Articles with examples of trading robots developed in MQL5

This article walks the reader through a reimagined version of the classical Bollinger Band breakout strategy. It identifies key weaknesses in the original approach, such as its well-known susceptibility to false breakouts. The article aims to introduce a possible solution: the Double Bollinger Band trading strategy. This relatively lesser known approach supplements the weaknesses of the classical version and offers a more dynamic perspective on financial markets. It helps us overcome the old limitations defined by the original rules, providing traders with a stronger and more adaptive framework.

In this article, we enhance the ChatGPT-integrated program in MQL5 overcoming multiline input limitations with improved text rendering, introducing a sidebar for navigating persistent chat storage using AES256 encryption and ZIP compression, and generating initial trade signals through chart data integration.

This article introduces the new PSformer framework, which adapts the architecture of the vanilla Transformer to solving problems related to multivariate time series forecasting. The framework is based on two key innovations: the Parameter Sharing (PS) mechanism and the Segment Attention (SegAtt).

As a continuation of this series of articles, let's consider another type of problems within unsupervised learning methods: mining association rules. This problem type was first used in retail, namely supermarkets, to analyze market baskets. In this article, we will talk about the applicability of such algorithms in trading.

The study of the environment in reinforcement learning is a pressing problem. We have already looked at some approaches previously. In this article, we will have a look at yet another method based on maximizing the nuclear norm. It allows agents to identify environmental states with a high degree of novelty and diversity.

Dynamic multi pair Expert Advisor leverages both on correlation and inverse correlation strategies to optimize trading performance. By analyzing real-time market data, it identifies and exploits the relationship between currency pairs.

Explore the world of MQL5 arrays in Part 5, designed for absolute beginners. Simplifying complex coding concepts, this article focuses on clarity and inclusivity. Join our community of learners, where questions are embraced, and knowledge is shared!

In this article, we will take a look to one the famous strategies of Bill Williams, and discuss it, and try to improve the strategy with other indicators and with predictions.

Whenever we consider reinforcement learning methods, we are faced with the issue of efficiently exploring the environment. Solving this issue often leads to complication of the algorithm and training of additional models. In this article, we will look at an alternative approach to solving this problem.

In this article, we introduce the development of an interactive Trade Assistant Tool in MQL5, designed to simplify placing pending orders in Forex trading. We outline the conceptual design, focusing on a user-friendly GUI for setting entry, stop-loss, and take-profit levels visually on the chart. Additionally, we detail the MQL5 implementation and backtesting process to ensure the tool’s reliability, setting the stage for advanced features in the preceding parts.

We continue to study unsupervised learning algorithms. This time I suggest that we discuss the features of autoencoders when applied to recurrent model training.

In the previous article, we explored the theoretical foundations and began implementing the approaches of the Multitask-Stockformer framework, which combines the wavelet transform and the Self-Attention multitask model. We continue to implement the algorithms of this framework and evaluate their effectiveness on real historical data.

The models we create are becoming larger and more complex. This increases the costs of not only their training as well as operation. However, the time required to make a decision is often critical. In this regard, let us consider methods for optimizing model performance without loss of quality.

In this article, we develop a Bat Pattern system in MQL5 that identifies bullish and bearish Bat harmonic patterns using pivot points and Fibonacci ratios, triggering trades with precise entry, stop loss, and take-profit levels, enhanced with visual feedback through chart objects

We introduce the Multi-Agent Self-Adaptive Portfolio Optimization Framework (MASAAT), which combines attention mechanisms and time series analysis. MASAAT generates a set of agents that analyze price series and directional changes, enabling the identification of significant fluctuations in asset prices at different levels of detail.

In this part we continue discussing Artificial Intelligence models. Namely, we study unsupervised learning algorithms. We have already discussed one of the clustering algorithms. In this article, I am sharing a variant of solving problems related to dimensionality reduction.

Enhancing the MQL5 GUI panel with dynamic features can significantly improve the trading experience for users. By incorporating interactive elements, hover effects, and real-time data updates, the panel becomes a powerful tool for modern traders.

We invite you to explore FinAgent, a multimodal financial trading agent framework designed to analyze various types of data reflecting market dynamics and historical trading patterns.

In this article, we enhance the MQL5 Economic Calendar with a dynamic scrollbar for intuitive news navigation. We ensure seamless event display and efficient updates. We validate the responsive scrollbar and polished dashboard through testing.

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.

In this article, we will focus on visually styling the graphical user interface (GUI) of our Trading Administrator Panel using MQL5. We’ll explore various techniques and features available in MQL5 that allow for customization and optimization of the interface, ensuring it meets the needs of traders while maintaining an attractive aesthetic.

Learn how to develop and implement a comprehensive pending orders EX5 library in your MQL5 code or projects. This article will show you how to create an extensive pending orders management EX5 library and guide you through importing and implementing it by building a trading panel or graphical user interface (GUI). The expert advisor orders panel will allow users to open, monitor, and delete pending orders associated with a specified magic number directly from the graphical interface on the chart window.

This article guides you through building a custom Heikin Ashi indicator from scratch and demonstrates how to integrate custom indicators into an EA. It covers indicator calculations, trade execution logic, and risk management techniques to enhance automated trading strategies.

This article demonstrates how to design and implement a Larry Williams volatility breakout Expert Advisor in MQL5, covering swing-range measurement, entry-level projection, risk-based position sizing, and backtesting on real market data.

Object-oriented programming enables creation of a more compact code that is easy to read and modify. Here we will have a look at the example for three ONNX models.