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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Hierarchical Risk Parity: A Robust Portfolio Allocator and Expert Advisor
We implement a Hierarchical Risk Parity allocator in MQL5 as a single class, validate each stage against an independent Python reference, and package it in a rebalancing Expert Advisor. The pipeline covers returns, covariance/correlation, clustering, quasi-diagonalization, and recursive bisection, and contrasts HRP with Markowitz on stressed data. You finish with a verified allocator and an EA ready for basket-level testing.
Neural Networks in Trading: A Cross-Domain Time Series Forecasting Framework (Conclusion)
The article focuses on the practical implementation of the TimeFound model for time series forecasting. The key stages of implementing the framework's main approaches using MQL5 are examined.
Path Signatures for Lead-Lag Detection
Build a level-2 path-signature engine in pure MQL5 to read the lead-lag ordering between two data streams without choosing a lag and without a linear model. The article delivers a reusable library, an indicator that plots the Levy‑area oscillator, and a simple rule‑based Expert Advisor. Code is cross‑checked against closed‑form cases, and the components are ready to plug into your projects.
Neural Networks in Trading: An Intelligent Forecast Pipeline (Time-MoE)
We invite you to explore the modern Time-MoE framework, which has been adapted for time series forecasting tasks. In this article, we will implement the key components of the architecture step by step, providing explanations and practical examples along the way. This approach will allow you not only to understand how the model works, but also to apply those principles to real-world trading scenarios.
Automating Trading Strategies in MQL5 (Part 52): The tCISD Model with SSMT and Quarterly Theory
We build a tCISD program in MQL5 that pairs Quarterly Theory cycles anchored to New York time with a correlated-symbol SSMT divergence to time reversals. The article shows how to map cycles and quarters, detect the cross-symbol sweep disagreement, and derive the tCISD level whose break confirms the change in delivery. You will get a working entry logic that arms on divergence and executes on a confirmation close or a retest.
From Novice to Expert: Systematic Profit Conservation Using Candle Range Theory
A hybrid exit engine for MQL5 replaces static TPs with CRT-derived structural levels. The CRT_ProfitConserve class secures a partial at the first level and then trails the remaining position by structural anchors rather than fixed pips. The article walks through the class API, essential methods, and example usage in EAs, providing a clear path to embed CRT-based exits into existing strategies.