Articles on trading system automation in MQL5
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Neural Networks in Trading: An Ensemble of Agents with Attention Mechanisms (Final Part)
In the previous article, we introduced the multi-agent adaptive framework MASAAT, which uses an ensemble of agents to perform cross-analysis of multimodal time series at different data scales. Today we will continue implementing the approaches of this framework in MQL5 and bring this work to a logical conclusion.
Developing a multi-currency Expert Advisor (Part 13): Automating the second stage — selection into groups
We have already implemented the first stage of the automated optimization. We perform optimization for different symbols and timeframes according to several criteria and store information about the results of each pass in the database. Now we are going to select the best groups of parameter sets from those found at the first stage.
Mastering PD Arrays: Optimizing Trading from Imbalances in PD Arrays
This is an article about a specialized trend-following EA that aims to clearly elaborate how to frame and utilize trading setups that occur from imbalances found in PD arrays. This article will explore in detail an EA that is specifically designed for traders who are keen on optimizing and utilizing PD arrays and imbalances as entry criteria for their trades and trading decisions. It will also explore how to correctly determine and profile premium and discount arrays and how to validate and utilize each of them when they occur in their respective market conditions, thus trying to maximize opportunities that occur from such scenarios.
Engineering Trading Discipline into Code (Part 3): Enforcing Symbol-Level Trading Boundaries with a Whitelist System in MQL5
This article details an MQL5 framework that restricts trading to an approved set of symbols. The solution combines a shared library, a configuration dashboard, and an enforcement Expert Advisor that validates each trade against a whitelist and logs blocked attempts. It includes fully functional code examples, a clear explanation of the structural design decisions, and validation tests that confirm reliable symbol filtering, controlled market exposure, and transparent monitoring of rule enforcement.
Neural networks made easy (Part 20): Autoencoders
We continue to study unsupervised learning algorithms. Some readers might have questions regarding the relevance of recent publications to the topic of neural networks. In this new article, we get back to studying neural networks.
Introduction to MQL5 (Part 20): Introduction to Harmonic Patterns
In this article, we explore the fundamentals of harmonic patterns, their structures, and how they are applied in trading. You’ll learn about Fibonacci retracements, extensions, and how to implement harmonic pattern detection in MQL5, setting the foundation for building advanced trading tools and Expert Advisors.
Building a Research-Grounded Grid EA in MQL5: Why Most Grid EAs Fail and What Taranto Proved
This article implements a regime-adaptive grid trading EA based on the PhD research of Aldo Taranto. It presents a regime‑adaptive grid trading EA that constrains risk through restartable cycles and equity‑based safeguards. We explain why naive grids fail (variance growth and almost‑sure ruin), derive the loss formula for real‑time exposure, and implement regime‑aware gating, ATR‑dynamic spacing, and a live kill switch. Readers get the mathematical tools and production patterns needed to build, test, and operate a constrained grid safely.
Neural Networks in Trading: Optimizing the Transformer for Time Series Forecasting (LSEAttention)
The LSEAttention framework offers improvements to the Transformer architecture. It was designed specifically for long-term multivariate time series forecasting. The approaches proposed by the authors of the method can be applied to solve problems of entropy collapse and learning instability, which are often encountered with vanilla Transformer.
Neuro-symbolic systems in algorithmic trading: Combining symbolic rules and neural networks
The article describes the experience of developing a hybrid trading system that combines classical technical analysis with neural networks. The author provides a detailed analysis of the system architecture from basic pattern analysis and neural network structure to the mechanisms behind trading decisions, and shares real code and practical observations.
Developing a multi-currency Expert Advisor (Part 2): Transition to virtual positions of trading strategies
Let's continue developing a multi-currency EA with several strategies working in parallel. Let's try to move all the work associated with opening market positions from the strategy level to the level of the EA managing the strategies. The strategies themselves will trade only virtually, without opening market positions.
Finding custom currency pair patterns in Python using MetaTrader 5
Are there any repeating patterns and regularities in the Forex market? I decided to create my own pattern analysis system using Python and MetaTrader 5. A kind of symbiosis of math and programming for conquering Forex.
Data Science and ML (Part 42): Forex Time series Forecasting using ARIMA in Python, Everything you need to Know
ARIMA, short for Auto Regressive Integrated Moving Average, is a powerful traditional time series forecasting model. With the ability to detect spikes and fluctuations in a time series data, this model can make accurate predictions on the next values. In this article, we are going to understand what is it, how it operates, what you can do with it when it comes to predicting the next prices in the market with high accuracy and much more.
Neural networks made easy (Part 53): Reward decomposition
We have already talked more than once about the importance of correctly selecting the reward function, which we use to stimulate the desired behavior of the Agent by adding rewards or penalties for individual actions. But the question remains open about the decryption of our signals by the Agent. In this article, we will talk about reward decomposition in terms of transmitting individual signals to the trained Agent.
