Jonathan Pereira / 个人资料
- 信息
3 年
经验
|
5
产品
|
5
演示版
|
57
工作
|
1
信号
|
0
订阅者
|
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.
В этой статье расскажем о том, как MQL5 может взаимодействовать с Python и FastAPI, используя HTTP-вызовы в MQL5 для взаимодействия с игрой "крестики-нолики" на Python. В статье рассматривается создание API с помощью FastAPI для этой интеграции и приводится тестовый скрипт на MQL5, подчеркивающий универсальность MQL5, простоту Python и эффективность FastAPI в соединении различных технологий для создания инновационных решений.
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.
本文描述了一个基于决策树的回归模型的实现。该模型应预测金融资产的价格。我们已经准备好了数据,对模型进行了训练和评估,并对其进行了调整和优化。然而,需要注意的是,该模型仅用于研究目的,不应用于实际交易。
这篇资料提供了以 MQL5 创建类,从而高效管理 CSV 文件的完整指南。 我们将看到打开、写入、读取、和转换数据等方法的实现。 我们还将研究如何使用它们来存储和访问信息。 此外,我们将讨论使用该类的限制和最重要的方面。 本文对于那些想要学习如何在 MQL5 中处理 CSV 文件的人来说是一个宝贵的资源。
多层感知器是简单感知器的演变,可以解决非线性可分离问题。 结合反向传播算法,可以有效地训练该神经网络。 在多层感知器和反向传播系列的第 3 部分当中,我们将见识到如何将此技术集成到策略测试器之中。 这种集成将允许使用复杂的数据分析,旨在制定更好的决策,从而优化您的交易策略。 在本文中,我们将讨论这种技术的优点和问题。
有一个 Python 程序包可用于开发与 MQL 的集成,它提供了大量机会,例如数据探索、创建和使用机器学习模型。 集成在 MQL5 内置的 Python,能够创建各种解决方案,从简单的线性回归、到深度学习模型。 我们来看看如何设置和准备开发环境,以及如何使用一些机器学习函数库。
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
这两种方法的普及性日益增加,因此在 Matlab、R、Python、C++ 等领域开发了大量的库,它们接收到一个训练集作为输入,并自动为问题创建合适的网络。让我们试着理解基本的神经网络类型是如何工作的(包括单神经元感知机和多层感知机)。我们将探讨一个令人兴奋的算法,它负责网络训练 - 梯度下降和反向传播。现有的复杂模型往往基于这样简单的网络模型。
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