MQL5 Matrix Utilities for CSV Data and Machine Learning Preparation

Summary

This article presents reusable MQL5 utilities that extend matrix and vector operations, with an emphasis on preparing data for trading systems and machine-learning workflows. The functions described include reading numeric or categorical CSV data, writing matrices, converting between arrays, vectors, and matrices, removing rows or columns, splitting data into training and test sets, separating features from targets, and building design matrices. It also covers one-hot or label encoding, extracting classes, generating random vectors, appending vectors, and copying selected ranges.

Examples show CSV headers stored separately from numeric matrix values and categorical columns encoded as integer labels before being placed in a matrix. The article illustrates operations with sample outputs, but does not present a trading strategy, model evaluation, or evidence that the utilities improve predictive performance. The code is a collection of data-handling helpers; users still need to check assumptions such as consistent CSV row widths, correct label interpretation, and suitable train/test separation for their application. Its value is primarily practical infrastructure for organizing inputs to quantitative analysis.

Key ideas

• CSV readers can load numeric data into matrices while retaining column headers separately.
• Categorical CSV columns can be converted into numeric class labels for matrix use.
• The utility collection supports matrix reshaping, row or column removal, and vector conversions.
• Train/test and feature/target split helpers support preparation of modeling datasets.
• The examples demonstrate data manipulation but do not evaluate a trading model or strategy.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.