Articles on machine learning in trading

In this article, we will look at an innovative optimization method called BSO (Brain Storm Optimization) inspired by a natural phenomenon called "brainstorming". We will also discuss a new approach to solving multimodal optimization problems the BSO method applies. It allows finding multiple optimal solutions without the need to pre-determine the number of subpopulations. We will also consider the K-Means and K-Means++ clustering methods.

Self-supervised learning is a powerful paradigm of statistical learning that searches for supervisory signals generated from the observations themselves. This approach reframes challenging unsupervised learning problems into more familiar supervised ones. This technology has overlooked applications for our objective as a community of algorithmic traders. Our discussion, therefore, aims to give the reader an approachable bridge into the open research area of self-supervised learning and offers practical applications that provide robust and reliable statistical models of financial markets without overfitting to small datasets.

What is the relationship between weather and Forex? Classical economic theory has long ignored the influence of such factors as weather on market behavior. But everything has changed. Let's try to find connections between the weather conditions and the position of agricultural currencies on the market.

In this article, we present an MQL5 implementation of Stepwise Feature Selection based on the mutual information between an optimal predictor set and a target variable.

In this article, we present the implementation of several ensemble classifiers in MQL5 and discuss their efficacy in varying situations.

One of the directions for increasing the efficiency of the model training and convergence process is the improvement of optimization methods. Adam-mini is an adaptive optimization method designed to improve on the basic Adam algorithm.

In this article, we will consider another optimization algorithm inspired by inanimate nature - Charged System Search (CSS) algorithm. The purpose of this article is to present a new optimization algorithm based on the principles of physics and mechanics.

Machine learning is often viewed through statistical or linear algebraic lenses, but this article emphasizes a geometric perspective of model predictions. It demonstrates that models do not truly approximate the target but rather map it onto a new coordinate system, creating an inherent misalignment that results in irreducible error. The article proposes that multi-step predictions, comparing the model’s forecasts across different horizons, offer a more effective approach than direct comparisons with the target. By applying this method to a trading model, the article demonstrates significant improvements in profitability and accuracy without changing the underlying model.

The article considers the practical application of quantization in the construction of tree models. The methods for selecting quantum tables and data preprocessing are considered. No complex mathematical equations are used.

Support Vector Regression is an idealistic way of finding a function or ‘hyper-plane’ that best describes the relationship between two sets of data. We attempt to exploit this in time series forecasting within custom classes of the MQL5 wizard.

We wrap our look into the complementary pairing of the MA & Stochastic oscillator by examining what role inference-learning can play in a post supervised-learning & reinforcement-learning situation. There are clearly a multitude of ways one can choose to go about inference learning in this case, our approach, however, is to use variational auto encoders. We explore this in python before exporting our trained model by ONNX for use in a wizard assembled Expert Advisor in MetaTrader.

Neural Architecture Search, an automated approach at determining the ideal neural network settings can be a plus when facing many options and large test data sets. We examine how when paired Eigen Vectors this process can be made even more efficient.