Metatrader 5 Overview

Principal Component Analysis (PCA) is the focusing on only the ‘principal components’ among the many dimensions of a data set, such that one is reducing the dimensions of that data set by ignoring the ‘non-principal’ parts.

PCA though, with eigen values & vectors, take on a slightly deeper approach. Typically, data sets that are handled under PCA are in a matrix format and the principal components, that are sought from a matrix would be a single vector column (or row) that is most significant among the other matrix vectors and would suffice as a representative of the entire matrix. As alluded in the intro above, this vector alone would hold the main components of the entire matrix, hence the name PCA. Identifying this vector though does not necessarily have to be done by eigen vectors & values, as Singular Value Decomposition (SVD) and the Power Iteration are other alternatives.

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This article continues the series on how the MQL5 wizard is ideal for rapid testing and prototyping ideas for traders. For a lot of people developing expert advisers and trade systems, the need to keep learning and be abreast with trends in not just machine learning but trade & risk management in general is important. We therefore consider within these series how the MQL5 IDE is useful in this regard by not only saving time but also minimizing coding errors.

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In this article series, we'll explore how MQL5 indicators are created, customized, and utilized to enhance trading strategies in MetaTrader 5. From basic indicator logic to advanced customization options, we'll cover the basics and gradually move deeper into the more advanced concepts of indicator development as we progress with the article series. The main aim of this article series is to empower you to create your own MQL5 custom indicators tailored to your trading preferences and goals.

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We continue the series on MQL5 wizard implementation by looking into Neural Architecture Search while specifically dwelling on the role Eigen Vectors can play in making this process, of expediting network training, more efficient. Neural networks are arguably the fitting of a curve to a set of data in that they help come up with a formulaic expression that, when applied to input data (x), provides a target value (y) just like a quadratic equation does with a curve. The x and y data points though can be, and in fact are often, multidimensional, which is why neural networks have gained a lot of popularity. Nonetheless, the principle of coming up with a formulaic expression does remain, which is why neural networks are simply a means of arriving at this but not the only way of doing so.

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