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.
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.
All readings on this track · 43 readings
- 1990Constructing dollar baselines from rates, inflation, and residuals
- 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