Regression models describe the relationship between variables by fitting a line to the observed data. Linear regression models use a straight line, while logistic and nonlinear regression models use a curved line. Regression allows you to estimate how a dependent variable changes as the independent variable(s) change.
Simple linear regression is used to estimate the relationship between two quantitative variables.
This indicator calculates the difference of two different regression calculations and the moving average of this difference over the selected period.
Thus, it tries to determine the future estimation of the independent variable, the price.
In my experiments, I saw that different timeframes work more successfully and different inputs can be used for different instruments.
You can take both the intersection of the two lines and the lines intersecting the zero point as a signal.