Discussing the article: "Overcoming Accessibility Problems in MQL5 Trading Tools (Part IV): Remote voice trading"
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Check out the new article: Overcoming Accessibility Problems in MQL5 Trading Tools (Part IV): Remote voice trading.
Learn a practical way to execute MetaTrader 5 trades from Telegram voice notes using a Python middleware and an MQL5 EA acting as an HTTP client. The article covers architecture, WebRequest polling, in-memory queuing, JSON parsing with null-terminator stripping, and a constrained command grammar with a 0.001-lot default. You will configure the environment and validate round‑trip latency suitable for mobile data connections.
A voice‑controlled Expert Advisor that works perfectly at your desk (Part III, offline Vosk) loses all remote capability the moment you step away. Your mobile phone cannot execute MQL5 logic – the runtime is desktop‑only. Manual intervention in a high‑probability setup becomes impossible while commuting, cooking, or multitasking. This article builds a semi‑automated accessibility pipeline that lets you speak a trade command from anywhere and have your MetaTrader 5 terminal execute it over a low‑bandwidth pull‑based HTTP channel.
You already have an MetaTrader 5 terminal running on a Windows machine with Market Watch available, an Expert Advisor framework installed, and an offline voice command system (Vosk from Part III) that works locally. The terminal can send WebRequest (HTTP client) calls to a permitted URL. The trader is physically present at the desk and can speak a command that the EA executes instantly.
When you leave the desk, the terminal remains online but there is no way to feed it commands. Telegram voice messages are a natural remote input: you record a voice note and it is delivered to a bot, but nothing connects it to the MetaTrader 5 runtime. The goal is to complete the loop: voice note → transcription → command JSON → HTTP endpoint → EA polling → trade execution → result back to Telegram. All without touching the desktop.
We introduce a Python middleware that acts as a Telegram bot and a local HTTP server. The bot receives voice messages, converts them to text with Google STT, parses a simple natural‑language command, and enqueues it. A separate HTTP endpoint serves this command to the EA. The EA polls the endpoint every two seconds, executes the trade using CTrade, and POSTs the result back. The entire chain adds less than 3 seconds of latency and works over mobile data (< 5 KB per trade). The system is privacy‑preserving: it requires no cloud API keys beyond Telegram and no recurring fees. The speech‑to‑text engine is interchangeable, so you can later substitute an offline model for even greater independence.
Author: Clemence Benjamin