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1989issue C091-8

Constructing symmetric market-breadth ratio accumulators

Market-breadth construction is a scale problem. A ratio is not a cumulative advance-decline difference, a moving average of an unadjusted ratio is not a neutral baseline, and a moving-average crossover is a falsifiable hypothesis only after the series has been forced onto a two-sided, comparable scale.

  • A ratio is one series divided by another and, when the inputs share similar ranges, tends to oscillate around 1.0. A conventional advance-decline line is a cumulative difference, not that quotient.
  • The raw ratio that divides the advance/decline split by the up-volume/down-volume split cannot fall below zero but can rise without an upper bound, so a 39-day exponential moving average of the unadjusted series is pulled toward values above 1.0.
  • The unity-symmetry-transform maps each reading below 1.0 to its negative inverse, so that 0.5 becomes -2, centering the series on zero before smoothing or a ratio-accumulator.
  • After the volume terms are scaled, a 19-day exponential moving average of the advance/decline ratio oscillates near +1.0 to -1.0, and crossings of a 9-day and a 40-day exponential moving average can be plotted as a moving-average-crossover.
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Construction starts with scale

Market-breadth is participation measured from advancing versus declining issues and from up volume versus down volume, used to turn a repeatable chart condition into a testable hypothesis. The first construction choice is how those counts are combined.

A ratio is one series divided by another. When the two inputs share similar ranges, the quotient tends to oscillate around 1.0. A conventional advance-decline line is a cumulative difference between advancing and declining issues, not a ratio of those two counts.

What the combined ratio encodes

In a ratio that divides the advance/decline split by the up-volume/down-volume split, readings above 1.0 correspond to relatively more down volume in declining issues, and readings below 1.0 correspond to the opposite mix.

Asymmetry pulls the moving average

Because that raw ratio can rise without an upper bound but cannot fall below zero, a 39-day exponential moving average of the unadjusted series is pulled toward values above 1.0. A moving average is a lookback average of ordered price, volume, or breadth observations that supplies an explicit quantitative baseline over a defined sampling interval.

The unity-symmetry-transform

Mapping each reading below 1.0 to its negative inverse, so that 0.5 becomes -2, centers the series on zero and evens the spike distribution before further smoothing or accumulation. That step is the unity-symmetry-transform: it maps each reading below 1.0 to its negative inverse so magnitudes on either side of unity are comparable.

What still drifted after the transform

After that transform, a 39-day exponential moving average remained above zero through a 15% decline in the Dow Jones Industrial Average, and accumulating the transformed readings produced a series that kept rising except for a limited setback around October 1987. A ratio-accumulator is a running sum of transformed ratio readings intended to show directional persistence rather than a single-session imbalance.

An accumulator built from the advance/decline ratio alone did not show that persistent upward drift, which isolated the volume terms as the remaining source of bias.

Equal-weight adjustment of volume

Scaling the up-volume inputs down by 20% flipped the accumulated series from a rising slope to a falling slope. A 7.5% reduction was the adjustment chosen for the January 1984 to February 1989 window.

A crossover on a two-sided series

After equal-weight adjustment, a 19-day exponential moving average of the advance/decline ratio oscillated near +1.0 to -1.0, and crossings between a 9-day and a 40-day exponential moving average were plotted as trading signals. A moving-average-crossover is a signal formed when a shorter moving average of an adjusted breadth series crosses a longer one, converting a repeatable chart condition into a falsifiable trade hypothesis.