MetaTrader 5 Machine Learning Blueprint (Part 8): Bayesian Hyperparameter Optimization with Purged Cross-Validation and Trial Pruning
GridSearchCV and RandomizedSearchCV share a fundamental limitation in financial ML: each trial is independent, so search quality does not improve with additional compute. This article integrates Optuna — using the Tree-structured Parzen Estimator — with PurgedKFold cross-validation, HyperbandPruner early stopping, and a dual-weight convention that separates training weights from evaluation weights. The result is a five-component system: an objective function with fold-level pruning, a suggestion layer that optimizes the weighting scheme jointly with model hyperparameters, a financially-calibrated pruner, a resumable SQLite-backed orchestrator, and a converter to scikit-learn cv_results_ format. The article also establishes the boundary — drawn from Timothy Masters — between statistical objectives where directed search is beneficial and financial objectives where it is harmful.
Overcoming The Limitation of Machine Learning (Part 5): A Quick Recap of Time Series Cross Validation
In this series of articles, we look at the challenges faced by algorithmic traders when deploying machine-learning-powered trading strategies. Some challenges within our community remain unseen because they demand deeper technical understanding. Today’s discussion acts as a springboard toward examining the blind spots of cross-validation in machine learning. Although often treated as routine, this step can easily produce misleading or suboptimal results if handled carelessly. This article briefly revisits the essentials of time series cross-validation to prepare us for more in-depth insight into its hidden blind spots.
Creating a market making algorithm in MQL5
How do market makers work? Let's consider this issue and create a primitive market-making algorithm.
Developing a multi-currency Expert Advisor (Part 19): Creating stages implemented in Python
So far we have considered the automation of launching sequential procedures for optimizing EAs exclusively in the standard strategy tester. But what if we would like to perform some handling of the obtained data using other means between such launches? We will attempt to add the ability to create new optimization stages performed by programs written in Python.
Neural networks made easy (Part 66): Exploration problems in offline learning
Models are trained offline using data from a prepared training dataset. While providing certain advantages, its negative side is that information about the environment is greatly compressed to the size of the training dataset. Which, in turn, limits the possibilities of exploration. In this article, we will consider a method that enables the filling of a training dataset with the most diverse data possible.
Neural Networks in Trading: Hybrid Graph Sequence Models (GSM++)
Hybrid graph sequence models (GSM++) combine the advantages of different architectures to provide high-fidelity data analysis and optimized computational costs. These models adapt effectively to dynamic market data, improving the presentation and processing of financial information.
Experiments with neural networks (Part 4): Templates
In this article, I will use experimentation and non-standard approaches to develop a profitable trading system and check whether neural networks can be of any help for traders. MetaTrader 5 as a self-sufficient tool for using neural networks in trading. Simple explanation.
MQL5 Wizard Techniques you should know (Part 71): Using Patterns of MACD and the OBV
The Moving-Average-Convergence-Divergence (MACD) oscillator and the On-Balance-Volume (OBV) oscillator are another pair of indicators that could be used in conjunction within an MQL5 Expert Advisor. This pairing, as is practice in these article series, is complementary with the MACD affirming trends while OBV checks volume. As usual, we use the MQL5 wizard to build and test any potential these two may possess.
Data Science and Machine Learning(Part 14): Finding Your Way in the Markets with Kohonen Maps
Are you looking for a cutting-edge approach to trading that can help you navigate complex and ever-changing markets? Look no further than Kohonen maps, an innovative form of artificial neural networks that can help you uncover hidden patterns and trends in market data. In this article, we'll explore how Kohonen maps work, and how they can be used to develop smarter, more effective trading strategies. Whether you're a seasoned trader or just starting out, you won't want to miss this exciting new approach to trading.
Neural Networks in Trading: A Multimodal, Tool-Augmented Agent for Financial Markets (Final Part)
We continue to develop the algorithms for FinAgent, a multimodal financial trading agent designed to analyze multimodal market dynamics data and historical trading patterns.
Neural Networks Made Easy (Part 87): Time Series Patching
Forecasting plays an important role in time series analysis. In the new article, we will talk about the benefits of time series patching.
Self Optimizing Expert Advisors in MQL5 (Part 17): Ensemble Intelligence
All algorithmic trading strategies are difficult to set up and maintain, regardless of complexity—a challenge shared by beginners and experts alike. This article introduces an ensemble framework where supervised models and human intuition work together to overcome their shared limitations. By aligning a moving average channel strategy with a Ridge Regression model on the same indicators, we achieve centralized control, faster self-correction, and profitability from otherwise unprofitable systems.
