Articles
Desenvolvendo um agente de Aprendizado por Reforço em MQL5 com Integração RestAPI (Parte 5): Escolhendo o Algoritmo do agente pour MetaTrader 5
Este capítulo da série aborda algoritmos de aprendizado por reforço, focando em Q-Learning, Deep Q-Network (DQN), e Proximal Policy Optimization (PPO). Explora como essas técnicas podem ser integradas para melhorar a automação de tarefas, detalhando suas características, vantagens, e aplicabilidades
Developing an MQL5 RL agent with RestAPI integration (Part 4): Organizing functions in classes in MQL5 pour MetaTrader 5
This article discusses the transition from procedural coding to object-oriented programming (OOP) in MQL5 with an emphasis on integration with the REST API. Today we will discuss how to organize HTTP request functions (GET and POST) into classes. We will take a closer look at code refactoring and
Developing an MQL5 RL agent with RestAPI integration (Part 3): Creating automatic moves and test scripts in MQL5 pour MetaTrader 5
This article discusses the implementation of automatic moves in the tic-tac-toe game in Python, integrated with MQL5 functions and unit tests. The goal is to improve the interactivity of the game and ensure the reliability of the system through testing in MQL5. The presentation covers game logic
Developing an MQL5 RL agent with RestAPI integration (Part 2): MQL5 functions for HTTP interaction with the tic-tac-toe game REST API pour MetaTrader 5
In this article we will talk about how MQL5 can interact with Python and FastAPI, using HTTP calls in MQL5 to interact with the tic-tac-toe game in Python. The article discusses the creation of an API using FastAPI for this integration and provides a test script in MQL5, highlighting the versatility
Developing an MQL5 Reinforcement Learning agent with RestAPI integration (Part 1): How to use RestAPIs in MQL5 pour MetaTrader 5
In this article we will talk about the importance of APIs (Application Programming Interface) for interaction between different applications and software systems. We will see the role of APIs in simplifying interactions between applications, allowing them to efficiently share data and functionality
Integrating ML models with the Strategy Tester (Conclusion): Implementing a regression model for price prediction pour MetaTrader 5
This article describes the implementation of a regression model based on a decision tree. The model should predict prices of financial assets. We have already prepared the data, trained and evaluated the model, as well as adjusted and optimized it. However, it is important to note that this model is
Integrating ML models with the Strategy Tester (Part 3): Managing CSV files (II) pour MetaTrader 5
This material provides a complete guide to creating a class in MQL5 for efficient management of CSV files. We will see the implementation of methods for opening, writing, reading, and transforming data. We will also consider how to use them to store and access information. In addition, we will
Multilayer perceptron and backpropagation algorithm (Part 3): Integration with the Strategy Tester - Overview (I). pour MetaTrader 5
The multilayer perceptron is an evolution of the simple perceptron which can solve non-linear separable problems. Together with the backpropagation algorithm, this neural network can be effectively trained. In Part 3 of the Multilayer Perceptron and Backpropagation series, we'll see how to integrate
Multilayer perceptron and backpropagation algorithm (Part II): Implementation in Python and integration with MQL5 pour MetaTrader 5
There is a Python package available for developing integrations with MQL, which enables a plethora of opportunities such as data exploration, creation and use of machine learning models. The built in Python integration in MQL5 enables the creation of various solutions, from simple linear regression
Multilayer perceptron and backpropagation algorithm pour MetaTrader 5
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