Articles on trading system automation in MQL5

An empirical study of Larry Williams' short-term trading patterns, showing how classic setups can be automated in MQL5, tested on real market data, and evaluated for consistency, profitability, and practical trading value.

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.

We invite you to explore a new approach that combines classical methods and modern neural networks for time series analysis. The article provides a detailed explanation of the architecture and operating principles of the K²VAE model.

The article presents a complete implementation of TimeGPT, a specialized Transformer-based architecture for forecasting financial time series on the MetaTrader 5 platform. Adaptation of the attention mechanism to financial data, selective tokenization of price changes, hardware-aware optimizations, and advanced learning techniques are discussed. Included are practical testing results showing 87% forecast accuracy over a 24-bar horizon with a training time of 15 minutes on the CPU. We also present a ready-made trading EA with automatic retraining.

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 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.

Non-linear regression models on the stock exchange: Is it possible to predict financial markets? Let's consider creating a model for forecasting prices for EURUSD, and make two robots based on it - in Python and MQL5.

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.

Currently, our EA uses the database to obtain initialization strings for single instances of trading strategies. However, the database is quite large and contains a lot of information that is not needed for the actual EA operation. Let's try to ensure the EA's functionality without a mandatory connection to the database.

The article presents a simple and accessible way to use a neural network in a trading EA that does not require deep knowledge of machine learning. The method eliminates the target function normalization, as well as overcomes "weight explosion" and "network stall" issues offering intuitive training and visual control of the results.

This article is aimed at beginners and pro-MQL5 developers. It provides a piece of code to define and constrain signal-generating indicators to trends in higher timeframes. In this way, traders can enhance their strategies by incorporating a broader market perspective, leading to potentially more robust and reliable trading signals.

In this article, we enhance the regression graphing tool in MQL5 by adding a cyberpunk theme mode with neon glows, animations, and holographic effects for immersive visualization. We integrate theme toggling, dynamic backgrounds with stars, glowing borders, and neon points/lines, while maintaining standard mode compatibility. This dual-theme system elevates pair analysis with futuristic aesthetics, supporting real-time updates and interactions for engaging trading insights.

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.

In this article, we create a dynamic holographic dashboard in MQL5 for monitoring symbols and timeframes with RSI, volatility alerts, and sorting options. We add pulse animations, interactive buttons, and holographic effects to make the tool visually engaging and responsive.

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.

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 develop a canvas-based price dashboard in MQL5 using the CCanvas class to create interactive panels for visualizing recent price graphs and account statistics, with support for background images, fog effects, and gradient fills. The system includes draggable and resizable features via mouse event handling, theme toggling between dark and light modes with dynamic color adjustments, and minimize/maximize controls for efficient chart space management.

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.

Examples of passing indicators to a perceptron. The article describes general concepts and showcases the simplest ready-made Expert Advisor followed by the results of its optimization and forward test.

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 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.

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.

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.

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.

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.

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

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.

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.

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.

In this discussion, we introduce a structured, multi-layered defense system designed to pursue aggressive profit targets while minimizing exposure to catastrophic loss. The focus is on blending offensive trading logic with protective safeguards at every level of the trading pipeline. The idea is to engineer an EA that behaves like a “risk-aware predator”—capable of capturing high-value opportunities, but always with layers of insulation that prevent blindness to sudden market stress.