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2012issue C0466-71

Assembling a market-mode heatmap from successive band-pass filters

A market-mode study can be rebuilt as a heatmap by applying a band-pass filter at successive periods, scoring trend against estimated cycle amplitude, and coloring the transformed ratio so cycling and trending conditions can be read across many lengths at once.

  • The construction displays wave and trend information as a multi-period heatmap rather than as a single-length oscillator.
  • A band-pass filter is applied at successive periods, then peaks and valleys of the filtered series are used to estimate cycle amplitude.
  • Trend versus cycle is scored as the ratio of a smoothed band-pass mean to that amplitude and passed through an inverse Fisher transform so the ratio stays between minus one and plus one.
  • A companion multi-period stochastic heatmap sits beside the band-pass and market-mode views so stochastic readings can be compared across lengths on the same chart.
Entries in this reading3 entries
Current trackSpectral analysis63 readingsDominant cycle detection180 readingsStochastic oscillator193 readings

A heatmap instead of one oscillator length

The featured construction examines market structure by displaying wave and trend information as a multi-period heatmap rather than as a single-length oscillator.

The heatmap is meant to show how several periods can be cycling or trending at once, adding a second dimension that a single-period market-mode line cannot display.

Band-pass filters at successive periods

One reconstruction applies a band-pass filter at successive periods, then measures peaks and valleys of that filtered series to estimate cycle amplitude.

Scoring trend versus cycle

Trend versus cycle is scored as the ratio of a smoothed band-pass mean to that estimated amplitude, then passed through an inverse Fisher transform so the ratio stays between minus one and plus one.

Color as trend or cycle mode

Heatmap coloring maps the transformed ratio toward red and green extremes for trend and toward yellow for cycle-mode or swing-oriented conditions.

Parameters that rebuild the study

Platform implementations expose adjustable parameters such as fastest and lowest period, delta, fraction, price source, and line thickness so the same market-mode study can be rebuilt on different charting systems.

A companion stochastic heatmap

Companion constructions include a multi-period stochastic heatmap alongside band-pass and market-mode views so classic stochastic readings can be compared across lengths on the same chart.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
16 of 21 in the Spectral analysis track
201220-27 pp.Next on Spectral analysisAdaptive trend detection from dominant-cycle spectraA tradable trend can be framed as a longer-period wave relative to the cycle under review, so one series can show a shorter cycle inside a larger advance.
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 year2012165 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