MQL5 Matrix and Vector Tools for Quantitative Analysis
Summary
This reference article surveys MQL5's matrix and vector types and their built-in methods for mathematical and data-processing work. It covers object creation and initialization, copying arrays and timeseries, matrix and vector arithmetic, transformations, statistics, equation solving, and machine-learning methods. A practical example shows how historical close prices for several currency pairs can be copied directly into a matrix for correlation analysis, avoiding intermediate array transfers.
The article is primarily a language and API guide, not a trading strategy or empirical study. Its value for quantitative work is in showing how built-in linear algebra operations can make data preparation and numerical workflows more concise, including integration with OpenCL. It notes that support differs among data types and that some complex-valued types were unfinished at the time of writing. Results depend on the user's implementation and data; the examples illustrate functionality rather than establish an investment edge.
Key ideas
- MQL5 matrix and vector types provide built-in structures for common linear algebra operations.
- Initialization and assignment methods support moving between arrays and matrix or vector objects.
- CopyRates can load historical price fields directly into a matrix or vector for analysis such as correlation calculations.
- The article is an API overview, and type support varies, particularly for complex-valued objects.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.