Jonathan Pereira / Profile
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Foi em 2016 que, por um feliz acaso, me deparei com o mercado financeiro e me encantei instantaneamente. Ao descobrir a plataforma MetaTrader e sua capacidade de integrar estratégias codificadas ao mercado financeiro, soube que tinha encontrado um novo amor.
Explore meus tutoriais no GitHub e acompanhe minha jornada de crescimento e compartilhamento de conhecimento: https://github.com/jowpereira/mql5-tutoriais
Se desejar iniciar um novo projeto e aproveitar minha expertise, acesse: https://www.mql5.com/pt/job/new?prefered=14134597.
Tenho certeza de que, juntos, podemos desenvolver soluções interessantes e inspiradoras!
Conheça meu GPT - https://chat.openai.com/g/g-1DCzqDcMF-arnaldo
![Desenvolvendo um agente de Aprendizado por Reforço em MQL5 com Integração RestAPI (Parte 5): Escolhendo o Algoritmo do agente](https://c.mql5.com/2/74/Desenvolvendo_um_agente_de_Aprendizado_por_Refor4o_em_MQL5_com_Integrav6o_RestAPI_8Parte_5v___LOGO.png)
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 práticas. A seleção do algoritmo mais adequado é vista como crucial para otimizar a eficiência operacional em ambientes dinâmicos e incertos, prometendo discussões futuras sobre a implementação prática e teórica desses métodos.
![Developing an MQL5 RL agent with RestAPI integration (Part 4): Organizing functions in classes in MQL5](https://c.mql5.com/2/64/RestAPIs_em_MQL5_Logo.png)
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 show how to replace isolated functions with class methods. The article contains practical examples and tests.
Operating Principle: The "RSDForce" merges trading volume analysis and price movements to provide valuable market insights. Here's how it works: Volume and Price Analysis : The indicator examines the trading volume (quantity of traded assets) and price variations over time. Market Force Calculation : It calculates a value that reflects the market's 'force', indicating whether the price trend is strong and based on substantial trading volume. Simple Visualization : The result is displayed as a
The "ZScore Quantum Edge" is based on an advanced algorithm that combines volume analysis and price movement, providing a clear and accurate representation of market trends. Key Features: In-Depth Trend Analysis : The indicator uses a configurable period for trend analysis, allowing traders to adjust the indicator's sensitivity according to their trading strategies. Data Smoothing : With an adjustable range for data smoothing, the "ZScore Quantum Edge" offers a clearer view of the market
![Developing an MQL5 RL agent with RestAPI integration (Part 3): Creating automatic moves and test scripts in MQL5](https://c.mql5.com/2/61/RestAPI_Parte_3_-_Criando_jogadas_automuticas_e_Scripts_de_Teste_em_MQL5__LOGO.png)
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 development, integration, and hands-on testing, and concludes with the creation of a dynamic game environment and a robust integrated system.
![Developing an MQL5 RL agent with RestAPI integration (Part 2): MQL5 functions for HTTP interaction with the tic-tac-toe game REST API](https://c.mql5.com/2/61/DALLvE_2023-11-26_00.52.08_-_A_digital_artwork_illustrating_the_integration_of_MQL55_Pythono_and_Fas.png)
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 of MQL5, the simplicity of Python, and the effectiveness of FastAPI in connecting different technologies to create innovative solutions.
![Developing an MQL5 Reinforcement Learning agent with RestAPI integration (Part 1): How to use RestAPIs in MQL5](https://c.mql5.com/2/60/RestAPIs_em_MQL5_Logo.png)
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](https://c.mql5.com/2/58/implementation_regression_model_avatar.png)
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 intended for study purposes only and should not be used in real trading.
![Integrating ML models with the Strategy Tester (Part 3): Managing CSV files (II)](https://c.mql5.com/2/51/Avatar_Perceptron_Multicamadas_e_o-Algoritmo_Backpropagation_Parte_3_02.png)
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 discuss the limitations and the most important aspects of using such a class. This article ca be a valuable resource for those who want to learn how to process CSV files in MQL5.
![Multilayer perceptron and backpropagation algorithm (Part 3): Integration with the Strategy Tester - Overview (I).](https://c.mql5.com/2/51/Perceptron_Multicamadas_60x60.png)
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 this technique into the Strategy Tester. This integration will allow the use of complex data analysis aimed at making better decisions to optimize your trading strategies. In this article, we will discuss the advantages and problems of this technique.
![Multilayer perceptron and backpropagation algorithm (Part II): Implementation in Python and integration with MQL5](https://c.mql5.com/2/42/Sem_tqtulo.png)
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 to deep learning models. Let's take a look at how to set up and prepare a development environment and how to use use some of the machine learning libraries.
Tillson's T3 moving average was introduced to the world of technical analysis in the article ''A Better Moving Average'', published in the American magazine Technical Analysis of Stock Commodities. Developed by Tim Tillson, analysts and traders of futures markets soon became fascinated with this technique that smoothes the price series while decreasing the lag (lag) typical of trend-following systems
Volume is a widely used indicator in technical analysis, however there is a variation that is even more useful than Volume alone: the Moving Average of Volume. It is nothing more than a moving average applied to the popular Volume indicator. As the name says, Volume + MA serves to display the transacted volume (purchases and sales executed) of a certain financial asset at a given point of time together with the moving average of that same volume over time. What is it for? With the Volume + MA
![Multilayer perceptron and backpropagation algorithm](https://c.mql5.com/2/41/Sem_tbtulo.png)
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.
Hi-Lo is an indicator whose purpose is to more precisely assist the trends of a given asset - thus indicating the possible best time to buy or sell. What is Hi-lo? Hi-Lo is a term derived from English, where Hi is linked to the word High and Lo to the word Low. It is a trend indicator used to assess asset trading in the financial market. Therefore, its use is given to identify whether a particular asset is showing an upward or downward trend in value. In this way, Hi-Lo Activator can be
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![Jose Gustavo De Almeida Verneque Jose Gustavo De Almeida Verneque](https://c.mql5.com/avatar/2019/9/5D6F00EA-F072.jpg)
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