Linear Regression Slope (LRS)

Linear Regression Slope (LRS) is a statistical engine that helps to represent the direction of a trend. LRS calculates the slope value of regression lines using the current bar and the previous n-1 bars where n=”regression periods”. The slope values are normalized by multiplying the raw slope values by 100 and then dividing by the price.

Fundamentally, LRS is based on the optimization of squared estimation error. That squaring process makes LRS vulnerable to bias by a single outlier. One bad value can skew the entire line for as long as that outlier is relevant. LRS has poor response to market gaps. The deficiencies of LRS should be recognized and weighed against its positive attributes. LRS changes faster than other indicators. With LRS you do not need to wait for divergence.

Example Chart:

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Tags: Divergence, Squared Estimation Error, Statistical Engine