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2018issue C0630-35

Constructing an adaptive filter for adoption-cycle reversals

The constructed filter joins an adaptive moving average, an adoption-of-price histogram, and a supply-demand state in one visual output. Editorial reading: students can use that single readout to test when participation is still rising even as new buyers start to fade.

  • Assemble the adaptive moving average, the adoption-of-market-price histogram, and the supply-demand state as one visual output rather than three separate readouts.
  • Map buyer and seller arrival onto a bell-shaped path so new participation can be located as increasing, saturating, or already fading.
  • Treat a decline in new adopters as a leading participation change and a mean-reversion setup, not as proof that total participation or price has already peaked.
  • Complete the trend-filter state only when filter distance, participation pace, and the shift from demand-side to supply-side control agree.
Entries in this reading3 entries
Current trackAdaptive moving average28 readingsMean reversion43 readingsTrend filter137 readings

One shared visual output

The constructed filter is assembled so an adaptive moving average, an adoption-of-price histogram, and a supply-demand state share one visual output rather than three separate readouts.

The adaptive moving average is a responsive, smoothed price filter that updates with current observations and serves as the baseline from which diversions and reversions are measured.

Mapping arrival on the adoption path

The adoption-of-market-price construction maps buyer and seller arrival onto a bell-shaped path so a trader can locate where new participation is still increasing, saturating, or already fading. Adoption-of-market-price is a market analogue of an innovation-adoption curve in which new buyers or sellers arrive, saturate, and then thin while total participation can still rise.

A decline in new adopters is specified as a leading participation change, not as immediate proof that total participation or price has already peaked.

The early-warning histogram is the constructed series that maps participation arrival and fade as a bell-shaped path around the adaptive filter so a shift from demand toward supply can be seen before price fully turns.

Measuring diversion from the adaptive filter

The same decline is treated as a mean-reversion setup: fewer participants willing to buy at the prevailing price is aligned with a later reversion toward the adaptive filter within a defined number of bars. Mean reversion is the tendency of price to return toward the adaptive filter after a moderate-to-strong diversion, treated as a testable turning-point process rather than a forecast of the next print.

The adaptive filter is the baseline for measuring how far price has diverted and how it later reverts. Those irregular swings, not a perfect parabola, are what the histogram is built to display.

In moderate-to-strong diversions away from the adaptive filter, a bell-shaped histogram is expected to become distinguishable even when the live path is chaotic and does not match an idealized curve.

When the trend-filter state is complete

When new demand fades while offers continue to arrive, the construction flags a supply-over-demand lag that can precede a downward turn. The opposite fade in sellers is used to flag upward pressure.

The trend-filter state is complete only when three constructed readings agree: strength of diversion and reversion from the adaptive filter, the pace of new participation on the adoption path, and the shift from demand-side to supply-side control as that path declines.

A trend-filter is a visual state of supply versus demand that advances or withholds a directional read when the adoption histogram, filter distance, and participation path agree.

AAPL price against the triple-passband Laguerre filter

On the published Apple window, the close still presses into the mid-176s after the Laguerre line has already flattened near 174, then slips back through that line. That lag is the early-warning the article is teaching. Dollar levels were read from the labeled price scale on the 24 Oct–28 Nov 2017 screenshot and rounded to the nearest half dollar.
On the published Apple window, the close still presses into the mid-176s after the Laguerre line has already flattened near 174, then slips back through that line. That lag is the early-warning the article is teaching. Dollar levels were read from the labeled price scale on the 24 Oct–28 Nov 2017 screenshot and rounded to the nearest half dollar.AAPL · 24 Oct 2017 – 28 Nov 2017 · 2017-10-24T00:00:00.000Z to 2017-11-28T00:00:00.000Z

The Early Warning histogram was not digitized because its vertical scale is overprinted and not uniquely labeled. Session dates follow the published window sampled along the raster; they are not a tick-by-tick extract.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
22 of 24 in the Adaptive moving average track
20188-11 pp.Next on Adaptive moving averageConstructing a deviation-scaled adaptive moving averageAn exponential moving average mixes the current close with the previous filter value through an alpha that lies between zero and one.
All readings on this track · 24 readings
  1. 1991Building variable-length moving averages from partitioned price changes
  2. 1991Variable-length moving average from change dispersion
  3. 1992Constructing volatility-adaptive exponential smoothing
  4. 1995Constructing an adaptive moving average with an efficiency ratio and filter
  5. 1995Two-gate breakout confirmation with adaptive averages
  6. 1995Building momentum-scaled adaptive moving averages
  7. 1995Adaptive length as a construction choice inside exponential smoothing
  8. 1998Testing price-channel breakouts with a lag-aware adaptive average
  9. 1998Constructing filters by nesting offsets and variable weights
  10. 1998Constructing an efficiency ratio adaptive average and entry filter
  11. 2001Encoding candle structure as a numeric filter
  12. 2001Adaptive averages driven by cycle-phase speed
  13. 2005Constructing an adaptive moving average from a fractal-dimension weight
  14. 2005Range-dimension adaptive exponential filter
  15. 2010Constructing simple, exponential, and adaptive averages
  16. 2010How a price-hugging smoother is assembled from ordinary averages
  17. 2013Evaluating an adaptive moving average against a same-window moving average
  18. 2016Three-layer confirmation: adaptive average, stochastic relative strength index, and stop-and-reverse
  19. 2017One-alpha reverse-path exponential smoothing
  20. 2018Pair two adaptive averages to filter swing turns
  21. 2018Two Adaptive moving averages as a confirmation pair
  22. 2018Constructing an adaptive filter for adoption-cycle reversals
  23. 2018Constructing a deviation-scaled adaptive moving average
  24. 2020Walk-forward and adaptive averages as two tests of the same trend
By fieldIndicators and filters682 readingsBy year201898 readings
All 28 readings tagged Adaptive moving average
Also on Adaptive moving average5 readings
1995Constructing an adaptive moving average with an efficiency ratio and filter1998Constructing an efficiency ratio adaptive average and entry filter2018Two Adaptive moving averages as a confirmation pair1991Building variable-length moving averages from partitioned price changes1991Variable-length moving average from change dispersion