Algorithmic Trading Articles — Guides & Examples

Risk Management (Part 5): Integrating the Risk Management System into an Expert Advisor

In this article, we will implement the risk management system developed in previous publications and add the Order Blocks indicator described in other articles. In addition, we will run a backtest so we can compare results with the risk management system enabled and evaluate the impact of dynamic risk.

In this article we are implementing ARIMA forecasting indicator in MQL5. It examines how the ARIMA model generates forecasts, its applicability to the Forex market and the stock market in general. It also explains what AR autoregression is, how autoregressive models are used for forecasting, and how the autoregression mechanism works.

Angular Analysis of Price Movements: A Hybrid Model for Predicting Financial Markets

What is angular analysis of financial markets? How to use price action angles and machine learning to make accurate forecasts with 67% accuracy? How to combine a regression and classification model with angular features and obtain a working algorithm? What does Gann have to do with it? Why are price movement angles a good indicator for machine learning?

Python-MetaTrader 5 Strategy Tester (Part 05): Multi-Symbols and Timeframes Strategy Tester

This article presents a MetaTrader 5–compatible backtesting workflow that scales across symbols and timeframes. We use HistoryManager to parallelize data collection, synchronize bars and ticks from all timeframes, and run symbol‑isolated OnTick handlers in threads. You will learn how modelling modes affect speed/accuracy, when to rely on terminal data, how to reduce I/O with event‑driven updates, and how to assemble a complete multicurrency trading robot.

Analyzing Overbought and Oversold Trends Via Chaos Theory Approaches

We determine the overbought and oversold condition of the market according to chaos theory: integrating the principles of chaos theory, fractal geometry and neural networks to forecast financial markets. The study demonstrates the use of the Lyapunov exponent as a measure of market randomness and the dynamic adaptation of trading signals. The methodology includes an algorithm for generating fractal noise, hyperbolic tangent activation, and moment optimization.

Integrating Computer Vision into Trading in MQL5 (Part 1): Creating Basic Functions

The EURUSD forecasting system with the use of computer vision and deep learning. Learn how convolutional neural networks can recognize complex price patterns in the foreign exchange market and predict exchange rate movements with up to 54% accuracy. The article shares the methodology for creating an algorithm that uses artificial intelligence technologies for visual analysis of charts instead of traditional technical indicators. The author demonstrates the process of transforming price data into "images", their processing by a neural network, and a unique opportunity to peer into the "consciousness" of AI through activation maps and attention heatmaps. Practical Python code using the MetaTrader 5 library allows readers to reproduce the system and apply it in their own trading.

Market Simulation (Part 10): Sockets (IV)

In this article, we'll look at what you need to do to start using Excel to manage MetaTrader 5, but in a very interesting way. To do this, we will use an Excel add-in to avoid using built-in VBA. If you don't know what add-in is meant, read this article and learn how to program in Python directly in Excel.

Tables in the MVC Paradigm in MQL5: Customizable and sortable table columns

In the article, we will make the table column widths adjustable using the mouse cursor, sort the table by column data, and add a new class to simplify the creation of tables based on any data sets.

Tables in the MVC Paradigm in MQL5: Integrating the Model Component into the View Component

In the article, we will create the first version of the TableControl (TableView) control. This will be a simple static table being created based on the input data defined by two arrays — a data array and an array of column headers.

The View and Controller components for tables in the MQL5 MVC paradigm: Resizable elements

In the article, we will add the functionality of resizing controls by dragging edges and corners of the element with the mouse.

The View and Controller components for tables in the MQL5 MVC paradigm: Containers

In this article, we will discuss creating a "Container" control that supports scrolling its contents. Within the process, the already implemented classes of graphics library controls will be improved.

The View and Controller components for tables in the MQL5 MVC paradigm: Simple controls

The article covers simple controls as components of more complex graphical elements of the View component within the framework of table implementation in the MVC (Model-View-Controller) paradigm. The basic functionality of the Controller is implemented for interaction of elements with the user and with each other. This is the second article on the View component and the fourth one in a series of articles on creating tables for the MetaTrader 5 client terminal.

Table and Header Classes based on a table model in MQL5: Applying the MVC concept

This is the second part of the article devoted to the implementation of the table model in MQL5 using the MVC (Model-View-Controller) architectural paradigm. The article discusses the development of table classes and the table header based on a previously created table model. The developed classes will form the basis for further implementation of View and Controller components, which will be discussed in the following articles.

