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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Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks

Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks

In the previous article, we discussed a simple RNN which despite its inability to understand long-term dependencies in the data, was able to make a profitable strategy. In this article, we are discussing both the Long-Short Term Memory(LSTM) and the Gated Recurrent Unit(GRU). These two were introduced to overcome the shortcomings of a simple RNN and to outsmart it.
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SP500 Trading Strategy in MQL5 For Beginners

SP500 Trading Strategy in MQL5 For Beginners

Discover how to leverage MQL5 to forecast the S&P 500 with precision, blending in classical technical analysis for added stability and combining algorithms with time-tested principles for robust market insights.
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Using PatchTST Machine Learning Algorithm for Predicting Next 24 Hours of Price Action

Using PatchTST Machine Learning Algorithm for Predicting Next 24 Hours of Price Action

In this article, we apply a relatively complex neural network algorithm released in 2023 called PatchTST for predicting the price action for the next 24 hours. We will use the official repository, make slight modifications, train a model for EURUSD, and apply it to making future predictions both in Python and MQL5.
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Eigenvectors and eigenvalues: Exploratory data analysis in MetaTrader 5

Eigenvectors and eigenvalues: Exploratory data analysis in MetaTrader 5

In this article we explore different ways in which the eigenvectors and eigenvalues can be applied in exploratory data analysis to reveal unique relationships in data.
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Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state

Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state

In the previous article, we got acquainted with one of the methods for detecting objects in an image. However, processing a static image is somewhat different from working with dynamic time series, such as the dynamics of the prices we analyze. In this article, we will consider the method of detecting objects in video, which is somewhat closer to the problem we are solving.
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Neural networks made easy (Part 78): Decoder-free Object Detector with Transformer (DFFT)

Neural networks made easy (Part 78): Decoder-free Object Detector with Transformer (DFFT)

In this article, I propose to look at the issue of building a trading strategy from a different angle. We will not predict future price movements, but will try to build a trading system based on the analysis of historical data.
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Reimagining Classic Strategies in Python: MA Crossovers

Reimagining Classic Strategies in Python: MA Crossovers

In this article, we revisit the classic moving average crossover strategy to assess its current effectiveness. Given the amount of time since its inception, we explore the potential enhancements that AI can bring to this traditional trading strategy. By incorporating AI techniques, we aim to leverage advanced predictive capabilities to potentially optimize trade entry and exit points, adapt to varying market conditions, and enhance overall performance compared to conventional approaches.
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Neural networks made easy (Part 77): Cross-Covariance Transformer (XCiT)

Neural networks made easy (Part 77): Cross-Covariance Transformer (XCiT)

In our models, we often use various attention algorithms. And, probably, most often we use Transformers. Their main disadvantage is the resource requirement. In this article, we will consider a new algorithm that can help reduce computing costs without losing quality.
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Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)

Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)

Recurrent neural networks (RNNs) excel at leveraging past information to predict future events. Their remarkable predictive capabilities have been applied across various domains with great success. In this article, we will deploy RNN models to predict trends in the forex market, demonstrating their potential to enhance forecasting accuracy in forex trading.
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Propensity score in causal inference

Propensity score in causal inference

The article examines the topic of matching in causal inference. Matching is used to compare similar observations in a data set. This is necessary to correctly determine causal effects and get rid of bias. The author explains how this helps in building trading systems based on machine learning, which become more stable on new data they were not trained on. The propensity score plays a central role and is widely used in causal inference.
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Creating Time Series Predictions using LSTM Neural Networks: Normalizing Price and Tokenizing Time

Creating Time Series Predictions using LSTM Neural Networks: Normalizing Price and Tokenizing Time

This article outlines a simple strategy for normalizing the market data using the daily range and training a neural network to enhance market predictions. The developed models may be used in conjunction with an existing technical analysis frameworks or on a standalone basis to assist in predicting the overall market direction. The framework outlined in this article may be further refined by any technical analyst to develop models suitable for both manual and automated trading strategies.
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Integrate Your Own LLM into EA (Part 4): Training Your Own LLM with GPU

