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2015issue C1122-25

Constructing fractal swings as support-resistance atoms

Fractal recognition is a construction problem: score every bar by how many neighbors confirm a swing, treat those neighbor-bar-counts as the smallest support-resistance atom, and only then ask whether the same atom appears on more than one time frame.

  • Unlabeled charts can look interchangeable across assets and horizons because similar participant reactions repeat across scale, while the displayed series remains residue of executed orders rather than a forecast of unplaced ones.
  • Every trend, once zoomed to its origin, begins at a lowest low or highest high that reduces to a three-bar-atom: a V or inverted V.
  • Four neighbor-bar-counts on each bar keep the same swing-chart and support-resistance rule on every time frame, so the reading does not depend on chart scale.
  • Multi-horizon-alignment is treated as a stronger construction cue than a single-scale signal, and it is asked only after the bar-level atom has been scored.
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Charts as residue, not forecast

Unlabeled charts of different assets and horizons can look interchangeable because similar participant reactions repeat across scale. A displayed market series is the residue of already executed orders, not a forecast of orders that have not yet been placed.

External impulses such as news and earnings enter the same loop as internal reactions to price, so the chart already mixes both. Price scale is not infinitely fine: a single transaction is the smallest unit, even though prints can be bucketed at many precisions.

Editorial reading: that mix of residue, impulse, and repeating reaction is why a swing can be built from bar structure alone, without first naming the asset or the horizon.

The three-bar atom

Every uptrend or downtrend, once zoomed to its origin, begins at a lowest low or highest high that reduces to a three-bar V or inverted V. That three-bar-atom is the smallest swing unit: a middle bar that is the lowest low or highest high of the three-bar pattern.

A fractal-indicator flags a bar as a local extreme when a stated number of neighboring bars print less extreme highs or lows. A one-bar down fractal is a middle bar with one higher high on each side. A two-bar up fractal has two flanking bars on each side of the middle high.

Published fractal rules disagree on how many neighbor bars are required and on whether those neighbors must form a monotonic sequence. Editorial note: that disagreement is why the construction problem is framed as a count on every bar, not as a single named pattern.

Four neighbor-bar-counts on every bar

Each bar can be scored with four neighbor-bar-count values: bars to the left and right that print a higher low, and bars to the left and right that print a lower high. Those neighbor counts are meant to stay the same construction rule on every time frame so swing and support-resistance readings do not depend on chart scale.

A swing-chart is a price map built from those confirmed highs and lows so a trend is drawn from pivot to pivot rather than from every print. Support-resistance, in this construction, is the set of levels implied by how many bars to the left and right fail to undercut a low or exceed a high.

Ask about other time frames last

Because the same techniques are applied on every horizon, a condition that appears on several time frames is treated as a stronger construction cue than a single-scale signal. Multi-horizon-alignment is that check: whether the same swing condition appears on more than one chart scale.

Editorial: alignment is a second question, not a substitute for the bar-level score. A three-bar-atom remains a valid construction atom even when neighboring horizons do not repeat it. The stack is asked only after the neighbor-bar-count has already defined the support-resistance atom.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
7 of 7 in the Fractal indicator track
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All readings on this track · 7 readings
  1. 1992Constructing fractal templates from successive index changes
  2. 1994Constructing polarized fractal efficiency as a path filter
  3. 2002Long memory, regimes, and the limits of bell-curve models
  4. 2003Constructing the fractal dimension index
  5. 2005Constructing a fractal-dimension adaptive moving average
  6. 2007Constructing a fractal-dimension regime filter
  7. 2015Constructing fractal swings as support-resistance atoms
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