Jonathan Pereira / Profil
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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
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.
Este artículo examina la transición de la codificación procedimental a la programación orientada a objetos (POO) en MQL5, enfocándose en la integración con REST APIs. Discutimos la organización de funciones de solicitudes HTTP (GET y POST) en clases y destacamos ventajas como el encapsulamiento, la modularidad y la facilidad de mantenimiento. La refactorización de código se detalla, y se muestra la sustitución de funciones aisladas por métodos de clases. El artículo incluye ejemplos prácticos y pruebas.
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
Este artículo explora la implementación de jugadas automáticas en el juego del tres en raya de Python, integrado con funciones de MQL5 y pruebas unitarias. El objetivo es mejorar la interactividad del juego y asegurar la robustez del sistema a través de pruebas en MQL5. La exposición cubre el desarrollo de la lógica del juego, la integración y las pruebas prácticas, y finaliza con la creación de un entorno de juego dinámico y un sistema integrado confiable.
Este artículo detalla cómo MQL5 puede interactuar con Python y FastAPI, utilizando llamadas HTTP en MQL5 para comunicarse con un juego de tres en raya en Python. En él se discute la creación de una API con FastAPI para esta integración e se incluye un script de prueba en MQL5, resaltando la versatilidad del MQL5, la simplicidad del Python y la eficiencia del FastAPI en la conexión de diferentes tecnologías para soluciones innovadoras.
Este artículo aborda la importancia de las APIs (application programming interface) en la comunicación entre diferentes aplicaciones y sistemas de software. En él, se destaca el papel de las API a la hora de simplificar la interacción entre aplicaciones, ya que les permiten compartir datos y funcionalidades de forma eficiente.
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.
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.
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.
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
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