Articles on machine learning in trading

All algorithmic trading strategies are difficult to set up and maintain, regardless of complexity—a challenge shared by beginners and experts alike. This article introduces an ensemble framework where supervised models and human intuition work together to overcome their shared limitations. By aligning a moving average channel strategy with a Ridge Regression model on the same indicators, we achieve centralized control, faster self-correction, and profitability from otherwise unprofitable systems.

In this article, we will study the algorithm of an artificial bee colony and supplement our knowledge with new principles of studying functional spaces. In this article, I will showcase my interpretation of the classic version of the algorithm.

We invite you to get acquainted with a new approach to detecting objects using hypernetworks. A hypernetwork generates weights for the main model, which allows taking into account the specifics of the current market situation. This approach allows us to improve forecasting accuracy by adapting the model to different trading conditions.

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.

In this piece, we look at a key data preparation step for machine learning that is gaining rapid significance. Data Preprocessing Pipelines. These in essence are a streamlined sequence of data transformation steps that prepare raw data before it is fed to a model. As uninteresting as this may initially seem to the uninducted, this ‘data standardization’ not only saves on training time and execution costs, but it goes a long way in ensuring better generalization. In this article we are focusing on some SCIKIT-LEARN preprocessing functions, and while we are not exploiting the MQL5 Wizard, we will return to it in coming articles.

CatBoost AI models have gained massive popularity recently among machine learning communities due to their predictive accuracy, efficiency, and robustness to scattered and difficult datasets. In this article, we are going to discuss in detail how to implement these types of models in an attempt to beat the forex market.

In the previous article, we introduced the DIAYN method, which offers the algorithm for learning a variety of skills. The acquired skills can be used for various tasks. But such skills can be quite unpredictable, which can make them difficult to use. In this article, we will look at an algorithm for learning predictable skills.

Can we benefit from seasonality when creating models for Deep Learning with Python? Does filtering data for the ONNX models help to get better results? What time period should we use? We will cover all of this over this article.

Convolutional Neural Networks (CNNs) are renowned for their prowess in detecting patterns in images and videos, with applications spanning diverse fields. In this article, we explore the potential of CNNs to identify valuable patterns in financial markets and generate effective trading signals for MetaTrader 5 trading bots. Let us discover how this deep machine learning technique can be leveraged for smarter trading decisions.

We continue our work on creating the FinMem framework, which uses layered memory approaches that mimic human cognitive processes. This allows the model not only to effectively process complex financial data but also to adapt to new signals, significantly improving the accuracy and effectiveness of investment decisions in dynamically changing markets.