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2007issue C061-7

Rectangle breakout targets beyond height

A rectangle is sideways price action between two horizontal or nearly horizontal parallel lines. In a 100-formation 2004-05 sample, breakout size tracked rectangle height more closely than the classical one-height objective, and a through-origin linear regression set breakout equal to 1.75 times height.

  • A rectangle is sideways price action bounded by two horizontal or nearly horizontal parallel lines, typically defined by at least two points on each line and more often by three.
  • In a 100-formation 2004-05 sample, rectangles were more often continuations than reversals, about 61% to 39%, or roughly 1.5 to 1.
  • Breakout size correlated strongly with rectangle height and historical volatility, but only weakly with formation duration.
  • A points-based linear regression of breakout on height produced r-squared 0.71; forcing the line through the origin gave breakout equal to 1.75 times height with r-squared 0.60, versus the classical one-height objective.
Entries in this reading3 entries
Rectangle pattern3 readingsCurrent trackLinear regression112 readingsBreakout confirmation172 readings

Confirm the sideways structure

A rectangle is sideways price action bounded by two horizontal or nearly horizontal parallel lines, typically defined by at least two points on each line and more often by three.

Continuation, reversal, and time to complete

In a 100-formation 2004-05 sample, rectangles were more often continuations than reversals, about 61% to 39%, or roughly 1.5 to 1.

Reversal rectangles completed faster on average than continuation rectangles, 152 days versus 218 days.

Volume inside the rectangle and at the breakout

Volume during the formation averaged 90% of usual activity, while upside-breakout volume commonly rose to about two to three times rectangle-period volume.

Height-based forecasts after breakout confirmation

Breakout size correlated strongly with rectangle height (Spearman rho 0.66) and with historical volatility (0.62), but only weakly with formation duration (0.09).

A points-based linear regression of breakout on height produced r-squared 0.71; forcing the line through the origin gave Breakout = 1.75 times height with r-squared 0.60, versus the classical one-height objective.

Height-based forecasts versus actual rectangle breakout highs

Traders using the classical add-the-height objective would have been taken out well before these seven breakouts finished. The article's power-law forecast sits within about a percent of the actual high in each row. The figures are the exact cells from the source price-target spreadsheet, not a tracing of the candlestick images.
Traders using the classical add-the-height objective would have been taken out well before these seven breakouts finished. The article's power-law forecast sits within about a percent of the actual high in each row. The figures are the exact cells from the source price-target spreadsheet, not a tracing of the candlestick images.Daily · 2003-12-08T00:00:00.000Z to 2005-05-23T00:00:00.000Z

Predicted price uses formula (2), breakout = 2.3 times height to the 0.8, added to the upper boundary. The traditional column adds one rectangle height. The source notes that FUL, CW, and HP later split 2-for-1 and these unadjusted rows were left as published.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
35 of 43 in the Linear regression track
20071-9 pp.Next on Linear regressionConfirming a price trend with regression slope and r-squaredA least-squares line on a fixed lookback of closes reduces bar-to-bar noise and serves as a price-trend baseline.
All readings on this track · 43 readings
  1. 1990Constructing dollar baselines from rates, inflation, and residuals
  2. 1990Constructing a nominal index value from forward earnings and fitted yield
  3. 1990Constructing a nominal index price from earnings and a fitted yield
  4. 1990Endpoint-pinned price paths are not forecasts
  5. 1990Constructing least-squares polynomial smoothers
  6. 1991Endpoint growth rates versus linear-regression consistency
  7. 1991Out-of-sample checks for linear growth fits
  8. 1991Trend as persistence, not a straight line
  9. 1991Quadratic trend, residual oscillator, and a secondary cycle calendar
  10. 1991Time-origin offset and residual-price divergence on a quadratic least-squares fit
  11. 1991A least-squares trendline from ordered prices
  12. 1992Constructing log-linear growth and reliability screens
  13. 1992Constructing log-linear growth-rate baselines
  14. 1992Next-session high, low, and close from rolling linear regression
  15. 1993Auditing an index price-earnings multiple with short-rate regression
  16. 1994Regression-seeded nested exponential price filter
  17. 1994Constructing the double exponential average from lag cancellation
  18. 1994Evaluating money supply as a linear leading-index baseline
  19. 1995Constructing least-squares trend channels
  20. 1995Linear baseline holdout checks for annual bill-rate forecasts
  21. 1995Projection bands from high and low regression slopes
  22. 1995Evaluating a least-squares end-point moving average on a known test series
  23. 1996Constructing an endpoint moving average from a least-squares line
  24. 1996Scoring equity path consistency with a k-ratio overlay
  25. 1996Evaluating month-end yield gaps for equity regimes
  26. 1996Constructing session-indexed standard error bands
  27. 1998Evaluating linear regression baselines for index valuation
  28. 1998R-squared as a two-state trend filter from a price-time fit
  29. 2000Second-order moving-average lag correction
  30. 2002Price regression line versus beta for index tracking
  31. 2003Regression slope with an r-squared trend confidence gate
  32. 2003Constructing finite-volume-element divergence with slope comparison
  33. 2004Building a daily score from regression, retracement, and volume
  34. 2004Constructing least-squares trendlines from ordered prices
  35. 2007Rectangle breakout targets beyond height
  36. 2007Confirming a price trend with regression slope and r-squared
  37. 2008A linear-regression angle assembled as one trend filter
  38. 2010A two-state swing machine from four running extremes
  39. 2016Score oil-complex tightness before divergence or regression
  40. 2017Nikkei-yen intermarket divergence as a regime case study
  41. 2017Constructing Calmar ratio and linear regression baselines
  42. 2019Pair-trade layer construction versus average-spread management
  43. 2020A convolution slope built from nested linear regression
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