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1998issue C021-7

Two-group exponential average compression as a trend filter

Two exponential-average groups, separated by a lookback gap, are read together. Simultaneous narrowing is the usable warning, the slower group confirms direction, and a group crossover is used only after both bands have compressed.

  • Exponential weighting loads more influence onto recent observations, which changes how longer lookbacks behave around turning points.
  • The short-horizon average group uses 3-, 5-, 8-, 10-, 12- and 15-day lengths. The long-horizon average group uses 30-, 35-, 40-, 45-, 50- and 60-day lengths, with a lookback gap left unused between them.
  • A usable warning is dual-group convergence: both groups narrow in the same compression window. Tightening in only one group is treated as temporary short-horizon weakness.
  • After both bands narrow, the cue is the group-crossover direction while the long-horizon averages confirm the prevailing trend. Short-group expansion, or later tightening of the fastest two or three averages, is an exit cue, not a new entry rule.
Entries in this reading3 entries

A multiple-moving-average stack is a single overlay of many exponential averages arranged in two lookback groups. Compression, expansion, and group alignment are read as market-state information rather than as a single numeric threshold.

The short-horizon average group is the faster cluster. It is used to show near-term agreement on value and the first visual tightening. The long-horizon average group is the slower cluster. It is used to confirm broader direction and to reject setups that tighten in only the short group.

Two gapped exponential groups

Exponential averages are chosen because recent observations receive more weight. That exponential weighting changes how longer lookbacks behave around turning points.

The short-horizon average group is built from 3-, 5-, 8-, 10-, 12- and 15-day exponential averages, spaced around half-weekly sampling steps. The long-horizon average group starts by doubling the last short lookback to 30 days, then adds 35-, 40-, 45-, 50- and 60-day exponential averages.

The construction leaves a lookback gap of unused intermediate lookbacks between the two groups so constriction and crossing remain easier to see.

Compression as collapsing agreement

On the illustrated daily series, averages from widely different lookbacks often converge in a short window just before and during major turns instead of crossing in a long staggered lag.

That interval is a compression window: a short span in which several averages from one or both groups narrow together. It is treated as collapsing agreement on value rather than a single price print.

A usable warning is defined as simultaneous narrowing across both groups. That dual-group convergence is treated as a stronger warning than compression confined to one group. Tightening in only one group is treated as temporary short-horizon weakness.

When a group crossover is read

A crossover is read as momentary agreement on value across time frames, not only as a possible change in price direction. That second reading is valuation agreement: two or more lookbacks momentarily price the same series similarly.

After both bands narrow, the operational cue is to follow the direction of the group crossover while the long-horizon averages confirm the prevailing trend. The group crossover is the directional crossing of the short group through the long group. It is used as the long or short cue only after compression has already formed.

Short-group expansion, or a later tightening of the fastest two or three averages, is treated as an exit cue rather than as a new stand-alone entry rule.

A confirmatory trend filter

The stack is used as a confirmatory trend filter after other price or indicator screens. It is not framed as an initiating formula or a single numeric threshold.

In that role, the long-horizon average group is the trend filter. It keeps candidates aligned with the broader average stack and discards those that fail the dual-group test.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
19 of 57 in the Moving-average crossover track
19981-7 pp.Next on Moving-average crossoverConstructing r-squared trend filters with dual lookbacksA linear regression of price on time returns slope, a fitted forecast, the standard error of the estimate, and r-squared. A simple moving average smooths noise but does not report those quantities.
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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