Discussion of article "MQL5 Wizard techniques you should know (Part 04): Linear Discriminant Analysis"

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New article MQL5 Wizard techniques you should know (Part 04): Linear Discriminant Analysis has been published:

Todays trader is a philomath who is almost always looking up new ideas, trying them out, choosing to modify them or discard them; an exploratory process that should cost a fair amount of diligence. These series of articles will proposition that the MQL5 wizard should be a mainstay for traders in this effort.

LDA is very similar to PCA: in fact, some have asked whether or not it would make sense to perform PCA followed by LDA regularisation ( to avoid curve fitting). That is a lengthy topic which perhaps should be an article for another day.

For this article though, crucial difference between the two dimensionality reduction methods is PCA tries to find the axes with maximum variance for the whole data set with the assumption being the more dispersed the data the more the separability, whereas LDA tries to find the axes that actually set the data apart based on  classification.

So from the illustration above, it’s not hard to see that PCA would give us LD2, whereas LDA would give us LD1. This makes the main difference (and therefore LDA preference) between PCA and LDA painfully obvious: just because a feature has a high variance (dispersion), doesn’t mean it will be useful in making predictions for the classes.

Author: Stephen Njuki

[CapeCoddah]

Hi Stephan,

Great article and great content!. I have enjoyed studying your previous articles as well.

I am currently converting my mq4 EA to mq5 and would like to include this content into the conversion to enhance the signals,stoploss and money management.  As you did not include an EA, i it possibleto post one that could be used as a learning example for studying the application of the DA techniques?

I am looking forward to your next articles.

Cheers, CapeCoddah