Range-normalization for comparison

Recalculating each series as 100 times (value minus minimum) divided by (maximum minus minimum) placed a raw advance-decline accumulation and an adjusted advance/decline-ratio series on a shared 0-100 scale so their paths could be compared. That recasting is range-normalization: each series becomes a percent of its own minimum-to-maximum span.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
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19891-7 pp.Next on Moving-average crossoverObjective crossover tests of Fibonacci wave ratiosAfter the first four Fibonacci-sequence pairs, successive ratios cluster near 0.618 and inverse pairs cluster near 1.618.
All readings on this track · 57 readings
  1. 1988Constructing moving averages: weights, smoothing and crossovers
  2. 1988Constructing breadth and average trend states
  3. 1989Evaluating an always-in-the-market moving-average crossover
  4. 1989Constructing symmetric market-breadth ratio accumulators
  5. 1989Objective crossover tests of Fibonacci wave ratios
  6. 1990Volume-adjusted moving average construction
  7. 1991Constructing a mechanical crossover on a synthetic price series
  8. 1991A two-speed breadth reading for intermediate market direction
  9. 1992A Deutschemark yield map with dual-average and relative-strength timing
  10. 1992Confirming currency-fund trends with a crossover and a filter
  11. 1992A moving-average slope filter for crossover signals
  12. 1992Occupancy and split-sample tests for average crossovers
  13. 1994Gold-mining seasonality and bond-fund duration switching
  14. 1994Price oscillator from two moving averages
  15. 1995Explicit exponential weights and binary entry filters
  16. 1996Currency futures crossover with slope, bond filter, and stop
  17. 1996Two-market average crossover entry with a fixed stop
  18. 1997Construction of a filtered three-average crossover
  19. 1998Two-group exponential average compression as a trend filter
  20. 1998Constructing r-squared trend filters with dual lookbacks
  21. 1998Moving-average length is a habit, not a secret
  22. 1999Solving the close that triggers a moving-average crossover
  23. 2000Kagi yang and yin control versus crossover noise
  24. 2000Constructing simple moving average crossover filters
  25. 2000Building a vertical-horizontal filter to gate trend signals
  26. 2000Two-average crossover as a check on trend following
  27. 2003Stacked exponential-average retracement entries and extreme stops
  28. 2003Evaluating oscillator thresholds against optimized crossovers
  29. 2004Constructing a semicycle trend-quality filter
  30. 2004Commodity subgroups labeled by crossover, support, or convergence
  31. 2004Full-window evaluation of crossover trend systems
  32. 2004Two-average trend filters as a classroom critique of indicator stacking
  33. 2005Three-layer confirmation from a moving-average cross, candles, and Q-stick
  34. 2005Charting put prices beside an equity breakdown
  35. 2005Range-gated moving-average crossover construction
  36. 2007Anticipating a simple-average crossover with a threshold-close
  37. 2007Anticipating moving-average crossovers one bar ahead
  38. 2007Lead-series moving-average crossovers with a stochastic and relative strength index
  39. 2007Next-bar SMA crossover hypotheses from theoretical crossing values
  40. 2007Anticipating a moving-average crossover before confirmation
  41. 2007A three-horizon moving-average stack as a construction problem
  42. 2007Confirming trend with regression slope and r-squared
  43. 2008Constructing a multi-timeframe smoothed crossover
  44. 2008Best-day clusters versus trend filters
  45. 2008Allied markets as a confirmation gate for crossover and breakout signals
  46. 2008Weekly exponential-average crossover as a mechanical trend case study
  47. 2010Evaluating a 200-day crossover as long, short, and stand-aside rules
  48. 2010Read a 10-and-40 trend on two neighboring time frames
  49. 2012Sampling unit as a first-class parameter on dual simple moving averages
  50. 2012Constructing index-ETF entries from volatility-index persistence
  51. 2013Moving-average baselines versus crossover signals
  52. 2013Constructing a typical-price and heikin-ashi crossover as one mechanical procedure
  53. 2016A three-gate checklist for longs after a sharp drop
  54. 2016Weekly inflation-ratio crossover for commodity regimes
  55. 2017Normalized Laguerre zero-axis warning as a two-marker construction
  56. 2019Range-weighted construction of an adaptive exponential moving average
  57. 2020Construct a second-pullback entry after a moving-average crossover
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