2004issue C031
Building a daily score from regression, retracement, and volume
A daily-chart construction fits a linear-regression midline over a zigzag-defined lookback, then adds Fibonacci retracement bands and a volume-slope check. Editorial: stack those terms so each method can be audited or removed without treating the whole setup as one visual pattern.
- The lookback window is one plus the bars since the latest 20 percent close-based zigzag pivot, and a score is issued only when that window is between 10 and 60 trading days.
- Linear regression fits a least-squares midline to closes; residual slopes of accumulated closes below and above that midline must point back toward the line.
- Fibonacci retracement is pullback depth as a percent of the prior swing range, gated at the 20, 38, and 50 percent bands used in this construction.
- When the gates pass, the composite formation score adds points for lookback length, a low coefficient of determination, shallow retracement, and a negative volume slope, and it is retained only when that integer is greater than zero.
A daily chart built from stacked terms
The construction is specified for daily charts. It keeps formations lasting at most 60 trading days with retracements of no more than 50 percent.
Editorial: read the setup as stacked, inspectable terms, a linear-regression midline first, then Fibonacci-style retracement bands and a volume-slope check, so each method can be audited or removed without treating the whole setup as one visual pattern.
Zigzag swings and the lookback window
Swing location uses a 20 percent close-based zigzag, and the construction is specified to need at least four such swings already present in the chart history.
Lookback length I is one plus the bars since the latest zigzag pivot. The lookback-window is that bar count from the latest zigzag pivot, and a score is issued only when the count falls between 10 and 60 trading days.
Linear regression as the first term
Linear regression is a least-squares midline, slope, and coefficient of determination fitted to ordered closes over a defined lookback, with the same slope form reused on residuals and on volume. A linear-regression slope of closes over I defines the local regression midline.
Residual slopes and a short-term swing count
Closes below and above that midline are accumulated only after the prior pivot date, and separate least-squares slopes are then taken of those down and up residual series. Residual-slopes are those separate least-squares slopes of accumulated closes below and above the regression midline, required to point back toward that line.
A short-term swing count tallies, over the open lookback, crossings between a 5-period simple moving average of the close and the regression midline.
Retracement bands and volume participation
Fibonacci retracement is pullback depth measured as a percent of the prior swing range and gated at the 20, 38, and 50 percent bands used in this construction. Maximum retracement is the greater of the current close's distance to the lookback high or lookback low, divided by the prior zigzag-leg price span and expressed as a percent.
Volume-price analysis is reading participation from the sign of volume's lookback slope together with how price residuals sit around the regression midline. Volume participation is measured as the least-squares slope of volume over the same lookback I.
Gates and the composite formation score
The composite score is evaluated only when I is between 10 and 60 inclusive, the down-residual slope is positive, the up-residual slope is negative, retracement is under 50, and the short-term swing count is at least 3.
When those gates pass, discrete points are added for lookback length (2 if I is at least 15 and under 30; 1 if I is 30 through 55), for a low coefficient of determination of closes (2 if under 0.2; 1 if at least 0.2 and under 0.5), for shallow retracement (4 if under 20; 3 if at least 20 and under 38), and for a negative volume slope (4 points). The composite-formation-score is an integer assembled from time, coefficient of determination, retracement depth, and volume-slope points and retained only when it is greater than zero.
All readings on this track · 43 readings
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- 1990Constructing a nominal index value from forward earnings and fitted yield
- 1990Constructing a nominal index price from earnings and a fitted yield
- 1990Endpoint-pinned price paths are not forecasts
- 1990Constructing least-squares polynomial smoothers
- 1991Endpoint growth rates versus linear-regression consistency
- 1991Out-of-sample checks for linear growth fits
- 1991Trend as persistence, not a straight line
- 1991Quadratic trend, residual oscillator, and a secondary cycle calendar
- 1991Time-origin offset and residual-price divergence on a quadratic least-squares fit
- 1991A least-squares trendline from ordered prices
- 1992Constructing log-linear growth and reliability screens
- 1992Constructing log-linear growth-rate baselines
- 1992Next-session high, low, and close from rolling linear regression
- 1993Auditing an index price-earnings multiple with short-rate regression
- 1994Regression-seeded nested exponential price filter
- 1994Constructing the double exponential average from lag cancellation
- 1994Evaluating money supply as a linear leading-index baseline
- 1995Constructing least-squares trend channels
- 1995Linear baseline holdout checks for annual bill-rate forecasts
- 1995Projection bands from high and low regression slopes
- 1995Evaluating a least-squares end-point moving average on a known test series
- 1996Constructing an endpoint moving average from a least-squares line
- 1996Scoring equity path consistency with a k-ratio overlay
- 1996Evaluating month-end yield gaps for equity regimes
- 1996Constructing session-indexed standard error bands
- 1998Evaluating linear regression baselines for index valuation
- 1998R-squared as a two-state trend filter from a price-time fit
- 2000Second-order moving-average lag correction
- 2002Price regression line versus beta for index tracking
- 2003Regression slope with an r-squared trend confidence gate
- 2003Constructing finite-volume-element divergence with slope comparison
- 2004Building a daily score from regression, retracement, and volume
- 2004Constructing least-squares trendlines from ordered prices
- 2007Rectangle breakout targets beyond height
- 2007Confirming a price trend with regression slope and r-squared
- 2008A linear-regression angle assembled as one trend filter
- 2010A two-state swing machine from four running extremes
- 2016Score oil-complex tightness before divergence or regression
- 2017Nikkei-yen intermarket divergence as a regime case study
- 2017Constructing Calmar ratio and linear regression baselines
- 2019Pair-trade layer construction versus average-spread management
- 2020A convolution slope built from nested linear regression