Modular MQL5 Trading Assistant with AI Signal Execution

Summary

This article describes updates to an MQL5 trading assistant that separates user interface components into a reusable include module and adds automated signal handling. The proposed flow checks for signals on new bars or by user request, parses AI responses formatted as JSON for BUY, SELL, or NONE decisions and entry, stop-loss, and take-profit levels, and can submit trades with configurable lot size and magic number. It also describes chart annotations for patterns such as engulfing formations and divergences, chat and search interface changes, and retained conversation history.

The article discusses using lower AI response temperature for more constrained, structured outputs and higher settings for less predictable general responses. Its backtesting section merely refers to a compiled visual demonstration; the supplied text gives no performance metrics, validation procedure, or evidence that generated signals are reliable. The material is primarily an implementation walkthrough, and automated execution depends on correctly parsing and checking model output as well as separate testing of the trading logic and risk controls.

Key ideas

• The design moves user interface components into a separate module to make the MQL5 system easier to organize.
• AI responses are parsed into trade direction and entry, stop-loss, and take-profit fields.
• Signal checks can run on new bars or be requested manually, with optional automated order submission.
• Lower response temperature is presented as a way to encourage structured output, while higher settings allow more variation.
• The described visual backtest does not provide quantitative evidence of trading performance.

Tags

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