MQL4 and MQL5 Programming Articles
Study the MQL5 language for programming trading strategies in numerous published articles mostly written by you - the community members. The articles are grouped into categories to help you quicker find answers to any questions related to programming: Integration, Tester, Trading Strategies, etc.
Follow our new publications and discuss them on the Forum!
Add a new article
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Defining your Edge (Part 5): Using GARCH Variance and Volatility-Scaled LSTM in an Expert Advisor
We merge GARCH(1,1) variance projections with ATR plus Bollinger-Bands patterns to form an algorithm that could optionally be used with volatility-scaled LSTM within LSTM Wizard-ready signal class. We cover feature scaling, mode scoring, thresholds, and safety checks. Readers can replicate backtest/forward test results to verify if the recurrent layer gives incremental discrimination over our deterministic baseline.
Automating Classic Market Methods in MQL5 (Part 7): The Nicolas Darvas Box System
This article implements the Darvas Box method as a complete MQL5 Expert Advisor. We code box detection with a three-session hold, volume contraction during consolidation, and volume-confirmed breakouts, plus a staircase pyramid with a shared, rolling stop at the latest box floor. The EA uses a state machine to run box scanning and trade management in parallel, providing a ready-to-compile system with configurable inputs and clear on-chart diagnostics.
Native Isolation Forest for Execution-Quality Anomaly Detection in MQL5
A step-by-step guide to a native Isolation Forest in MQL5 focused on execution metrics rather than price. It details five features, tree construction and path‑length scoring, rolling‑window training, CSV logging, and FILE_COMMON persistence, all integrated into OnTradeTransaction(). The resulting circuit breaker flags unusual fills in real time and applies controlled responses to stabilize live trading under changing execution conditions.
From Basic to Intermediate: Queues, Lists, and Trees (VII)
In this article, we will clearly and simply demonstrate and explain how to remove a node from a tree. This process usually confuses beginners rather than helping them understand how it's done and why it needs to be done that way.
Automating Classic Market Methods in MQL5 (Part 7): The Nicolas Darvas Box System
This article implements the Darvas Box method as a complete MQL5 Expert Advisor. We code box detection with a three-session hold, volume contraction during consolidation, and volume-confirmed breakouts, plus a staircase pyramid with a shared, rolling stop at the latest box floor. The EA uses a state machine to run box scanning and trade management in parallel, providing a ready-to-compile system with configurable inputs and clear on-chart diagnostics.
Machine Learning in Pure MQL5 (Part 1): Logistic Regression from Scratch with SGD
The series develops machine learning in 100% native MQL5 with no external dependencies. Part 1 delivers logistic regression from first principles: a CLogReg class with standardization, a stable sigmoid, SGD training, and model persistence, plus a script that builds ATR-normalized features, labels the next bar, and tests out-of-sample against a baseline. Readers get a compact include file and a clear template for leakage-free evaluation.
Beetle Swarm Optimization (BSO)
We consider a BAS+PSO (BSO) hybrid, where BAS provides a local direction signal and PSO facilitates the exchange of best solutions within the swarm. The article presents a mathematical model, pseudocode, an implementation of the class in MQL5, and test results from a standard test bench. This material allows reproducing the algorithm, configuring its parameters, and understanding how three objective-function evaluations per iteration affect efficiency.
Neural Networks in Trading: Heterogeneity-Informed Meta-Parameter Learning (HimNet)
We invite you to explore the HimNet framework, which combines the flexibility of spatio-temporal adaptation with high computational efficiency, enabling accurate and stable forecasts for financial time series. The article explains in detail how its key components interact with one another, transforming complex algorithms into a manageable architecture.