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1991issue C071-5

Trend as persistence, not a straight line

Common chart practice draws trend as an upward or downward line and skips the flat hold. This archive article treats trend as persistence, lets a trendline stay horizontal, pressure-tests that line with a walk-back linear-regression fit, and compares the result with a moving average that follows price without redrawing.

  • Trend is persistence of price over time, including a stable horizontal level, not only an upward or downward slope.
  • A trendline states direction or a sideways hold as a visual hypothesis; local dependence does not require that persistence be a straight line.
  • Walking a linear-regression fit backward from the most recent 20 days treats a decline in the coefficient of determination as a sign that the added price is inconsistent with the current trend.
  • A percentage filter sets which swing sizes count as trends, and a moving average follows price without requiring the analyst to redraw the line.
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Persistence includes a flat hold

Common chart practice reduces trend to an upward or downward line and often omits a horizontal line as a description of persistence. Trend here means persistence of price over time, including a stable horizontal level, not only an upward or downward slope.

Day-to-day price continuity is presented as more typical of large or administered markets than of markets that regularly jump from one level to another. Local dependence, each print arriving near the last, does not by itself require that persistence be modeled as a straight line.

A trendline is a straight chart line used to state direction or a sideways hold. It is a visual hypothesis about path rather than a complete description of how prices actually travel.

A walk-back fit can fail

A linear-regression procedure can fit the most recent 20 days, then step one observation farther back at a time and treat a decline in the coefficient of determination as a sign that the added price is inconsistent with the current trend.

That regression view counts a persistent flat level as a trend just as it counts rising or falling prices. Movement can be defined as a departure from the fitted line or a sharp change in the line's slope or length.

Filter scale and a line that is not redrawn

A 6% percentage filter on the S&P 500 is used to erase smaller swings and thereby define only moves of that size or larger as the operative trends. The swing size a filter keeps also sets effective horizon: seeking a 6% wave implies ignoring 1% to 2% swings and working on a different scale from 10% to 25% swing definitions.

Moving averages are described as a non-discretionary extension of straight-line trend thinking because they follow price without requiring the analyst to redraw the line.

S&P 500 with a 6% swing filter

A 6% swing filter on the S&P 500 keeps only moves of that size or larger, so the lower panel reads as a few persistent legs instead of the daily noise above. Prices were read from the published two-panel figure, not from a table.
A 6% swing filter on the S&P 500 keeps only moves of that size or larger, so the lower panel reads as a few persistent legs instead of the daily noise above. Prices were read from the published two-panel figure, not from a table.S&P 500 · daily

Sweeney’s own S&P scale is a 6% filtered wave; smaller 1–2% swings are dropped. Digitised from the printed chart, so turning points are approximate.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
8 of 43 in the Linear regression track
19911-8 pp.Next on Linear regressionQuadratic trend, residual oscillator, and a secondary cycle calendarAfter the mid-January 1991 break, the working near-term range for June gold sat between long-term support near 355 and newly formed resistance at 376.
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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