Integrate Your Own LLM into EA (Part 4): Training Your Own LLM with GPU

With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
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The base class of population algorithms as the backbone of efficient optimization

The base class of population algorithms as the backbone of efficient optimization

The article represents a unique research attempt to combine a variety of population algorithms into a single class to simplify the application of optimization methods. This approach not only opens up opportunities for the development of new algorithms, including hybrid variants, but also creates a universal basic test stand. This stand becomes a key tool for choosing the optimal algorithm depending on a specific task.
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Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models

Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models

In the forex markets It is very challenging to predict the future trend without having an idea of the past. Very few machine learning models are capable of making the future predictions by considering past values. In this article, we are going to discuss how we can use classical(Non-time series) Artificial Intelligence models to beat the market
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MQL5 Wizard Techniques you should know (Part 23): CNNs

MQL5 Wizard Techniques you should know (Part 23): CNNs

Convolutional Neural Networks are another machine learning algorithm that tend to specialize in decomposing multi-dimensioned data sets into key constituent parts. We look at how this is typically achieved and explore a possible application for traders in another MQL5 wizard signal class.
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Neural networks made easy (Part 76): Exploring diverse interaction patterns with Multi-future Transformer

Neural networks made easy (Part 76): Exploring diverse interaction patterns with Multi-future Transformer

This article continues the topic of predicting the upcoming price movement. I invite you to get acquainted with the Multi-future Transformer architecture. Its main idea is to decompose the multimodal distribution of the future into several unimodal distributions, which allows you to effectively simulate various models of interaction between agents on the scene.
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Neural networks made easy (Part 75): Improving the performance of trajectory prediction models

Neural networks made easy (Part 75): Improving the performance of trajectory prediction models

The models we create are becoming larger and more complex. This increases the costs of not only their training as well as operation. However, the time required to make a decision is often critical. In this regard, let us consider methods for optimizing model performance without loss of quality.
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Neural networks made easy (Part 74): Trajectory prediction with adaptation

Neural networks made easy (Part 74): Trajectory prediction with adaptation

This article introduces a fairly effective method of multi-agent trajectory forecasting, which is able to adapt to various environmental conditions.
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Gain An Edge Over Any Market (Part II): Forecasting Technical Indicators

Gain An Edge Over Any Market (Part II): Forecasting Technical Indicators

Did you know that we can gain more accuracy forecasting certain technical indicators than predicting the underlying price of a traded symbol? Join us to explore how to leverage this insight for better trading strategies.
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MQL5 Wizard Techniques you should know (Part 22): Conditional GANs

MQL5 Wizard Techniques you should know (Part 22): Conditional GANs

Generative Adversarial Networks are a pairing of Neural Networks that train off of each other for more accurate results. We adopt the conditional type of these networks as we look to possible application in forecasting Financial time series within an Expert Signal Class.
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Neural networks made easy (Part 73): AutoBots for predicting price movements

Neural networks made easy (Part 73): AutoBots for predicting price movements

We continue to discuss algorithms for training trajectory prediction models. In this article, we will get acquainted with a method called "AutoBots".
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Neural networks made easy (Part 72): Trajectory prediction in noisy environments

Neural networks made easy (Part 72): Trajectory prediction in noisy environments

The quality of future state predictions plays an important role in the Goal-Conditioned Predictive Coding method, which we discussed in the previous article. In this article I want to introduce you to an algorithm that can significantly improve the prediction quality in stochastic environments, such as financial markets.
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Reimagining Classic Strategies: Crude Oil

Reimagining Classic Strategies: Crude Oil

In this article, we revisit a classic crude oil trading strategy with the aim of enhancing it by leveraging supervised machine learning algorithms. We will construct a least-squares model to predict future Brent crude oil prices based on the spread between Brent and WTI crude oil prices. Our goal is to identify a leading indicator of future changes in Brent prices.
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MQL5 Wizard Techniques you should know (Part 21): Testing with Economic Calendar Data

