MQL4 and MQL5 Programming Articles

The strategy tester allows you to do more than just optimize your trading robot's parameters. I will show how to evaluate your account's trading history post-factum and make adjustments to your trading in the tester by changing the stop-losses of your open positions.

Today, we use the MQL5 Standard Library to build custom signal classes and let the MQL5 Wizard assemble a professional Expert Advisor for us. This approach simplifies development so that even beginner programmers can create robust EAs without in-depth coding knowledge, focusing instead on tuning inputs and optimizing performance. Join this discussion as we explore the process step by step.

In this article, we explore the challenge of understanding how AI works. AI models often make decisions in ways that are hard to explain, leading to what's known as the "disagreement problem". This issue is key to making AI more transparent and trustworthy.

In this article we will do things that many will find strange and completely out of context, but which, if used correctly, will make your learning much more fun and interesting: we will be able to build quite interesting things based on what is shown here. This will allow you to better understand the syntax of the MQL5 language. The materials provided here are for educational purposes only. It should not be considered in any way as a final application. Its purpose is not to explore the concepts presented.

In this article, we will take a very close look at how to solve the problem posed at the end of the previous article. There was an attempt to create a template of such type so that to be able to create a template for data union.

In this article, we will explore various methods used in binary genetic and other population algorithms. We will look at the main components of the algorithm, such as selection, crossover and mutation, and their impact on the optimization. In addition, we will study data presentation methods and their impact on optimization results.

Security prompts, such as those triggered every time you refresh the chart, add a new pair to the chat with the Admin Panel EA, or restart the terminal, can become tedious. In this discussion, we will explore and implement a feature that tracks the number of login attempts to identify a trusted user. After a set number of failed attempts, the application will transition to an advanced login procedure, which also facilitates passcode recovery for users who may have forgotten it. Additionally, we will cover how cryptography can be effectively integrated into the Admin Panel to enhance security.

This article presents a comprehensive guide to implementing a sophisticated trading system using Causality Network Analysis (CNA) and Vector Autoregression (VAR) in MQL5. It covers the theoretical background of these methods, provides detailed explanations of key functions in the trading algorithm, and includes example code for implementation.

In this article, we continue our exploration of ensemble models by discussing the concept of gates, specifically how they may be useful in combining model outputs to enhance either prediction accuracy or model generalization.

The article presents the Artificial Showering Algorithm (ASHA), a new metaheuristic method developed for solving general optimization problems. Based on simulation of water flow and accumulation processes, this algorithm constructs the concept of an ideal field, in which each unit of resource (water) is called upon to find an optimal solution. We will find out how ASHA adapts flow and accumulation principles to efficiently allocate resources in a search space, and see its implementation and test results.

Discover how to create exportable EX5 functions to efficiently query and save historical position data. In this step-by-step guide, we will expand the History Management EX5 library by developing modules that retrieve key properties of the most recently closed position. These include net profit, trade duration, pip-based stop loss, take profit, profit values, and various other important details.

The article demonstrates spurious regression occurring when attempting to apply regression analysis to non-stationary processes using Monte Carlo simulation.

This article proposes using Rolling Windows Eigenvector Comparison for early imbalance diagnostics and portfolio rebalancing in a mean-reversion statistical arbitrage strategy based on cointegrated stocks. It contrasts this technique with traditional In-Sample/Out-of-Sample ADF validation, showing that eigenvector shifts can signal the need for rebalancing even when IS/OOS ADF still indicates a stationary spread. While the method is intended mainly for live trading monitoring, the article concludes that eigenvector comparison could also be integrated into the scoring system—though its actual contribution to performance remains to be tested.

This article offers a step-by-step guide to mastering CRUD (Create, Read, Update, Delete) operations in Firebase, focusing on its Realtime Database and Firestore. Discover how to use Firebase SDK methods to efficiently manage data in web and mobile apps, from adding new records to querying, modifying, and deleting entries. Explore practical code examples and best practices for structuring and handling data in real-time, empowering developers to build dynamic, scalable applications with Firebase’s flexible NoSQL architecture.

Today we have a very funny and quite interesting article. We will look at Union and will try to solve the problem discussed earlier. We'll also explore some unusual situations that can arise when using union in applications. The materials presented here are intended for didactic purposes only. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.

In this article we explore different ways in which the eigenvectors and eigenvalues can be applied in exploratory data analysis to reveal unique relationships in data.

The MacroHFT framework for high-frequency cryptocurrency trading uses context-aware reinforcement learning and memory to adapt to dynamic market conditions. At the end of this article, we will test the implemented approaches on real historical data to assess their effectiveness.

This article will dive into methods to improve the expert's runtime in the strategy tester, the code will be written to divide news event times into hourly categories. These news event times will be accessed within their specified hour. This ensures that the EA can efficiently manage event-driven trades in both high and low-volatility environments.

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.

We aim to create a system for automatic periodic optimization of trading strategies used in one final EA. As the system evolves, it becomes increasingly complex, so it is necessary to look at it as a whole from time to time in order to identify bottlenecks and suboptimal solutions.

This piece follows up ‘Part-84’, where we introduced the pairing of Stochastic and the Fractal Adaptive Moving Average. We now shift focus to Inference Learning, where we look to see if laggard patterns in the last article could have their fortunes turned around. The Stochastic and FrAMA are a momentum-trend complimentary pairing. For our inference learning, we are revisiting the Beta algorithm of a Variational Auto Encoder. We also, as always, do the implementation of a custom signal class designed for integration with the MQL5 Wizard.

In this article, we will get acquainted with an algorithm that uses closed-form policy improvement operators to optimize Agent actions in offline mode.

In this series of articles, we revisit well-known trading strategies to see if we can improve them using AI. In today's discussion, join us as we test whether there is a reliable relationship between precious metals and currencies.

In this article, we present the Arithmetic Optimization Algorithm (AOA) based on simple arithmetic operations: addition, subtraction, multiplication and division. These basic mathematical operations serve as the foundation for finding optimal solutions to various problems.

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.