Building NumPy-Style Numerical Tools and ML Examples in MQL5

Summary

This article presents a NumPy-inspired utility class for MQL5, intended to make mathematical and data-processing code easier to translate from Python. It surveys vector and matrix constructors, arithmetic operations, mathematical and statistical functions, random sampling, Fourier transforms, linear algebra, polynomial tools, and commonly used array methods. The examples show how these utilities can support machine-learning implementations written directly in MQL5.

As a demonstration, the article builds linear regression from scratch and compares its output with a Python implementation. The displayed predictions and fit metrics are described as matching the Python results. This is a code-oriented library tutorial, not a trading strategy study: it provides no evidence that the example forecasts market prices profitably or that the implementation covers NumPy comprehensively. The author explicitly notes that many NumPy functions remain absent and invites readers to extend or adapt the available methods. Its value is mainly as a practical bridge for researchers prototyping numerical and ML workflows in MQL5.

Key ideas

• A NumPy-like interface can make vector and matrix calculations in MQL5 more familiar to Python users.
• The described utilities cover initialization, mathematics, statistics, random generation, transforms, and linear algebra.
• The article demonstrates the library by implementing linear regression from scratch.
• The example's reported predictions and fit measures agree with the Python version.
• The library is partial, and the demonstration does not show trading performance.

Tags

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