 6, 19, and 73 weeks and 5 years are also assigned to gold.

Inventory, open interest, and joint diagnostics

Agricultural inventory change is specified as new crop plus carryover plus imports, minus domestic use and exports, with an extra government-loan adjustment for grain. That specification sits with the archive note that deterministic formulas are rare and mainly a fundamental-analysis device. Most commodity trade models are described as stochastic or mixed.

Open-interest-deviation is the gap between current open interest and its seasonal norm. That gap is said to mark usual price-trend reversals at its lows. Joint moves in price, volume, and open interest are offered as multidimensional trend or reversal diagnostics, and some analysts add derivatives of open interest and other variables.

Revise coefficients or retire the model

The same survey warns that no model remains best for long because markets change, so coefficients and variables must be revised. A model should not be used unless it is fully understood. Running a formal model whose structure and coefficients cannot be fully justified is overmathematization.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
1 of 26 in the Fourier analysis track
19821-9 pp.Next on Fourier analysisEvaluating half-hour crossovers with cycle priors and stopsThe oscillator is the difference between a short and a long moving average of closes, and the stance stays always in the market until the opposite cross.
All readings on this track · 26 readings
  1. 1982Critiquing Fourier, Elliott, and seasonal commodity models
  2. 1982Evaluating half-hour crossovers with cycle priors and stops
  3. 1984Building moving-window spectral cycle forecasts
  4. 1988Fourier cycles versus calendar seasonal regimes
  5. 1989Constructing analog cycle signatures from price lows
  6. 1990Curve fits fail the forecast test
  7. 1991Auditing moving-average length with Fourier spectra
  8. 1991Closed-form phase for dominant-cycle spectra
  9. 1993Evaluating windowed spectral forecasts of index turning points
  10. 1994Window length for moving-window spectral forecasts
  11. 1994Constructing WAMI power and phase spectra
  12. 1994Fourier-tuned momentum filter design
  13. 1994Aliasing, leakage, and dominant-cycle reads from sampled prices
  14. 1995Constructing indicator lookbacks from Fourier spectra
  15. 1996Constructing phase from a dominant-cycle sine and cosine pair
  16. 1999Sinusoidal pseudo-securities for indicator evaluation
  17. 2000Constructing dominant cycle length from Hilbert phase
  18. 2000Constructing adaptive channel breakouts from a measured cycle period
  19. 2005Embedding forex prices into a singular-spectrum forecast basis
  20. 2005Constructing recurrent forecasts from grouped spectral components
  21. 2007Constructing a dominant cycle from transformed Fourier spectra
  22. 2010Why cycle indicators fail when parameters shift
  23. 2019Constructing futures momentum as open-interest-scaled rate of change
  24. 2019Judging a dominant cycle and a trend filter by sample, lag and the move they keep
  25. 2020Constructing futures turnover time and urgency
  26. 2020Stock power ratio as a growth trend filter
By fieldCycles and spectral analysis129 readingsBy year198223 readings
All 41 readings tagged Fourier analysis
Also on Fourier analysis5 readings
1988Fourier cycles versus calendar seasonal regimes1982Building FFT spectra to size cycle filters1982Evaluating half-hour crossovers with cycle priors and stops1984Building moving-window spectral cycle forecasts1984Constructing maximum-entropy spectra for dominant-cycle forecasts