Articles on machine learning in trading

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Creating AI-based trading robots: native integration with Python, matrices and vectors, math and statistics libraries and much more.

Find out how to use machine learning in trading. Neurons, perceptrons, convolutional and recurrent networks, predictive models — start with the basics and work your way up to developing your own AI. You will learn how to train and apply neural networks for algorithmic trading in financial markets.

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Using association rules in Forex data analysis

Using association rules in Forex data analysis

How to apply predictive rules of supermarket retail analytics to the real Forex market? How are purchases of cookies, milk and bread related to stock exchange transactions? The article discusses an innovative approach to algorithmic trading based on the use of association rules.
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MQL5 Wizard Techniques you should know (Part 72): Using Patterns of MACD and the OBV with Supervised Learning

MQL5 Wizard Techniques you should know (Part 72): Using Patterns of MACD and the OBV with Supervised Learning

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.
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Automating Trading Strategies in MQL5 (Part 21): Enhancing Neural Network Trading with Adaptive Learning Rates

Automating Trading Strategies in MQL5 (Part 21): Enhancing Neural Network Trading with Adaptive Learning Rates

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
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Atomic Orbital Search (AOS) algorithm: Modification

Atomic Orbital Search (AOS) algorithm: Modification

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.
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Volumetric neural network analysis as a key to future trends

Volumetric neural network analysis as a key to future trends

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.
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Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis (Part 2)

Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis (Part 2)

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.
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Fast trading strategy tester in Python using Numba

Fast trading strategy tester in Python using Numba

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.
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MetaTrader 5 Machine Learning Blueprint (Part 1): Data Leakage and Timestamp Fixes

MetaTrader 5 Machine Learning Blueprint (Part 1): Data Leakage and Timestamp Fixes

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.
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Atomic Orbital Search (AOS) algorithm

Atomic Orbital Search (AOS) algorithm

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.
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Data Science and ML (Part 44): Forex OHLC Time series Forecasting using Vector Autoregression (VAR)

Data Science and ML (Part 44): Forex OHLC Time series Forecasting using Vector Autoregression (VAR)

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.
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MQL5 Wizard Techniques you should know (Part 71): Using Patterns of MACD and the OBV

MQL5 Wizard Techniques you should know (Part 71): Using Patterns of MACD and the OBV

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.
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Training a multilayer perceptron using the Levenberg-Marquardt algorithm

Training a multilayer perceptron using the Levenberg-Marquardt algorithm

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.
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Data Science and ML (Part 43): Hidden Patterns Detection in Indicators Data Using Latent Gaussian Mixture Models (LGMM)

Data Science and ML (Part 43): Hidden Patterns Detection in Indicators Data Using Latent Gaussian Mixture Models (LGMM)

Have you ever looked at the chart and felt that strange sensation… that there’s a pattern hidden just beneath the surface? A secret code that might reveal where prices are headed if only you could crack it? Meet LGMM, the Market’s Hidden Pattern Detector. A machine learning model that helps identify those hidden patterns in the market.
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Analyzing weather impact on currencies of agricultural countries using Python

Analyzing weather impact on currencies of agricultural countries using Python

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.
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Neural Networks in Trading: Directional Diffusion Models (DDM)

Neural Networks in Trading: Directional Diffusion Models (DDM)

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.
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Reimagining Classic Strategies (Part 13): Taking Our Crossover Strategy to New Dimensions (Part 2)

Reimagining Classic Strategies (Part 13): Taking Our Crossover Strategy to New Dimensions (Part 2)

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.
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MQL5 Wizard Techniques you should know (Part 70):  Using Patterns of SAR and the RVI with a Exponential Kernel Network

MQL5 Wizard Techniques you should know (Part 70): Using Patterns of SAR and the RVI with a Exponential Kernel Network

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.
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Build Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis

Build Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis

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.
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Neural Networks in Trading: Node-Adaptive Graph Representation with NAFS

Neural Networks in Trading: Node-Adaptive Graph Representation with NAFS

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.
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Neural Networks in Trading: Contrastive Pattern Transformer (Final Part)

Neural Networks in Trading: Contrastive Pattern Transformer (Final Part)

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.
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ALGLIB library optimization methods (Part II)

ALGLIB library optimization methods (Part II)

