Using MQL5 Vector and Matrix Types for Linear Algebra
Summary
This technical guide introduces MQL5’s vector and matrix types as tools for representing ordered numerical data and expressing linear algebra operations. It explains how to initialize vectors with preset values or a filling function, resize and copy them, and perform scalar and element-wise arithmetic. Examples also show dot and matrix multiplication, Kronecker and outer products, and vector norms, including use of a norm to measure distance between vectors.
The matrix section extends the discussion to two-dimensional arrays, their dimensions, and compatible multiplication patterns involving matrices and horizontal or vertical vectors. Worked examples provide the resulting shapes and values, illustrating why orientation and dimension compatibility matter. The article also briefly demonstrates the built-in complex number representation. This is a programming and mathematics reference rather than a trading method: it gives no market data, strategy evaluation, or evidence that using these types improves trading results. Correct interpretation still depends on the programmer understanding the mathematical operation and input dimensions.
Key ideas
- MQL5 vectors store one-dimensional double-valued data and support common arithmetic operations.
- Vector initialization methods include zero-filled, one-filled, constant-filled, and function-generated values.
- Vector products include dot, matrix multiplication, Kronecker, and outer products.
- Norms can measure vector magnitude and the distance between two vectors.
- Matrix multiplication results depend on compatible dimensions and vector orientation.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.