Building a Local Voice Interface for Trading Commands in MetaTrader

Summary

This article presents a local voice interface for a MetaTrader Expert Advisor. A Python listener uses offline speech recognition to detect a wake phrase, recognize a limited set of trading commands, and expose the latest parsed command through a local HTTP endpoint. The EA checks that endpoint periodically, executes supported actions such as buying, selling, or closing trades, then sends a message to a second local service for spoken feedback.

The design separates speech recognition, command delivery, trade execution, and text-to-speech into independent processes. A constrained recognition grammar is intended to reduce false matches, while HTTP replaces file polling and its encoding and locking problems. The article walks through setup and gives a test example, but it does not provide systematic accuracy, reliability, or trading-performance results. The command set and symbol aliases are limited, and the EA’s periodic polling and automated order execution still require careful configuration and monitoring.

Key ideas

• Offline speech recognition can turn a wake phrase and spoken instruction into a standardized trading command.
• A local HTTP endpoint lets the EA retrieve commands without polling a shared text file.
• Separating recognition, execution, and speech feedback makes the components independently replaceable.
• A restricted vocabulary can limit recognition to known actions and symbol aliases.
• The described example does not establish recognition reliability or trading performance across conditions.

Tags

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