Discussing the article: "MQL5 Wizard Techniques you should know (Part 30): Spotlight on Batch-Normalization in Machine Learning"

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Check out the new article: MQL5 Wizard Techniques you should know (Part 30): Spotlight on Batch-Normalization in Machine Learning.

Batch normalization is the pre-processing of data before it is fed into a machine learning algorithm, like a neural network. This is always done while being mindful of the type of Activation to be used by the algorithm. We therefore explore the different approaches that one can take in reaping the benefits of this, with the help of a wizard assembled Expert Advisor.

This article, like all in this series, highlights the use of wizard assembled Expert Advisors in testing out new ideas. Introductions on how this is done can be got from here and here for new readers, with those 2 articles providing some guidance on how to use the code attached at the end of this article. For this piece, we are using quite a few custom enumerations of data as optimizable inputs. The MQL5 inbuilt enumerations can be declared in the custom signal file’s header, and they will be automatically indicated as inputs and initialized as part of the signal filter. When the enumerations are custom though, placing them in the header will prevent the file from being visible (or recognizable) in the MQL5 wizard, meaning you cannot do the wizard assembly. The work around this we have, for now, is omitting them from the custom signal class header but having the parameters and their assignment functions declared within the signal class, as is always the case with any input parameter. Once the wizard assembly is completed, we then make manual changes to the roster of input parameters and also the initialization of the signal class to add this custom enumeration parameters.

Author: Stephen Njuki