Organizing MQL5 REST API Functions into Classes

Summary

This installment in a series on building an MQL5 reinforcement-learning agent reorganizes HTTP request functions into classes. The starting code uses separate procedures for GET and POST requests and a dispatcher that selects a request method. The article describes grouping this functionality into class methods, with the goal of encapsulating related operations and making the project easier to maintain, extend, and reuse.

Its examples focus on request handling: sending parameters or payloads, receiving responses, decoding returned text, and reporting errors. The discussion presents object-oriented organization as a software design approach for MQL5 projects, including trading automation, rather than as a new learning algorithm. The material is primarily a code-structure tutorial; it does not provide performance measurements or evidence that the refactoring improves execution speed. Its relevance to reinforcement learning comes from the surrounding API integration project, including earlier parts that connect MQL5 to a tic-tac-toe service.

Key ideas

• The existing design uses separate functions for GET and POST requests and a dispatcher for choosing the method.
• Grouping related HTTP operations into MQL5 classes can make responsibilities easier to locate and maintain.
• The request examples cover payloads, headers, response decoding, and error handling.
• The article teaches software organization for an API integration project rather than a reinforcement-learning method or trading strategy.

Tags

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