Gamuchirai Zororo Ndawana / Perfil

This article explores the powerful role of matrix factorization in algorithmic trading, specifically within MQL5 applications. From regression models to multi-target classifiers, we walk through practical examples that demonstrate how easily these techniques can be integrated using built-in MQL5 functions. Whether you're predicting price direction or modeling indicator behavior, this guide lays a strong foundation for building intelligent trading systems using matrix methods.

In this discussion, we will set the foundation for using powerful linear, algebra tools that are implemented in the MQL5 matrix and vector API. For us to make proficient use of this API, we need to have a firm understanding of the principles in linear algebra that govern intelligent use of these methods. This article aims to get the reader an intuitive level of understanding of some of the most important rules of linear algebra that we, as algorithmic traders in MQL5 need,to get started, taking advantage of this powerful library.

Factorization is a mathematical process used to gain insights into the attributes of data. When we apply factorization to large sets of market data — organized in rows and columns — we can uncover patterns and characteristics of the market. Factorization is a powerful tool, and this article will show how you can use it within the MetaTrader 5 terminal, through the MQL5 API, to gain more profound insights into your market data.

In this article, we continue our exploration of building an ensemble of trading strategies and using the MT5 genetic optimizer to tune the strategy parameters. Today, we analyzed the data in Python, showing our model could better predict which strategy would outperform, achieving higher accuracy than forecasting market returns directly. However, when we tested our application with its statistical models, our performance levels fell dismally. We subsequently discovered that the genetic optimizer unfortunately favored highly correlated strategies, prompting us to revise our method to keep vote weights fixed and focus optimization on indicator settings instead.

This article outlines the design of a double moving average crossover strategy that uses signals from a higher timeframe (D1) to guide entries on a lower timeframe (M15), with stop-loss levels calculated from an intermediate risk timeframe (H4). It introduces system constants, custom enumerations, and logic for trend-following and mean-reverting modes, while emphasizing modularity and future optimization using a genetic algorithm. The approach allows for flexible entry and exit conditions, aiming to reduce signal lag and improve trade timing by aligning lower-timeframe entries with higher-timeframe trends.

This article explores how determining the optimal number of strategies in an ensemble can be a complex task that is easier to solve through the use of the MetaTrader 5 genetic optimizer. The MQL5 Cloud is also employed as a key resource for accelerating backtesting and optimization. All in all, our discussion here sets the stage for developing statistical models to evaluate and improve trading strategies based on our initial ensemble results.

Join us for our follow-up discussion, where we will merge our first two trading strategies into an ensemble trading strategy. We shall demonstrate the different schemes possible for combining multiple strategies and also how to exercise control over the parameter space, to ensure that effective optimization remains possible even as our parameter size grows.

Join us in our discussion as we look for additional improvements to make to our moving-average cross over strategy to reduce the lag in our trading strategy to more reliable levels by leveraging our skills in data science. It is a well-studied fact that projecting your data to higher dimensions can at times improve the performance of your machine learning models. We will demonstrate what this practically means for you as a trader, and illustrate how you can weaponize this powerful principle using your MetaTrader 5 Terminal.

How best can we combine multiple strategies to create a powerful ensemble strategy? Join us in this discussion as we look to fit together three different strategies into our trading application. Traders often employ specialized strategies for opening and closing positions, and we want to know if our machines can perform this task better. For our opening discussion, we will get familiar with the faculties of the strategy tester and the principles of OOP we will need for this task.

In this series of articles, we have considered multiple different ways of identifying the best period to use our technical indicators with. Today, we shall demonstrate to the reader how they can instead perform the opposite logic, that is to say, instead of picking the single best period to use, we will demonstrate to the reader how to employ all available periods effectively. This approach reduces the amount of data discarded, and offers alternative use cases for machine learning algorithms beyond ordinary price prediction.

The article explores why trading results can differ significantly between brokers, even when using the same strategy and financial symbol, due to decentralized pricing and data discrepancies. The piece helps MQL5 developers understand why their products may receive mixed reviews on the MQL5 Marketplace, and urges developers to tailor their approaches to specific brokers to ensure transparent and reproducible outcomes. This could grow to become an important domain-bound best practice that will serve our community well if the practice were to be widely adopted.

