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2015issue C0610-19

Constructing price paths from cycles and support

A constructed price-movement model treats a chart as two hidden layers: a support-and-resistance skeleton from located supply-and-demand pools, plus a set of cyclic components. The plotted path is the summed contribution of those cycles, with remaining unexplained moves treated as news-driven impulses.

  • Treat the chart as a support-and-resistance skeleton plus cyclic components. The plotted path is the sum of those cycles, and leftover moves are treated as news-driven impulses.
  • Spectral analysis turns ordered price bars into a spectrogram heatmap that shows each detected cycle’s wavelength and relative power once a minimum power threshold is cleared.
  • Isolating the three or four strongest spectrogram cycles, summing them, and adding that sum to a longer-term low-lag average rebuilds the original price path.
  • Empirically defended support and resistance can constrain cycle amplitude until the local pool is absorbed. Apparent trendline resistance can be an artifact of cyclic summation.
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Two layers in a constructed price path

A constructed price-movement model treats a chart as two hidden layers: a support-and-resistance skeleton from located supply-and-demand pools, plus a set of cyclic components.

Under that construction, the plotted path is the summed contribution of component cycles, with remaining unexplained moves treated as news-driven impulses.

Lock the support-and-resistance skeleton first

Support and resistance, in this construction, means empirically located pools of supply and demand that form a price skeleton and can constrain cycle amplitude until those pools are absorbed.

Empirically located support and resistance at defended prices is distinct from apparent trendline resistance. Apparent trendline resistance can be an artifact of cyclic summation rather than an absorbed supply-or-demand pool.

Read cycles on a spectrogram heatmap

Spectral analysis converts a price series into a spectrogram that reports each detected cycle’s wavelength and relative power. That frequency-domain decomposition of ordered price bars is usually displayed as a spectrogram or heatmap.

A heatmap spectrogram plots, bar by bar, only cycles that clear a minimum power threshold, with wavelength scaled in bars and relative power shown by line brightness and thickness.

Selecting a bar interval is equivalent to selecting a subset of cycles. A spectrogram covering 10 to 140 bars typically shows about half a dozen cycles strong enough to appear.

Rebuild the path from a few strong cycles

In more liquid symbols, detected cycles often remain relatively stable for hundreds of bars. Amplitudes can shift relative to each other without a wavelength change, and the strongest cycle on the heatmap can change.

The dominant cycle is the strongest wavelength isolated from a spectrogram and is used as a primary building block when reconstructing a price path from a few cyclic components.

Isolating the three or four strongest spectrogram cycles and summing their contributions produces a detrended reconstruction. Adding that sum to a longer-term low-lag average rebuilds the original price path.

Familiar chart shapes as summed cycles

Chart patterns such as head-and-shoulders, channels, and flags can be constructed by adding shorter cycles with shifted phase and smaller amplitude to a longer cycle.

Amplitude meets defended prices

Cycle amplitude is treated as a consensus of participants’ expectations within a chosen time frame. Defended prices can constrain that amplitude until the local supply-or-demand pool is absorbed.

The useful distinction is apparent versus defended levels: trendline turns that are artifacts of summed cycles, against defended prices where volume actually absorbs a supply or demand pool.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
18 of 21 in the Spectral analysis track
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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
All 63 readings tagged Spectral analysis
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