Discussing the article: "Building AI-Powered Trading Systems in MQL5 (Part 1): Implementing JSON Handling for AI APIs"
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Check out the new article: Building AI-Powered Trading Systems in MQL5 (Part 1): Implementing JSON Handling for AI APIs.
In this article, we develop a JSON parsing framework in MQL5 to handle data exchange for AI API integration, focusing on a JSON class for processing JSON structures. We implement methods to serialize and deserialize JSON data, supporting various data types like strings, numbers, and objects, essential for communicating with AI services like ChatGPT, enabling future AI-driven trading systems by ensuring accurate data handling and manipulation.
JSON (JavaScript Object Notation) is a lightweight, text-based data interchange format widely used for structuring and transmitting data between systems, particularly in web-based APIs, due to its simplicity, readability, and compatibility across programming languages. In the context of AI-powered trading systems, like we want to build, serves as the standard format for exchanging data with AI APIs, such as OpenAI’s ChatGPT, enabling MQL5 applications to send trading-related prompts and receive structured responses for decision-making. Our approach in this article focuses on building a JSON parsing framework to handle these API interactions, laying the groundwork for integrating AI-driven insights into automated trading strategies.
JSON represents data as key-value pairs, arrays, and nested objects, making it ideal for encoding complex information like market data, trading signals, or AI responses in a format that is both human-readable and machine-parsable. For example, a JSON object might look like this:
{ "model": "gpt-3.5-turbo", "messages": [ {"role": "user", "content": "Analyze EURUSD trend"}, {"role": "assistant", "content": "EURUSD shows a bullish trend"} ], "max_tokens": 500 }This structure includes strings, numbers, arrays, and nested objects, which an MQL5 Expert Advisor (EA) must parse to extract relevant information, such as the AI’s response to a trading query. JSON’s role in AI integration is critical here because APIs return responses in JSON format, requiring the program to serialize inputs (convert data to JSON) and deserialize outputs (parse JSON into usable data) to enable dynamic trading decisions. If this sounds like a jargon to you, here is a quick visualization of what data serialization and deserialization means.
Author: Allan Munene Mutiiria