Building an MQL5 JSON Parser for AI API Integration

Summary

This article explains why an MQL5 trading application needs to serialize requests to an AI service and parse JSON responses. It outlines a parser design centered on a value class that represents primitive types, arrays, and nested objects, with methods for deserialization, serialization, string escaping, and error handling. The goal is to prepare infrastructure for later AI integrations rather than to present a trading strategy.

The article describes testing the framework with typical JSON structures and escape characters, and reports that those checks succeeded. That provides a basic indication that the parser handles the examples shown, but it does not establish reliability across all API responses or production conditions. The document is primarily a software implementation tutorial: it does not evaluate AI-generated trading decisions, provide historical market tests, or show that adding an AI API improves trading performance.

Key ideas

• JSON is the exchange format used to send structured requests to AI APIs and interpret their responses.
• A value class can represent JSON primitives as well as nested arrays and objects.
• Serialization, deserialization, escaping, and error checks form the core of the proposed parser.
• The article reports successful example tests for ordinary structures and escaped characters.
• Parser testing alone does not demonstrate that AI-driven trading decisions are accurate or profitable.

Tags

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