Articles on machine learning in trading

We follow up on our last article, where we introduced the indicator pair of the MACD and the OBV, by looking at how this pairing could be enhanced with Machine Learning. MACD and OBV are a trend and volume complimentary pairing. Our machine learning approach uses a convolution neural network that engages the Exponential kernel in sizing its kernels and channels, when fine-tuning the forecasts of this indicator pairing. As always, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.

In this article, we enhance a neural network trading strategy in MQL5 with an adaptive learning rate to boost accuracy. We design and implement this mechanism, then test its performance. The article concludes with optimization insights for algorithmic trading

In the second part of the article, we will continue developing a modified version of the AOS (Atomic Orbital Search) algorithm focusing on specific operators to improve its efficiency and adaptability. After analyzing the fundamentals and mechanics of the algorithm, we will discuss ideas for improving its performance and the ability to analyze complex solution spaces, proposing new approaches to extend its functionality as an optimization tool.

The article explores the possibility of improving price forecasting based on trading volume analysis by integrating technical analysis principles with LSTM neural network architecture. Particular attention is paid to the detection and interpretation of anomalous volumes, the use of clustering and the creation of features based on volumes and their definition in the context of machine learning.

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.

The article implements a fast strategy tester for machine learning models using Numba. It is 50 times faster than the pure Python strategy tester. The author recommends using this library to speed up mathematical calculations, especially the ones involving loops.

Before we can even begin to make use of ML in our trading on MetaTrader 5, it’s crucial to address one of the most overlooked pitfalls—data leakage. This article unpacks how data leakage, particularly the MetaTrader 5 timestamp trap, can distort our model's performance and lead to unreliable trading signals. By diving into the mechanics of this issue and presenting strategies to prevent it, we pave the way for building robust machine learning models that deliver trustworthy predictions in live trading environments.

The article considers the Atomic Orbital Search (AOS) algorithm, which uses the concepts of the atomic orbital model to simulate the search for solutions. The algorithm is based on probability distributions and the dynamics of interactions in the atom. The article discusses in detail the mathematical aspects of AOS, including updating the positions of candidate solutions and the mechanisms of energy absorption and release. AOS opens new horizons for applying quantum principles to computing problems by offering an innovative approach to optimization.

Explore how Vector Autoregression (VAR) models can forecast Forex OHLC (Open, High, Low, and Close) time series data. This article covers VAR implementation, model training, and real-time forecasting in MetaTrader 5, helping traders analyze interdependent currency movements and improve their trading strategies.

The Moving-Average-Convergence-Divergence (MACD) oscillator and the On-Balance-Volume (OBV) oscillator are another pair of indicators that could be used in conjunction within an MQL5 Expert Advisor. This pairing, as is practice in these article series, is complementary with the MACD affirming trends while OBV checks volume. As usual, we use the MQL5 wizard to build and test any potential these two may possess.

The article presents an implementation of the Levenberg-Marquardt algorithm for training feedforward neural networks. A comparative analysis of performance with algorithms from the scikit-learn Python library has been conducted. Simpler learning methods, such as gradient descent, gradient descent with momentum, and stochastic gradient descent are preliminarily discussed.

In this article, we discuss Directional Diffusion Models that exploit data-dependent anisotropic and directed noise in a forward diffusion process to capture meaningful graph representations.

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.

We follow up our last article, where we introduced the indicator pair of the SAR and the RVI, by considering how this indicator pairing could be extended with Machine Learning. SAR and RVI are a trend and momentum complimentary pairing. Our machine learning approach uses a convolution neural network that engages the Exponential kernel in sizing its kernels and channels, when fine-tuning the forecasts of this indicator pairing. As always, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.

We invite you to get acquainted with the NAFS (Node-Adaptive Feature Smoothing) method, which is a non-parametric approach to creating node representations that does not require parameter training. NAFS extracts features of each node given its neighbors and then adaptively combines these features to form a final representation.

