Articles on the MQL5 programming and use of trading robots

Linear Kernels are the simplest matrix of its kind used in machine learning for linear regression and support vector machines. The Matérn kernel on the other hand is a more versatile version of the Radial Basis Function we looked at in an earlier article, and it is adept at mapping functions that are not as smooth as the RBF would assume. We build a custom signal class that utilizes both kernels in forecasting long and short conditions.

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.

Let's outline the main stages of the EA development. One of the first things to be done will be to optimize a single instance of the developed trading strategy. Let's try to collect all the necessary information about the tester passes during the optimization in one place.

Learn to create and customize chart objects in MQL5 using current and historical data. This project-based guide helps you visualize trades and apply MQL5 concepts practically, making it easier to build tools tailored to your trading needs.

Reinforcement Learning is one of the three main tenets in machine learning, alongside supervised learning and unsupervised learning. It is therefore concerned with optimal control, or learning the best long-term policy that will best suit the objective function. It is with this back-drop, that we explore its possible role in informing the learning-process to an MLP of a wizard assembled Expert Advisor.

In this article, I would like to introduce you to a new complex timeseries forecasting method, which harmoniously combines the advantages of linear models and transformers.

In this article, we refactor the existing code used for sending messages and screenshots from MQL5 to Telegram by organizing it into reusable, modular functions. This will streamline the process, allowing for more efficient execution and easier code management across multiple instances.

This article introduces the Conformer algorithm originally developed for the purpose of weather forecasting, which in terms of variability and capriciousness can be compared to financial markets. Conformer is a complex method. It combines the advantages of attention models and ordinary differential equations.

Think about an independent Expert Advisor. Previously, we discussed an indicator-based Expert Advisor that also partnered with an independent script for drawing risk and reward geometry. Today, we will discuss the architecture of an MQL5 Expert Advisor, that integrates, all the features in one program.

In this article, we develop a Rapid-Fire Trading Expert Advisor in MQL5, leveraging the Parabolic SAR and Simple Moving Average (SMA) indicators to create a responsive trading strategy. We detail the strategy’s implementation, including indicator usage, signal generation, and the testing and optimization process.

In the world of big data, there are millions of alternative datasets that hold the potential to enhance our trading strategies. In this series of articles, we will help you identify the most informative public datasets.

In this article, we will automate the trading strategies with Parabolic SAR Strategy in MQL5: Crafting an Effective Expert Advisor. The EA will make trades based on trends identified by the Parabolic SAR indicator.

Restricted Boltzmann Machines are a form of neural network that was developed in the mid 1980s at a time when compute resources were prohibitively expensive. At its onset, it relied on Gibbs Sampling and Contrastive Divergence in order to reduce dimensionality or capture the hidden probabilities/properties over input training data sets. We examine how Backpropagation can perform similarly when the RBM ‘embeds’ prices for a forecasting Multi-Layer-Perceptron.

In this series of articles, we revisit classic strategies to see if we can improve them using AI. In today's article, we will examine the popular strategy of multiple time-frame analysis to judge if the strategy would be enhanced with AI.

In this series of articles, we revisit classical strategies to see if we can improve the strategy using AI. In today's article, we will examine a popular strategy of multiple symbol analysis using a basket of correlated securities, we will focus on the exotic USDZAR currency pair.

Regularization is a form of penalizing the loss function in proportion to the discrete weighting applied throughout the various layers of a neural network. We look at the significance, for some of the various regularization forms, this can have in test runs with a wizard assembled Expert Advisor.

Python is a well-known and popular programming language with many features, especially in the fields of finance, data science, Artificial Intelligence, and Machine Learning. Python is a powerful tool that can be useful in trading as well. MQL5 allows us to use this powerful language as an integration to get our objectives done effectively. In this article, we will share how we can use Python as an integration in MQL5 after learning some basic information about Python.

In this article, we create an MQL5-Telegram integrated Expert Advisor that sends moving average crossover signals to Telegram. We detail the process of generating trading signals from moving average crossovers, implementing the necessary code in MQL5, and ensuring the integration works seamlessly. The result is a system that provides real-time trading alerts directly to your Telegram group chat.

Loss Function is the key metric of machine learning algorithms that provides feedback to the training process by quantifying how well a given set of parameters are performing when compared to their intended target. We explore the various formats of this function in an MQL5 custom wizard class.

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.

This article outlines the steps to implement the Deus EA based on the RSI and Moving Average indicators for guiding automated trading.

In this article, we create an Expert Advisor (EA) in MQL5 to send messages to Telegram using a bot. We set up the necessary parameters, including the bot's API token and chat ID, and then perform an HTTP POST request to deliver the messages. Later, we handle the response to ensure successful delivery and troubleshoot any issues that arise in case of failure. This ensures we send messages from MQL5 to Telegram via the created bot.

A step-by-step guide to implementing an automated trading algorithm in MQL5 based on the Bollinger Bands trading strategy. A detailed tutorial based on creating an Expert Advisor that can be useful for traders.

In this article, we will discuss another type of models that are aimed at studying the dynamics of the environmental state.

Batch normalization is the pre-processing of data before it is fed into a machine learning algorithm, like a neural network. This is always done while being mindful of the type of Activation to be used by the algorithm. We therefore explore the different approaches that one can take in reaping the benefits of this, with the help of a wizard assembled Expert Advisor.

In this article, we will explore the development of a fully customizable Price Data export script using MQL5, marking new advancements in the simulation of the Price Man CGI Model. We have implemented advanced refinement techniques to ensure that the data is user-friendly and optimized for animation purposes. Additionally, we will uncover the capabilities of Blender 3D in effectively working with and visualizing price data, demonstrating its potential for creating dynamic and engaging animations.

In the previous parts, the Expert Advisor (EA) under development was able to use only a fixed position size for trading. This is acceptable for testing, but is not advisable when trading on a real account. Let's make it possible to trade using variable position sizes.

Unlock your potential! You're surrounded by opportunities. Discover 3 top secrets to kickstart your MQL5 journey or take it to the next level. Let's dive into discussion of tips and tricks for beginners and pros alike.

In this article, we develop a dynamic multi-symbol, multi-period RSI indicator dashboard in MQL5, providing traders real-time RSI values across various symbols and timeframes. The dashboard features interactive buttons, real-time updates, and color-coded indicators to help traders make informed decisions.

In this article, we will make an attempt to develop our own trading strategy. Any trading strategy must be based on some kind of statistical advantage. Moreover, this advantage should exist for a long time.

In previous works, we always assessed the current state of the environment. At the same time, the dynamics of changes in indicators always remained "behind the scenes". In this article I want to introduce you to an algorithm that allows you to evaluate the direct change in data between 2 successive environmental states.