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1989issue C121-7

Objective crossover tests of Fibonacci wave ratios

Fibonacci retracement and Elliott wave analysis treat limits near 0.618 and 1.618 as expected fractions of a prior swing. In the historical workflow, upwaves and downwaves were locked from local extremes between moving-average crossovers, then wave ratios were measured on Dow Jones Industrial Average prices through January 30, 1986. None of those crossover-defined means equaled 1.618, and every moving-average length showed large dispersion.

  • After the first four Fibonacci-sequence pairs, successive ratios cluster near 0.618 and inverse pairs cluster near 1.618.
  • Upwaves and downwaves were defined from local extremes between moving-average crossovers so trend turns did not depend on discretionary labeling.
  • For 10-day crossovers, a two-tailed test rejected a mean of 1.618 at the 1% level for total-advance-to-net-advance and advancing-to-declining days, and at the 5% level for total advances versus total declines.
  • None of the crossover-defined wave-ratio means equaled 1.618, and every moving-average length showed large dispersion with standard deviations on the order of the respective means.
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Ratios treated as expected fractions

Fibonacci retracement uses Fibonacci-sequence proportions, especially the limits near 0.618 and 1.618, as expected fractions of a prior price swing. After the first four Fibonacci-sequence pairs, successive ratios cluster near 0.618 and inverse pairs cluster near 1.618.

Elliott wave analysis is a self-similar count of five-wave impulses and three-wave corrections applied at more than one time scale. A recursively subdivided five-up and three-down wave count would be expected to produce ratios near 0.618 if that self-similar structure described prices.

The golden ratio is that limiting Fibonacci value near 0.618, with inverse near 1.618, treated as a testable mean for wave ratios rather than a chart decoration.

A cited split near the limit

One cited split of 8143 bull-market days against 4972 bear-market days equals 0.611, near 0.618, while a single cited primary-swing ratio of 1.621 was not matched by the other eight bull markets in that set.

Editorial reading: one day-count split near 0.618, and one primary-swing ratio near 1.618, do not show that those constants are general wave-ratio means.

Waves locked by crossovers

A moving-average crossover is a trend-turn rule that labels an upwave or downwave from the local extreme between index crossings of a moving average. Upwaves and downwaves were defined from local extremes between moving-average crossovers so trend turns did not depend on discretionary labeling.

A wave ratio is a measured comparison inside a defined wave, such as advancing days to declining days or total advance to total decline. Wave statistics were computed on Dow Jones Industrial Average prices through January 30, 1986 across moving-average lengths of 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, and 100 days.

Tests of a 1.618 mean

For 10-day crossovers, a two-tailed test rejected a mean of 1.618 at the 1% level for total-advance-to-net-advance and advancing-to-declining days, and at the 5% level for total advances versus total declines.

For 20- and 30-day crossovers the same 1.618 mean generally could not be rejected at the 1% level, except that the 20-day total-advance-to-net-advance ratio could be rejected at the 10% level.

None of the crossover-defined wave-ratio means equaled 1.618, and every moving-average length showed large dispersion with standard deviations on the order of the respective means.

Subwaves in two 1985 intervals

Inside the September 18, 1985 to January 7, 1986 advance, 10-day subwave advance/decline ratios were 2.534, 2.982, 5.903, and 2.758, with 50 advancing versus 26 declining days and a total-advance-to-net-advance ratio of 1.338.

In the June 20, 1985 to September 18, 1985 interval there were 35 advancing and 26 declining days, and the average advance of 4.606 points versus the average decline of 6.108 gave a ratio of 0.754.

Crossover-defined DJIA wave ratios against 1.618

Each point is one Dow Jones Industrial Average upwave or downwave whose turns were locked by a 10-, 20-, or 30-day moving-average crossover between 13 May 1985 and 30 January 1986. Day-count ratios sit well above the 1.618 Fibonacci constant, while total-advance-to-net-advance ratios cluster near 1.0–1.4, with only a few readings (1.581, 1.597, 1.621) near that constant. Numbers are the four-column observation table printed as Figure 3, not a redrawn curve.
Each point is one Dow Jones Industrial Average upwave or downwave whose turns were locked by a 10-, 20-, or 30-day moving-average crossover between 13 May 1985 and 30 January 1986. Day-count ratios sit well above the 1.618 Fibonacci constant, while total-advance-to-net-advance ratios cluster near 1.0–1.4, with only a few readings (1.581, 1.597, 1.621) near that constant. Numbers are the four-column observation table printed as Figure 3, not a redrawn curve.Dow Jones Industrial Average · daily · 1985-05-13T00:00:00.000Z to 1986-01-30T00:00:00.000Z

Down-wave entries already use the reciprocal form (declining over advancing) so both directions can be compared with 1.618. The 20- and 30-day samples are small; the author treated failure to reject 1.618 there as a sample-size issue, not as support for a Fibonacci mean.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
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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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