Skip to main content
Track Moving-average crossover
29 / 57
Search the archiveLibrary
Library

2004issue C041-3

Constructing a semicycle trend-quality filter

A two-stage trend filter first cuts price into directed up and down semicycles with a small pair of exponential averages, then grades each stretch by how far measured trend exceeds estimated noise.

  • The moving-average crossover is a segmentation rule that starts and ends directed semicycles, not a standalone trade.
  • Each semicycle is graded by how far measured trend exceeds estimated noise, not by which average sits on top.
  • The Q-indicator is a centered trend-to-noise oscillator; the B-indicator reports whether a trend exists and how strong it is on a zero-to-100 scale and still needs a separate reversal tool.
  • Averaging-period choice is a sensitivity tradeoff between tracking price closely and reducing false reversals.
Entries in this reading3 entries
Current trackMoving-average crossover108 readingsExponential smoothing117 readingsTrend filter137 readings

Two stages, not one signal

This archive note reconstructs a two-stage trend filter. The first stage is a moving-average filter limited to a small set of term-oriented averaging periods. That filter marks reversal points and the up and down semicycles that follow. The second stage scores whether the extracted trend looks promising and how strong it is.

A semicycle is the directed stretch of price between two successive moving-average reversal points. Exponential smoothing supplies the averages: a recursively weighted average applied to price, or later to cumulative price change, so recent observations dominate the extracted trend.

How semicycles are cut

Semicycle start points can be defined with a moving-average crossover, the rule that marks where a shorter and a longer average exchange rank. In this construction the crossover is used only to start and end semicycles.

Averaging-period choice is a sensitivity tradeoff. A shorter period tracks price more closely but admits more random fluctuations. A longer period lags current prices and reduces false reversals.

Trend inside the segment

Cumulative price change from that start is the running sum of successive price differences. The trend inside the semicycle is a moving average of that cumulative series after the start.

Noise and the Q-indicator

Noise is an n-period average of the absolute gap between cumulative price change and trend, or the root-mean-square of that gap. The noise lookback n is specified to be longer than the trend lookback m.

The Q-indicator is a centered oscillator formed by dividing the semicycle trend by a noise estimate and applying a correction factor. Strength is expressed relative to background fluctuation rather than as an absolute price move.

Published Q-indicator bands treat absolute readings at or below 1 as trend buried in noise, 1 to 2 as weak, 2 to 5 as moderate, and beyond 5 as strong. Readings past the strong band are described as stretched conditions that warrant closer monitoring.

The B-indicator

The B-indicator is a 0-to-100 banded oscillator equal to the absolute trend divided by the sum of absolute trend and noise, then scaled by 100. It reports whether a trend exists and how strong it is, not its direction, so a separate reversal tool is required.

Interpretation places equilibrium, where trend equals noise, at the center line. Weak trending sits in the 50-65 band, moderate trending in 65-80, and strong trending above 80. The value 65 is used as a cutoff between trending and ranging conditions.

Short-horizon and long-horizon illustrations

Worked illustrations construct a short-horizon Q-indicator from a 7-day and 15-day exponential moving-average crossover, and a long-horizon B-indicator from a 10-week and 40-week exponential moving-average crossover.

Editorial: those pairs are presented as fixed, term-oriented choices, one short and one long, rather than as a search across many averaging periods.

Citrix short-term Q-indicator, Aug 2002–May 2003

The Q-indicator stays near zero through the late-summer congestion, then climbs above the +2 promising-trend line in November and peaks near +11 in January before resetting. A second push in April again clears +5. Values were read off the histogram pane of the Citrix Systems chart (seven-day vs 15-day EMA semicycles), not from a table.
The Q-indicator stays near zero through the late-summer congestion, then climbs above the +2 promising-trend line in November and peaks near +11 in January before resetting. A second push in April again clears +5. Values were read off the histogram pane of the Citrix Systems chart (seven-day vs 15-day EMA semicycles), not from a table.CTXS · daily · 2002-08-07T00:00:00.000Z to 2003-05-02T00:00:00.000Z

Digitized from the plotted Q histogram. Semicycles come from the 7-day and 15-day EMA crossover named in the figure. y is approximate to about half a Q unit. The +2 line is the article’s cutoff for a promising trend.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
29 of 57 in the Moving-average crossover track
20041-2 pp.Next on Moving-average crossoverCommodity subgroups labeled by crossover, support, or convergenceA production-weighted commodity index can be reviewed through precious-metals, energy, agriculture, and livestock subgroups on weekly charts that use both 50-day and 200-day simple moving averages.
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
By fieldIndicators and filters682 readingsBy year2004107 readings
All 108 readings tagged Moving-average crossover
Also on Moving-average crossover5 readings
2017Normalized Laguerre zero-axis warning as a two-marker construction1988Constructing moving averages: weights, smoothing and crossovers1992Occupancy and split-sample tests for average crossovers1992Three-horizon KST maturity alignment1994Constructing a relative-strength MACD crossover spreadsheet