MQL5 Trading Tools (Part 7): Informational Dashboard for Multi-Symbol Position and Account Monitoring
In this article, we develop an informational dashboard in MQL5 for monitoring multi-symbol positions and account metrics like balance, equity, and free margin. We implement a sortable grid with real-time updates, CSV export, and a glowing header effect to enhance usability and visual appeal.
Developing a trading Expert Advisor from scratch (Part 23): New order system (VI)
We will make the order system more flexible. Here we will consider changes to the code that will make it more flexible, which will allow us to change position stop levels much faster.
MQL5 Trading Toolkit (Part 1): Developing A Positions Management EX5 Library
Learn how to create a developer's toolkit for managing various position operations with MQL5. In this article, I will demonstrate how to create a library of functions (ex5) that will perform simple to advanced position management operations, including automatic handling and reporting of the different errors that arise when dealing with position management tasks with MQL5.
Neural Networks in Trading: Scene-Aware Object Detection (HyperDet3D)
We invite you to get acquainted with a new approach to detecting objects using hypernetworks. A hypernetwork generates weights for the main model, which allows taking into account the specifics of the current market situation. This approach allows us to improve forecasting accuracy by adapting the model to different trading conditions.
MQL5 Trading Toolkit (Part 8): How to Implement and Use the History Manager EX5 Library in Your Codebase
Discover how to effortlessly import and utilize the History Manager EX5 library in your MQL5 source code to process trade histories in your MetaTrader 5 account in this series' final article. With simple one-line function calls in MQL5, you can efficiently manage and analyze your trading data. Additionally, you will learn how to create different trade history analytics scripts and develop a price-based Expert Advisor as practical use-case examples. The example EA leverages price data and the History Manager EX5 library to make informed trading decisions, adjust trade volumes, and implement recovery strategies based on previously closed trades.
Developing a multi-currency Expert Advisor (Part 12): Developing prop trading level risk manager
In the EA being developed, we already have a certain mechanism for controlling drawdown. But it is probabilistic in nature, as it is based on the results of testing on historical price data. Therefore, the drawdown can sometimes exceed the maximum expected values (although with a small probability). Let's try to add a mechanism that ensures guaranteed compliance with the specified drawdown level.
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.
Developing a trading Expert Advisor from scratch (Part 25): Providing system robustness (II)
In this article, we will make the final step towards the EA's performance. So, be prepared for a long read. To make our Expert Advisor reliable, we will first remove everything from the code that is not part of the trading system.
Quantum computing and trading: A fresh approach to price forecasts
The article describes an innovative approach to forecasting price movements in financial markets using quantum computing. The main focus is on the application of the Quantum Phase Estimation (QPE) algorithm to find prototypes of price patterns allowing traders to significantly speed up the market data analysis.
Creation of an Automated Trading System
You must admit that it sounds alluringly - you become a fortunate possessor of a program that can develop for you a profitable automated trading system (ATC) within a few minutes. All you need is to enter desirable inputs and press Enter. And - here you are, take your ATC tested and having positive expected payoff. Where thousands of people spend thousands of hours on developing that very unique ATC, which will "wine and dine", these statements soundб to put it mildly, very hollow. On the one hand, this really looks a little larger than life... However, to my mind, this problem can be solved.
Neural Networks Made Easy (Part 94): Optimizing the Input Sequence
When working with time series, we always use the source data in their historical sequence. But is this the best option? There is an opinion that changing the sequence of the input data will improve the efficiency of the trained models. In this article I invite you to get acquainted with one of the methods for optimizing the input sequence.
Data Science and Machine Learning (Part 19): Supercharge Your AI models with AdaBoost
AdaBoost, a powerful boosting algorithm designed to elevate the performance of your AI models. AdaBoost, short for Adaptive Boosting, is a sophisticated ensemble learning technique that seamlessly integrates weak learners, enhancing their collective predictive strength.
Brute force approach to patterns search (Part V): Fresh angle
In this article, I will show a completely different approach to algorithmic trading I ended up with after quite a long time. Of course, all this has to do with my brute force program, which has undergone a number of changes that allow it to solve several problems simultaneously. Nevertheless, the article has turned out to be more general and as simple as possible, which is why it is also suitable for those who know nothing about brute force.
Neural networks made easy (Part 35): Intrinsic Curiosity Module
We continue to study reinforcement learning algorithms. All the algorithms we have considered so far required the creation of a reward policy to enable the agent to evaluate each of its actions at each transition from one system state to another. However, this approach is rather artificial. In practice, there is some time lag between an action and a reward. In this article, we will get acquainted with a model training algorithm which can work with various time delays from the action to the reward.
Developing a multi-currency Expert Advisor (Part 4): Pending virtual orders and saving status
Having started developing a multi-currency EA, we have already achieved some results and managed to carry out several code improvement iterations. However, our EA was unable to work with pending orders and resume operation after the terminal restart. Let's add these features.