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.
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MQL5 Wizard Techniques you should know (Part 68):  Using Patterns of TRIX and the Williams Percent Range with a Cosine Kernel Network

MQL5 Wizard Techniques you should know (Part 68): Using Patterns of TRIX and the Williams Percent Range with a Cosine Kernel Network

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.
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Data Science and ML (Part 42): Forex Time series Forecasting using ARIMA in Python, Everything you need to Know

Data Science and ML (Part 42): Forex Time series Forecasting using ARIMA in Python, Everything you need to Know

ARIMA, short for Auto Regressive Integrated Moving Average, is a powerful traditional time series forecasting model. With the ability to detect spikes and fluctuations in a time series data, this model can make accurate predictions on the next values. In this article, we are going to understand what is it, how it operates, what you can do with it when it comes to predicting the next prices in the market with high accuracy and much more.
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Neural Networks in Trading: Contrastive Pattern Transformer

Neural Networks in Trading: Contrastive Pattern Transformer

The Contrastive Transformer is designed to analyze markets both at the level of individual candlesticks and based on entire patterns. This helps improve the quality of market trend modeling. Moreover, the use of contrastive learning to align representations of candlesticks and patterns fosters self-regulation and improves the accuracy of forecasts.
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ALGLIB library optimization methods (Part I)

ALGLIB library optimization methods (Part I)

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.
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Neural Networks in Trading: Market Analysis Using a Pattern Transformer

Neural Networks in Trading: Market Analysis Using a Pattern Transformer

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.
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Build Self Optimizing Expert Advisors in MQL5 (Part 7): Trading With Multiple Periods At Once

Build Self Optimizing Expert Advisors in MQL5 (Part 7): Trading With Multiple Periods At Once

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.
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Neural Networks in Trading: Transformer with Relative Encoding

Neural Networks in Trading: Transformer with Relative Encoding

Self-supervised learning can be an effective way to analyze large amounts of unlabeled data. The efficiency is provided by the adaptation of models to the specific features of financial markets, which helps improve the effectiveness of traditional methods. This article introduces an alternative attention mechanism that takes into account the relative dependencies and relationships between inputs.
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Neural Networks in Trading: Controlled Segmentation (Final Part)

Neural Networks in Trading: Controlled Segmentation (Final Part)

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.
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MQL5 Wizard Techniques you should know (Part 66): Using Patterns of FrAMA and the Force Index with the Dot Product Kernel

MQL5 Wizard Techniques you should know (Part 66): Using Patterns of FrAMA and the Force Index with the Dot Product Kernel

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.
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Data Science and ML (Part 41): Forex and Stock Markets Pattern Detection using YOLOv8

Data Science and ML (Part 41): Forex and Stock Markets Pattern Detection using YOLOv8

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.
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Overcoming The Limitation of Machine Learning (Part 2): Lack of Reproducibility

Overcoming The Limitation of Machine Learning (Part 2): Lack of Reproducibility

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.
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Neural Networks in Trading: Controlled Segmentation

Neural Networks in Trading: Controlled Segmentation

In this article. we will discuss a method of complex multimodal interaction analysis and feature understanding.
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Data Science and ML (Part 40): Using Fibonacci Retracements in Machine Learning data

Data Science and ML (Part 40): Using Fibonacci Retracements in Machine Learning data

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.
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Neural Networks in Trading: Generalized 3D Referring Expression Segmentation

Neural Networks in Trading: Generalized 3D Referring Expression Segmentation

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.
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Artificial Ecosystem-based Optimization (AEO) algorithm

Artificial Ecosystem-based Optimization (AEO) algorithm

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.
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Data Science and ML (Part 39): News + Artificial Intelligence, Would You Bet on it?

Data Science and ML (Part 39): News + Artificial Intelligence, Would You Bet on it?

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.
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Neural Networks in Trading: Mask-Attention-Free Approach to Price Movement Forecasting

Neural Networks in Trading: Mask-Attention-Free Approach to Price Movement Forecasting

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.
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African Buffalo Optimization (ABO)

African Buffalo Optimization (ABO)

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.
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MQL5 Wizard Techniques you should know (Part 64): Using Patterns of DeMarker and Envelope Channels with the White-Noise Kernel

MQL5 Wizard Techniques you should know (Part 64): Using Patterns of DeMarker and Envelope Channels with the White-Noise Kernel

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.