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1996issue C031

Scoring equity path consistency with a k-ratio overlay

A diagnostic-overlay can run beside a separate trade-generating system, write that system’s k-ratio to a log, and leave every buy or sell decision to the live engine.

  • The evaluation procedure issues no buy or sell signals. It is meant to run at the same time as a separate trade-generating system and write that system’s k-ratio to a log.
  • Each equity-observation stores wealth as closed net profit plus open-position profit.
  • The full k-ratio is computed only on the last calculation date of the sample, after equity is regressed on observation index with ordinary least squares.
  • The reported k-ratio is the regression slope divided by the product of the slope-standard-error and the square root of the observation count.
Entries in this reading1 entry

A diagnostic-overlay beside the trade engine

The archive describes a diagnostic-overlay: a companion procedure that records another system’s equity path and reports a score without emitting trade instructions.

It is meant to run at the same time as a separate trade-generating system and write that system’s k-ratio to a log. The evaluation procedure issues no buy or sell signals.

What each equity-observation stores

Each observation stores equity as closed net profit plus open-position profit. In the fixed wording used here, an equity-observation is a dated wealth reading defined that way.

The full ratio is computed only on the last calculation date of the sample.

How the k-ratio is obtained

Equity is regressed on observation index with ordinary least squares to obtain an intercept and a slope.

Residual scatter around the fitted line is converted into a slope-standard-error, using two fewer degrees of freedom than the observation count. That slope-standard-error is the residual scale of the equity-versus-index regression divided by the root of the centered sum of squares of the observation index.

The reported k-ratio is the regression slope divided by the product of the slope-standard-error and the square root of the observation count. A k-ratio is a unitless evaluation score equal to the linear-regression slope of sequential equity divided by that slope’s standard error scaled by the square root of the observation count.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
24 of 43 in the Linear regression track
19961-12 pp.Next on Linear regressionEvaluating month-end yield gaps for equity regimesLock the yield-gap at month-end closes against a six-month-arithmetic-average so the intermarket-regime-filter uses one signed difference for both direction and strength.
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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