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1990issue C021-17

Bezier-curve price trend filter

A Bezier path is built from a few control vertices of sampling distance and price, with the newest observation at the far end of a fixed lookback. The finished curve is a weighted blend of those vertices, and a rudimentary trend-filter compares the current close with one chosen segment-value.

  • Control vertices are ordered pairs of sampling distance and price, so the newest observation sits at the far end of a fixed lookback.
  • Each point on the finished curve is a weighted blend of all control vertices, and the path begins bending toward later vertices immediately.
  • Using every price as a control vertex collapses the spline into a connect-the-dots plot and fails to filter noise.
  • The chosen segment index sets responsiveness: early segments lag recent prices and late segments chase them.
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Control vertices along a fixed lookback

A bezier-curve is a cubic spline whose path is determined by a small set of control vertices and cannot cross itself. Each control-vertex is an ordered pair of sampling distance and price that pins the spline. The newest observation sits at the far end of a fixed lookback, and earlier prices sit at earlier distances.

The demonstration construction used seven vertices sampled at 28, 23, 18, 13, 8, 5 and 0 periods back, with the newest price as the last control point.

Weighted blending and output resolution

Each point on the finished curve is a weighted blend of all control vertices. The path begins bending toward later vertices immediately and is pulled off a straight line between the first two points.

Output resolution is a separate parameter. The demonstration spline was assembled from 30 line segments. Finer segmentation increases computation without changing the vertex set.

Using every price as a control vertex collapses the spline into a connect-the-dots plot. Too many vertices therefore fail to filter noise, because more vertices make the curve hug raw prices more closely.

Comparing the close with one segment-value

A trend-filter damps noisy price movement and compares the latest close with a smoothed baseline at a defined lookback. The archive workflow uses a rudimentary trading filter that compares the current close with one chosen segment. That segment-value is the comparison level for a long or short bias. A close above the segment is treated as an upside breakout-bias, and a close below it as a downside breakout-bias.

Early segments lag recent prices and late segments chase them, so the chosen segment index sets responsiveness in the same way a moving-average length does.

The same spline construction was applied to daily series and to half-hour series, with the close compared against segment indices 2, 7, 12, 17 and 22.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
2 of 33 in the Trend filter track
19921-8 pp.Next on Trend filterConstructing a damping-index trend filterThe damping-index compares the recent five-bar average high-minus-low span with the preceding five-bar span and treats any reading below 1 as strong damping.
All readings on this track · 33 readings
  1. 1988Opening-range brackets, a two-bar trend filter, and bounded stops
  2. 1990Bezier-curve price trend filter
  3. 1992Constructing a damping-index trend filter
  4. 1992Building a random walk index trend filter
  5. 1992Phase diagrams for moving-average trend filters
  6. 1993Volume-weighted change smoothing and trend ranking
  7. 1993Concurrent highest-low filter with a largest-low-fall trigger
  8. 1994Unit-invariant trend filters and the c-test
  9. 1995Constructing cup and cap entries with a three-bar net line
  10. 1997Why a daily timing evaluation depends on interval, lookbacks, and the fitting objective
  11. 2001A volume budget clock for trend-segment construction
  12. 2001Keep three jobs separate when you test a composite score
  13. 2002Evaluating the weekly four-percent close filter as a market-state procedure
  14. 2003Constructing a confirmed zigzag trend filter
  15. 2004Decompose high, low, and close into separate forecast streams
  16. 2005Three-state moving-average breakout bar coloring
  17. 2005Constructing a volume and move-adjusted trend filter
  18. 2005A fifty-day average breakout as a trend permission filter
  19. 2005Current-bar inclusion can mute a stochastic channel break
  20. 2006A stochastic oscillator gated by a long-term exponential average
  21. 2010A construction test for a modified volume-price trend filter
  22. 2011Constructing a Spearman rank trend filter
  23. 2013Constructing a repeated-median slope as a resistant trend filter
  24. 2014Combining a relative-strength index and trend filters for oversold setups
  25. 2014Price-rooted lookbacks for a relative strength index, a moving average, and a trend filter
  26. 2015Evaluating next-session intermarket range forecasts
  27. 2018Read the intermarket weight matrix first, then the predicted moving-average filter
  28. 2018Constructing the stiffness trend filter from moving-average holds
  29. 2018The averaging kernel and the lagged trend gate are separate specifications
  30. 2019A trend filter is not ready to compare until portfolio constraints are written down
  31. 2019Lookback, threshold, and position-capacity for a stiffness trend-filter
  32. 2020Combining a trend filter with a moving average and a stochastic oscillator
  33. 2020Constructing a relative-strength oscillator with a rank-agreement trend filter
All 137 readings tagged Trend filter
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