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1992issue C091-8

Variable lookback and average true range as a trend-filter construction

Oscillators that lock one lookback treat a persistent cycle as given. First-difference autocorrelations did not support that cycle, so the trend filter was rebuilt to scale high and low excursions by average true range times the square root of n and to take today's lookback from the largest current index.

  • Many smoothing tools and 0-to-100 oscillators lock one lookback for every market on the assumption that a persistent cycle is always present.
  • First-difference autocorrelations for eight futures and two equities stayed near zero at lags from one through 40, so a shared short-term cycle was not a reason to keep one lookback.
  • The rebuilt trend filter divides the current extreme versus the opposite extreme n days ago by average true range times the square root of n, and keeps the largest index across many lookbacks.
  • The histogram of maximizing lookbacks bends between seven and eight days, which the construction uses to split short-horizon and longer-horizon trend readings.
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A single lookback as a standing assumption

Many smoothing tools and 0-to-100 oscillators lock one lookback for every market on the assumption that a persistent cycle, such as a half-cycle of a 28-day rhythm, is always present.

First-difference autocorrelations did not supply a cycle

Daily first-difference autocorrelations were computed for eight futures and two equities, at lags from one through 40, on samples of about one thousand closing days. First-difference autocorrelation is the correlation of one-day price changes with the same series lagged by one through many sessions.

With a standard error near 0.03, only isolated lags exceeded a three-error significance cutoff, and those readings remained too small to treat as a usable predictive link. The long-sample autocorrelation maps stayed near zero, so a persistent short-term cycle was not available as a reason to keep one lookback across markets and eras. A twelve-month lag was noted as a separate seasonal case for some commodities.

Channel height against a random-walk yardstick

Average n-day channel height divided by one-day range, measured over 1,121 days, ran above the square root of n. The construction treats that extra displacement as movement beyond a pure random walk, and therefore as a trend yardstick. The channel-height ratio is that average n-day high-low span divided by the one-day average span, compared with the square root of n.

A random-walk index scaled by average true range

The rebuilt trend filter is a constructed reading that asks whether the latest high or low excursion is larger than a random-walk benchmark over a chosen lookback. The high-side and low-side indexes divide the current extreme versus the opposite extreme n days ago by average true range times the square root of n. That ratio is the random-walk index. A reading above 1 is classified as larger than the random-walk expectation.

Average true range is the volatility scale in the denominator, so a move must clear a range-based bound before it is treated as non-random.

Lookback taken from the current swing

Instead of locking one interval in advance, the construction evaluates many lookback lengths and keeps the largest index. Each session's working reading is the largest index across that set, so the sampling interval is taken from the most significant current swing rather than from a preset cycle length.

The histogram of lookbacks that produce that maximum bends between seven and eight days, which the construction uses as a split between short-horizon and longer-horizon trend readings.

True range keeps a gapped bar from shrinking the scale

True range is the greater of the bar's high-low span and the gap from the prior close to today's extreme. Average true range, built from that true-range input, is the volatility figure that keeps a gapped, narrow bar from shrinking the denominator used to scale the filter.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
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19931-2 pp.Next on ATR position sizingA random-walk index that uses true range as its scaleOn the low side, the random-walk index tests whether the current low is occurring in a trend or in a random trading range.
All readings on this track · 36 readings
  1. 1988Constructing unsigned true range for directional models
  2. 1989Evaluate an always-in ATR breakout as one procedure
  3. 1992Variable lookback and average true range as a trend-filter construction
  4. 1993A random-walk index that uses true range as its scale
  5. 1993A shared harness for trend-filter construction
  6. 1998Finish a trend with a volatility trail, wave permission, and a slower-frame veto
  7. 1999A trend filter that switches tactics and scales ATR targets
  8. 2001Filter higher lows with linear regression, then judge the exit
  9. 2003A Mechanical trading system is a maintained procedure, not only an entry trigger
  10. 2005Construction of a volatility-bounded long entry
  11. 2005Six-zone encoding of open, high, low, and close
  12. 2006Normalized average true range as a pre-entry volatility bound
  13. 2006Chandelier exits, ATR position sizing, and trailing stops
  14. 2007Constructing a rule-based entry with Relative Strength Index and ATR position sizing
  15. 2008Constructing a zero-lag TMA and heikin-ashi crossover as a complete rule set
  16. 2010Use the session-range percent stop as a pre-trade filter
  17. 2011OCA exit groups, trailing limits, and ATR stops
  18. 2011ATR bands around support and resistance for stops and targets
  19. 2013Algorithmic head-and-shoulders construction with bounded exits
  20. 2013Constructing ATR-scaled swing pivots and linear-regression divergence
  21. 2013Constructing volatility bands from typical price
  22. 2014Constructing true-range contraction filters before expansion
  23. 2015Constructing touch plans from modified true range
  24. 2015One checklist for breakout entry and ATR risk
  25. 2015Percentage true-range construction for cross-market volatility filters
  26. 2015Construct a percentage true range for cross-market volatility
  27. 2015Percentage true range as a pre-entry exposure filter
  28. 2016Constructing ATR-filtered breakout entries
  29. 2017A dividend date as a pairs-trading classroom
  30. 2018Range-based volatility as a true-range construction
  31. 2018Moving average support and volatility-band construction
  32. 2018Construct a lifecycle breakout from compression
  33. 2018Pair the book first and let volatility or range set the size
  34. 2019Trend systems need a no-trade rule
  35. 2020Average true range as a shared unit for size, pairs, and stops
  36. 2020Volatility sizing and target-risk leverage as a pre-trade gate
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