There is a powerful and pervasive force quietly corrupting the collective efforts of our community to build reliable trading strategies that employ AI in any shape or form. This article establishes that part of the problems we face, are rooted in blind adherence to "best practices". By furnishing the reader with simple real-world market-based evidence, we will reason to the reader why we must refrain from such conduct, and rather adopt domain-bound best practices if our community should stand any chance of recovering the latent potential of AI.

High probability Setups are well known in our trading community, but regrettably they are not well-defined. In this article, we will aim to find an empirical and algorithmic way of defining exactly what is a high probability setup, identifying and exploiting them. By using Gradient Boosting Trees, we demonstrated how the reader can improve the performance of an arbitrary trading strategy and better communicate the exact job to be done to our computer in a more meaningful and explicit manner.

Dimension reduction techniques are widely used to improve the performance of machine learning models. Let us discuss a relatively new technique known as Uniform Manifold Approximation and Projection (UMAP). This new technique has been developed to explicitly overcome the limitations of legacy methods that create artifacts and distortions in the data. UMAP is a powerful dimension reduction technique, and it helps us group similar candle sticks in a novel and effective way that reduces our error rates on out of sample data and improves our trading performance.

This article explores optimizing RSI levels and periods for better trading signals. We introduce methods to estimate optimal RSI values and automate period selection using grid search and statistical models. Finally, we implement the solution in MQL5 while leveraging Python for analysis. Our approach aims to be pragmatic and straightforward to help you solve potentially complicated problems, with simplicity.

Los operadores suelen enfrentarse a pérdidas por señales falsas, mientras que esperar a la confirmación puede llevar a perder oportunidades. Este artículo presenta una estrategia comercial triangular que utiliza el precio de la plata en dólares (XAGUSD) y euros (XAGEUR), junto con el tipo de cambio EURUSD, para filtrar el ruido. Al aprovechar las relaciones entre mercados, los operadores pueden descubrir el sentimiento oculto y perfeccionar sus entradas en tiempo real.

Únase a nuestro debate de hoy, en el que buscaremos un procedimiento algorítmico para minimizar el número total de veces que nos detienen en operaciones ganadoras. El problema al que nos enfrentamos es muy complejo, y la mayoría de las soluciones que se plantean en los debates comunitarios carecen de normas establecidas y fijas. Nuestro enfoque algorítmico para resolver el problema aumentó la rentabilidad de nuestras operaciones y redujo nuestra pérdida media por operación. Sin embargo, aún quedan avances por realizar para filtrar completamente todas las operaciones que se detendrán. Nuestra solución es un buen primer paso que cualquiera puede probar.

En este artículo, hacemos nuestro segundo intento de convertir los cambios en los niveles de precios de cualquier mercado en un cambio correspondiente en el ángulo. En esta ocasión, seleccionamos un enfoque matemáticamente más sofisticado que el que elegimos en nuestro primer intento, y los resultados obtenidos sugieren que nuestro cambio de enfoque puede haber sido la decisión correcta. Únase a nosotros hoy para debatir cómo podemos utilizar las coordenadas polares para calcular el ángulo formado por los cambios en los niveles de precios, de una manera significativa, independientemente del mercado que esté analizando.

Las mejores prácticas, que definen cómo utilizar un indicador de forma segura, no siempre son fáciles de seguir. Las condiciones de mercado tranquilas pueden producir, sorprendentemente, lecturas en el indicador que no califican como señal de negociación, lo que conlleva la pérdida de oportunidades para los operadores algorítmicos. Este artículo propondrá una posible solución a este problema, al analizar cómo construir aplicaciones de negociación capaces de adaptar sus reglas de negociación a los datos de mercado disponibles.

El uso exitoso del trading algorítmico requiere un aprendizaje continuo e interdisciplinario. Sin embargo, la infinita gama de posibilidades puede consumir años de esfuerzo sin producir resultados tangibles. Para abordar esta cuestión, proponemos un marco que introduce gradualmente la complejidad, lo que permite a los operadores perfeccionar sus estrategias de forma iterativa en lugar de dedicar un tiempo indefinido a resultados inciertos.