Connecting MetaTrader Signals to a Human-Reviewed Social Decision System
Summary
This article outlines a social decision support system in which a MetaTrader 5 Expert Advisor sends trading signals to a web application, which stores them and publishes them on Twitter. The proposed workflow leaves a person to review algorithmic signals before acting, positioning the software as a decision aid rather than an autonomous execution system. The author also suggests that audience responses could later help assess perceived signal reliability, though this idea is not developed or evaluated.
The technical design uses a REST endpoint with JSON data, a PHP application built with Slim, a MySQL database for EA and signal records, and OAuth for Twitter access. The example defines a signal payload with an EA identifier, symbol, operation, and price value, while noting that terminal identification would matter if multiple terminals were connected. Authentication between the terminal and service is essential but skipped in the implementation discussion. This first part focuses on service architecture and the PHP side; the MQL5 client is deferred, and no trading results or signal-quality evidence are presented.
Key ideas
- The system routes EA signals through a REST service before publishing them on a social platform.
- Human review is proposed as a filter before algorithmic signals are acted on.
- JSON carries signal details, while a PHP application stores them in a relational database.
- OAuth connects the web application to the social platform, but terminal-to-service authentication is left unspecified in the example.
- Social responses are suggested as possible indicators of perceived signal reliability, without validation.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.