First, a few words about nonlinear regression:
Nonlinear regression is a form of regression analysis in which
data is fit to a model and then expressed as a mathematical function.
Simple linear regression relates two variables (X and Y) with a straight
line (y = mx + b), while nonlinear regression must generate a line
(typically a curve) as if every value of Y was a random variable. The
goal of the model is to make the sum of the squares as small as
The sum of squares is a measure that tracks how much observations
vary from the mean of the data set. It is computed by first finding the
difference between the mean and every point of data in the set. Then,
each of those differences is squared. Lastly, all of the squared figures
are added together. The smaller the sum of these squared figures, the
better the function fits the data points in the set. Nonlinear
regression uses logarithmic functions, trigonometric functions,
exponential functions, and other fitting methods.
This indicator is a MetaTrader 5 version of nonlinear regression.
Nonlinear regression is very "fast" when responding to sudden market
changes so the default calculation period is set to somewhat longer
period than it is usual for similar type indicator. Because of that some
experimenting with period is advised based on your trading strategy and
Author: Mladen Rakic