In the previous last article within this series, we looked at the Atom-Motif Contrastive Transformer (AMCT) framework, which uses contrastive learning to discover key patterns at all levels, from basic elements to complex structures. In this article, we continue implementing AMCT approaches using MQL5.

In this article, we will continue to study the remaining optimization methods from the ALGLIB library, paying special attention to their testing on complex multidimensional functions. This will allow us not only to evaluate the efficiency of each algorithm, but also to identify their strengths and weaknesses in different conditions.

We follow up our last article, where we introduced the indicator pair of TRIX and Williams Percent Range, by considering how this indicator pairing could be extended with Machine Learning. TRIX and William’s Percent are a trend and support/ resistance complimentary pairing. Our machine learning approach uses a convolution neural network that engages the cosine kernel in its architecture when fine-tuning the forecasts of this indicator pairing. As always, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.

In this article, we will get acquainted with the ALGLIB library optimization methods for MQL5. The article includes simple and clear examples of using ALGLIB to solve optimization problems, which will make mastering the methods as accessible as possible. We will take a detailed look at the connection of such algorithms as BLEIC, L-BFGS and NS, and use them to solve a simple test problem.

When we use models to analyze the market situation, we mainly focus on the candlestick. However, it has long been known that candlestick patterns can help in predicting future price movements. In this article, we will get acquainted with a method that allows us to integrate both of these approaches.

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.

We continue the work started in the previous article on building the RefMask3D framework using MQL5. This framework is designed to comprehensively study multimodal interaction and feature analysis in a point cloud, followed by target object identification based on a description provided in natural language.

The FrAMA Indicator and the Force Index Oscillator are trend and volume tools that could be paired when developing an Expert Advisor. We continue from our last article that introduced this pair by considering machine learning applicability to the pair. We are using a convolution neural network that uses the dot-product kernel in making forecasts with these indicators’ inputs. This is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.

Detecting patterns in financial markets is challenging because it involves seeing what's on the chart, something that's difficult to undertake in MQL5 due to image limitations. In this article, we are going to discuss a decent model made in Python that helps us detect patterns present on the chart with minimal effort.

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.

In this article. we will discuss a method of complex multimodal interaction analysis and feature understanding.

Fibonacci retracements are a popular tool in technical analysis, helping traders identify potential reversal zones. In this article, we’ll explore how these retracement levels can be transformed into target variables for machine learning models to help them understand the market better using this powerful tool.

While analyzing the market situation, we divide it into separate segments, identifying key trends. However, traditional analysis methods often focus on one aspect and thus limit the proper perception. In this article, we will learn about a method that enables the selection of multiple objects to ensure a more comprehensive and multi-layered understanding of the situation.

The article considers a metaheuristic Artificial Ecosystem-based Optimization (AEO) algorithm, which simulates interactions between ecosystem components by creating an initial population of solutions and applying adaptive update strategies, and describes in detail the stages of AEO operation, including the consumption and decomposition phases, as well as different agent behavior strategies. The article introduces the features and advantages of this algorithm.

News drives the financial markets, especially major releases like Non-Farm Payrolls (NFPs). We've all witnessed how a single headline can trigger sharp price movements. In this article, we dive into the powerful intersection of news data and Artificial Intelligence.

In this article, we will discuss the Mask-Attention-Free Transformer (MAFT) method and its application in the field of trading. Unlike traditional Transformers that require data masking when processing sequences, MAFT optimizes the attention process by eliminating the need for masking, significantly improving computational efficiency.

The article presents the African Buffalo Optimization (ABO) algorithm, a metaheuristic approach developed in 2015 based on the unique behavior of these animals. The article describes in detail the stages of the algorithm implementation and its efficiency in finding solutions to complex problems, which makes it a valuable tool in the field of optimization.

The DeMarker Oscillator and the Envelopes' indicator are momentum and support/ resistance tools that can be paired when developing an Expert Advisor. We continue from our last article that introduced these pair of indicators by adding machine learning to the mix. We are using a recurrent neural network that uses the white-noise kernel to process vectorized signals from these two indicators. This is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.