Implementation of a table model in MQL5: Applying the MVC concept

In this article, we look at the process of developing a table model in MQL5 using the MVC (Model-View-Controller) architectural pattern to separate data logic, presentation, and control, enabling structured, flexible, and scalable code. We consider implementation of classes for building a table model, including the use of linked lists for storing data.

Larry Williams Market Secrets (Part 8): Combining Volatility, Structure and Time Filters

An in-depth walkthrough of building a Larry Williams inspired volatility breakout Expert Advisor in MQL5, combining swing structure, volatility-based entries, trade day of the week filtering, time filters, and flexible risk management, with a complete implementation and reproducible test setup.

Forex Arbitrage Trading: Relationship Assessment Panel

This article presents the development of an arbitrage analysis panel in MQL5. How to get fair exchange rates on Forex in different ways? Create an indicator to obtain deviations of market prices from fair exchange rates, as well as to assess the benefits of arbitrage ways of exchanging one currency for another (as in triangular arbitrage).

Neural Networks in Trading: Two-Dimensional Connection Space Models (Final Part)

We continue to explore the innovative Chimera framework – a two-dimensional state-space model that uses neural network technologies to analyze multidimensional time series. This method provides high forecasting accuracy with low computational cost.

Introduction to MQL5 (Part 35): Mastering API and WebRequest Function in MQL5 (IX)

Discover how to detect user actions in MetaTrader 5, send requests to an AI API, extract responses, and implement scrolling text in your panel.

Larry Williams Market Secrets (Part 3): Proving Non-Random Market Behavior with MQL5

Explore whether financial markets are truly random by recreating Larry Williams’ market behavior experiments using MQL5. This article demonstrates how simple price-action tests can reveal statistical market biases using a custom Expert Advisor.

Neural Networks in Trading: Multi-Task Learning Based on the ResNeXt Model (Final Part)

We continue exploring a multi-task learning framework based on ResNeXt, which is characterized by modularity, high computational efficiency, and the ability to identify stable patterns in data. Using a single encoder and specialized "heads" reduces the risk of model overfitting and improves the quality of forecasts.

From Basic to Intermediate: Structs (II)

In this article, we will try to understand why programming languages like MQL5 have structures, and why in some cases structures are the ideal way to pass values between functions and procedures, while in other cases they may not be the best way to do it.

Neural Networks in Trading: Multi-Task Learning Based on the ResNeXt Model

A multi-task learning framework based on ResNeXt optimizes the analysis of financial data, taking into account its high dimensionality, nonlinearity, and time dependencies. The use of group convolution and specialized heads allows the model to effectively extract key features from the input data.

Neural Networks in Trading: Hierarchical Dual-Tower Transformer (Final Part)

We continue to build the Hidformer hierarchical dual-tower transformer model designed for analyzing and forecasting complex multivariate time series. In this article, we will bring the work we started earlier to its logical conclusion — we will test the model on real historical data.

Risk Management (Part 2): Implementing Lot Calculation in a Graphical Interface

In this article, we will look at how to improve and more effectively apply the concepts presented in the previous article using the powerful MQL5 graphical control libraries. We'll go step by step through the process of creating a fully functional GUI. I'll be explaining the ideas behind it, as well as the purpose and operation of each method used. Additionally, at the end of the article, we will test the panel we created to ensure it functions correctly and meets its stated goals.

How can century-old functions update your trading strategies?

This article considers the Rademacher and Walsh functions. We will explore ways to apply these functions to financial time series analysis and also consider various applications for them in trading.

Market Simulation (Part 05): Creating the C_Orders Class (II)

In this article, I will explain how Chart Trade, together with the Expert Advisor, will process a request to close all of the users' open positions. This may sound simple, but there are a few complications that you need to know how to manage.

Building a Trading System (Part 5): Managing Gains Through Structured Trade Exits

For many traders, it's a familiar pain point: watching a trade come within a whisker of your profit target, only to reverse and hit your stop-loss. Or worse, seeing a trailing stop close you out at breakeven before the market surges toward your original target. This article focuses on using multiple entries at different Reward-to-Risk Ratios to systematically secure gains and reduce overall risk exposure.

