Articles on trading system automation in MQL5

Aligned with our vision of simplifying price action, we are pleased to introduce another tool that can significantly enhance your market analysis and help you make well-informed decisions. This tool displays key technical indicators such as previous day's prices, significant support and resistance levels, and trading volume, while automatically generating visual cues on the chart.

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.

This article continues the series on category theory implementation in MQL5. Here we introduce monoids as domain (set) that sets category theory apart from other data classification methods by including rules and an identity element.

We continue to explore reinforcement learning methods. In this article, I will focus on a slightly different algorithm that considers the Agent’s policy in the paradigm of constructing a sequence of actions.

In this article, we develop a custom indicator in MQL5 called Smart WaveTrend Crossover, utilizing dual WaveTrend oscillators—one for generating crossover signals and another for trend filtering—with customizable parameters for channel, average, and moving average lengths. The indicator plots colored candles based on the trend direction, displays buy and sell arrow signals on crossovers, and includes options to enable trend confirmation and adjust visual elements like colors and offsets.

In this article, we'll cover an important topic that few people understand: Custom Events. Dangers. Advantages and disadvantages of these elements. This topic is key for those who want to become a professional programmer in MQL5 or any other language. Here we will focus on MQL5 and MetaTrader 5.

With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.

We continue to discuss algorithms for training trajectory prediction models. In this article, we will get acquainted with a method called "AutoBots".

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.

In an attempt to obtain the most accurate forecasts, researchers often complicate forecasting models. Which in turn leads to increased model training and maintenance costs. Is such an increase always justified? This article introduces an algorithm that uses the simplicity and speed of linear models and demonstrates results on par with the best models with a more complex architecture.

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.

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.

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.

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.

Forecasting plays an important role in time series analysis. In the new article, we will talk about the benefits of time series patching.

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.

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.

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.

Let's move on to a more complete order system directly on the chart. In this article, I will show a way to fix the order system, or rather, to make it more intuitive.

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.

We continue to develop the algorithms for FinAgent, a multimodal financial trading agent designed to analyze multimodal market dynamics data and historical trading patterns.

We are creating an adaptive self-learning trading expert advisor based on DQN machine learning, with multidimensional causal inference. The EA will successfully trade simultaneously on 7 currency pairs. And agents of different pairs will exchange information with each other.

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.

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.

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.

Tired of watching progress bars instead of testing trading strategies? Traditional caching fails financial ML, leaving you with lost computations and frustrating restarts. We've engineered a sophisticated caching architecture that understands the unique challenges of financial data—temporal dependencies, complex data structures, and the constant threat of look-ahead bias. Our three-layer system delivers dramatic speed improvements while automatically invalidating stale results and preventing costly data leaks. Stop waiting for computations and start iterating at the pace the markets demand.

In this session, we will build a sophisticated, multi-signal Expert Advisor using the MQL5 Standard Library. This approach allows us to seamlessly blend built-in signals with our own custom logic, demonstrating how to construct a powerful and flexible trading algorithm. For more, click to read further.

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.

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.

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.

A structured MQL5 implementation of a multi‑symbol trading panel with clear separation of concerns: symbol handling, trading logic, and GUI. Integrated into an Expert Advisor, it validates symbols, exposes centralized controls for opening and positions managing across symbols, and applies SL/TP changes. Real‑time account and portfolio metrics help streamline routine operations from a single chart.

In our series on integrating MQL5 with data processing packages, we delve in to the powerful combination of machine learning and predictive analysis. We will explore how to seamlessly connect MQL5 with popular machine learning libraries, to enable sophisticated predictive models for financial markets.

In this article, we develop a 5 Drives pattern system in MQL5 that identifies bullish and bearish 5 Drives harmonic patterns using pivot points and Fibonacci ratios, executing trades with customizable entry, stop loss, and take-profit levels based on user-selected options. We enhance trader insight with visual feedback through chart objects like triangles, trendlines, and labels to clearly display the A-B-C-D-E-F pattern structure.

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.