MQL4 and MQL5 Programming Articles

In this article, I will consider the possibility of upgrading the CCI indicator. Besides, I will present a modification of the indicator.

Join us in Part III of the "Improve Your Trading Charts With Interactive GUIs in MQL5" series as we explore the integration of interactive GUIs into movable trading dashboards in MQL5. This article builds on the foundations set in Parts I and II, guiding readers to transform static trading dashboards into dynamic, movable ones.

In this article, we create a supply and demand trading system in MQL5 that identifies supply and demand zones through consolidation ranges, validates them with impulsive moves, and trades retests with trend confirmation and customizable risk parameters. The system visualizes zones with dynamic labels and colors, supporting trailing stops for risk management.

The MetaTrader 5 module offered in Python provides a convenient way of opening trades in the MetaTrader 5 app using Python, but it has a huge problem, it doesn't have the strategy tester capability present in the MetaTrader 5 app, In this article series, we will build a framework for back testing your trading strategies in Python environments.

In this article, we will continue our series about how to design a trading system using the most popular indicators. In this article, we will learn about the Parabolic SAR indicator in detail and how we can design a trading system to be used in MetaTrader 5 using some simple strategies.

Dive into the fascinating realm of algorithmic trading with our beginner-friendly guide to MQL5 programming. Discover the essentials of MQL5, the language powering MetaTrader 5, as we demystify the world of automated trading. From understanding the basics to taking your first steps in coding, this article is your key to unlocking the potential of algorithmic trading even without a programming background. Join us on a journey where simplicity meets sophistication in the exciting universe of MQL5.

Training the CatBoost classifier in Python and exporting the model to mql5, as well as parsing the model parameters and a custom strategy tester. The Python language and the MetaTrader 5 library are used for preparing the data and for training the model.

This time our models are being made by matrices, which allows flexibility while it allows us to make powerful models that can handle not only five independent variables but also many variables as long as we stay within the calculations limits of a computer, this article is going to be an interesting read, that's for sure.

In this article, we explored the classic grid strategy, detailing its automation using an Expert Advisor in MQL5 and analyzing initial backtest results. We highlighted the strategy's need for high holding capacity and outlined plans for optimizing key parameters like distance, takeProfit, and lot sizes in future installments. The series aims to enhance trading strategy efficiency and adaptability to different market conditions.

The Multi-Currency Expert Advisor in this article is Expert Advisor or trading robot that can trade (open orders, close orders and manage orders an more) for more than 1 symbol pair only from one symbol chart.

We have previously considered the creation of automatic walk-forward optimization. This time, we will proceed to the internal structure of the auto optimizer tool. The article will be useful for all those who wish to further work with the created project and to modify it, as well as for those who wish to understand the program logic. The current article contains UML diagrams which present the internal structure of the project and the relationships between objects. It also describes the process of optimization start, but it does not contain the description of the optimizer implementation process.

The popularity of these two methods grows, so a lot of libraries have been developed in Matlab, R, Python, C++ and others, which receive a training set as input and automatically create an appropriate network for the problem. Let us try to understand how the basic neural network type works (including single-neuron perceptron and multilayer perceptron). We will consider an exciting algorithm which is responsible for network training - gradient descent and backpropagation. Existing complex models are often based on such simple network models.

In this article, we will create a dashboard class to be used in indicators and EAs. This is an introductory article in a small series of articles with templates for including and using standard indicators in Expert Advisors. I will start by creating a panel similar to the MetaTrader 5 data window.

In this article, we build a grid trading expert advisor in MQL5 that uses dynamic lot scaling. We cover the strategy design, code implementation, and backtesting process. Finally, we share key insights and best practices for optimizing the automated trading system.

In this article, we demonstrated how the fascinating mathematical concept of the Butterfly Curve can be transformed into a practical trading tool. We constructed the Butterfly Oscillator and built a foundational trading strategy around it. The strategy effectively combines the oscillator's unique cyclical signals with traditional trend confirmation from moving averages, creating a systematic approach for identifying potential market entries.