Biological neuron for forecasting financial time series

We will build a biologically correct system of neurons for time series forecasting. The introduction of a plasma-like environment into the neural network architecture creates a kind of "collective intelligence," where each neuron influences the system's operation not only through direct connections, but also through long-range electromagnetic interactions. Let's see how the neural brain modeling system will perform in the market.

Risk Management (Part 1): Fundamentals for Building a Risk Management Class

In this article, we'll cover the basics of risk management in trading and learn how to create your first functions for calculating the appropriate lot size for a trade, as well as a stop-loss. Additionally, we will go into detail about how these features work, explaining each step. Our goal is to provide a clear understanding of how to apply these concepts in automated trading. Finally, we will put everything into practice by creating a simple script with an include file.

Introduction to MQL5 (Part 23): Automating Opening Range Breakout Strategy

This article explores how to build an Opening Range Breakout (ORB) Expert Advisor in MQL5. It explains how the EA identifies breakouts from the market’s initial range and opens trades accordingly. You’ll also learn how to control the number of positions opened and set a specific cutoff time to stop trading automatically.

Price Action Analysis Toolkit Development (Part 45): Creating a Dynamic Level-Analysis Panel in MQL5

In this article, we explore a powerful MQL5 tool that let's you test any price level you desire with just one click. Simply enter your chosen level and press analyze, the EA instantly scans historical data, highlights every touch and breakout on the chart, and displays statistics in a clean, organized dashboard. You'll see exactly how often price respected or broke through your level, and whether it behaved more like support or resistance. Continue reading to explore the detailed procedure.

Moving to MQL5 Algo Forge (Part 4): Working with Versions and Releases

We'll continue developing the Simple Candles and Adwizard projects, while also describing the finer aspects of using the MQL5 Algo Forge version control system and repository.

Bivariate Copulae in MQL5 (Part 1): Implementing Gaussian and Student's t-Copulae for Dependency Modeling

This is the first part of an article series presenting the implementation of bivariate copulae in MQL5. This article presents code implementing Gaussian and Student's t-copulae. It also delves into the fundamentals of statistical copulae and related topics. The code is based on the Arbitragelab Python package by Hudson and Thames.

Market Simulation (Part 03): A Matter of Performance

Often we have to take a step back and then move forward. In this article, we will show all the changes necessary to ensure that the Mouse and Chart Trade indicators do not break. As a bonus, we'll also cover other changes that have occurred in other header files that will be widely used in the future.

How to publish code to CodeBase: A practical guide

In this article, we will use real-life examples to illustrate posting various types of terminal programs in the MQL5 source code base.

From Novice to Expert: Market Periods Synchronizer

In this discussion, we introduce a Higher-to-Lower Timeframe Synchronizer tool designed to solve the problem of analyzing market patterns that span across higher timeframe periods. The built-in period markers in MetaTrader 5 are often limited, rigid, and not easily customizable for non-standard timeframes. Our solution leverages the MQL5 language to develop an indicator that provides a dynamic and visual way to align higher timeframe structures within lower timeframe charts. This tool can be highly valuable for detailed market analysis. To learn more about its features and implementation, I invite you to join the discussion.

Reusing Invalidated Orderblocks As Mitigation Blocks (SMC)

In this article, we explore how previously invalidated orderblocks can be reused as mitigation blocks within Smart Money Concepts (SMC). These zones reveal where institutional traders re-enter the market after a failed orderblock, providing high-probability areas for trade continuation in the dominant trend.

How to earn money by fulfilling traders' orders in the Freelance service

MQL5 Freelance is an online service where developers are paid to create trading applications for traders customers. The service has been successfully operating since 2010, with over 100,000 projects completed to date, totaling $7 million in value. As we can see, a substantial amount of money is involved here.

Price Action Analysis Toolkit Development (Part 44): Building a VWMA Crossover Signal EA in MQL5

This article introduces a VWMA crossover signal tool for MetaTrader 5, designed to help traders identify potential bullish and bearish reversals by combining price action with trading volume. The EA generates clear buy and sell signals directly on the chart, features an informative panel, and allows for full user customization, making it a practical addition to your trading strategy.

This is my own algorithm. The article presents the Time Evolution Travel Algorithm (TETA) inspired by the concept of parallel universes and time streams. The basic idea of the algorithm is that, although time travel in the conventional sense is impossible, we can choose a sequence of events that lead to different realities.