Articles with examples of trading robots developed in MQL5
An Expert Advisor is the 'pinnacle' of programming and the desired goal of every automated trading developer. Read the articles in this section to create your own trading robot. By following the described steps you will learn how to create, debug and test automated trading systems.
The articles not only teach MQL5 programming, but also show how to implement trading ideas and techniques. You will learn how to program a trailing stop, how to apply money management, how to get the indicator values, and much more.
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Interactive Supply and Demand Zone Manager in MQL5 (Part V): Persisting Zones Across Terminal and EA Restarts
We introduce a versioned persistence layer that saves and restores the zone engine's logical state across sessions. The snapshot preserves lifecycle status, interaction history, scores, blacklist prices, and the geometry and time anchors of user‑drawn rectangles, and it is reconstructed and reconciled with chart objects on startup. User zones enter with the minimum score so they integrate into the same lifecycle and trading pipeline.
How to Detect and Normalize Chart Objects in MQL5 (Part 6): Manipulating Chart Objects with MetaTrader 5 Agentic AI Assistant
This article demonstrates how natural-language prompts generate MQL5 producer programs that place native chart objects, and how a deterministic Expert Advisor (AIObjectTrader) reads them through a structured naming and geometry contract. We compare indicator and script routes, using three cases: horizontal resistance, a persistent Andrews' Pitchfork, and Fibonacci Time Zones. Readers gain a reproducible workflow to connect AI-built objects to TOUCH/BREAK/ZONE evaluation and optional execution.
Neural Networks in Trading: A Spiking-Semantic Approach to Spatiotemporal Identification (S3CE-Net)
We invite you to explore the S3CE-Net framework and its SSAM and STFS mechanisms, which accurately process spiking events while taking causality into account. The model is lightweight, parallel, and capable of identifying complex relationships across time and space.
Building Your Personal Expert Advisor (Part 7): Risk Management VI (Final)—Consolidating the Risk Framework
The article consolidates trade, basket, and account risk into a modular framework for MQL5 EAs. Risk logic moves into reusable classes and shared types, leaving the EA with its inputs, signal, configuration, and call sequence. The result is a shorter strategy file and a second EA that reuses the basket tier and the shared conversions.
Neural Networks in Trading: A Spiking-Semantic Approach to Spatiotemporal Identification (Key Components)
In this article, we took a detailed look at the integration of the SSAM module into the SEW-ResNeXt block, demonstrating how the S3CE-Net framework enables the effective combination of spiking attention with residual blocks. This architecture ensures accurate processing of temporal and spatial data streams and high training stability. The modularity and flexibility of the components make it easier to extend the model and reuse proven methods.
Building a Prop Firm Compliance Monitor in MQL5 (Part 2): Daily Sessions and Restart-Safe Starting Balances
Establishes persistent daily-session management for Prop Firm Guard in MetaTrader 5. The EA derives the session identity from the trading-server date, account login, and server, persists state in SQLite, and adds a confirmation step for the daily starting balance. A timer detects trading-day changes and restores the confirmed baseline after EA removal or terminal restarts, ensuring a stable reference for daily risk checks.
Building Your Personal Expert Advisor (Part 7): Risk Management VI (Final)—Consolidating the Risk Framework
The article consolidates trade, basket, and account risk into a modular framework for MQL5 EAs. Risk logic moves into reusable classes and shared types, leaving the EA with its inputs, signal, configuration, and call sequence. The result is a shorter strategy file and a second EA that reuses the basket tier and the shared conversions.
Try/Catch style in MQL5: Bringing Modern Error Management to Algorithmic Trading
This article presents an exception-like model for MQL5 that prevents VM faults through defensive guards and cooperative TRY/CATCH/FINALLY macros. It introduces structured error objects (CException), result propagation (CResult), manual stack tracing, and integrated logging for clear diagnostics. Readers learn how to validate risky operations, propagate failures predictably, and keep EAs stable when facing zero‑divide, null access, invalid handles, and array‑bounds conditions.