MQL5 Wizard Techniques you should know (Part 21): Testing with Economic Calendar Data

Economic Calendar Data is not available for testing with Expert Advisors within Strategy Tester, by default. We look at how Databases could help in providing a work around this limitation. So, for this article we explore how SQLite databases can be used to archive Economic Calendar news such that wizard assembled Expert Advisors can use this to generate trade signals.
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Data Science and Machine Learning (Part 23): Why LightGBM and XGBoost outperform a lot of AI models?

Data Science and Machine Learning (Part 23): Why LightGBM and XGBoost outperform a lot of AI models?

These advanced gradient-boosted decision tree techniques offer superior performance and flexibility, making them ideal for financial modeling and algorithmic trading. Learn how to leverage these tools to optimize your trading strategies, improve predictive accuracy, and gain a competitive edge in the financial markets.
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Population optimization algorithms: Artificial Multi-Social Search Objects (MSO)

Population optimization algorithms: Artificial Multi-Social Search Objects (MSO)

This is a continuation of the previous article considering the idea of social groups. The article explores the evolution of social groups using movement and memory algorithms. The results will help to understand the evolution of social systems and apply them in optimization and search for solutions.
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Population optimization algorithms: Evolution of Social Groups (ESG)

Population optimization algorithms: Evolution of Social Groups (ESG)

We will consider the principle of constructing multi-population algorithms. As an example of this type of algorithm, we will have a look at the new custom algorithm - Evolution of Social Groups (ESG). We will analyze the basic concepts, population interaction mechanisms and advantages of this algorithm, as well as examine its performance in optimization problems.
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Neural networks made easy (Part 71): Goal-Conditioned Predictive Coding (GCPC)

Neural networks made easy (Part 71): Goal-Conditioned Predictive Coding (GCPC)

In previous articles, we discussed the Decision Transformer method and several algorithms derived from it. We experimented with different goal setting methods. During the experiments, we worked with various ways of setting goals. However, the model's study of the earlier passed trajectory always remained outside our attention. In this article. I want to introduce you to a method that fills this gap.
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Integrate Your Own LLM into EA (Part 3): Training Your Own LLM with CPU

Integrate Your Own LLM into EA (Part 3): Training Your Own LLM with CPU

With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
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Neural networks made easy (Part 70): Closed-Form Policy Improvement Operators (CFPI)

Neural networks made easy (Part 70): Closed-Form Policy Improvement Operators (CFPI)

In this article, we will get acquainted with an algorithm that uses closed-form policy improvement operators to optimize Agent actions in offline mode.
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Causal inference in time series classification problems

Causal inference in time series classification problems

In this article, we will look at the theory of causal inference using machine learning, as well as the custom approach implementation in Python. Causal inference and causal thinking have their roots in philosophy and psychology and play an important role in our understanding of reality.
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Spurious Regressions in Python

Spurious Regressions in Python

Spurious regressions occur when two time series exhibit a high degree of correlation purely by chance, leading to misleading results in regression analysis. In such cases, even though variables may appear to be related, the correlation is coincidental and the model may be unreliable.
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MQL5 Wizard Techniques you should know (Part 20): Symbolic Regression

MQL5 Wizard Techniques you should know (Part 20): Symbolic Regression

Symbolic Regression is a form of regression that starts with minimal to no assumptions on what the underlying model that maps the sets of data under study would look like. Even though it can be implemented by Bayesian Methods or Neural Networks, we look at how an implementation with Genetic Algorithms can help customize an expert signal class usable in the MQL5 wizard.
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Population optimization algorithms: Binary Genetic Algorithm (BGA). Part II

Population optimization algorithms: Binary Genetic Algorithm (BGA). Part II

In this article, we will look at the binary genetic algorithm (BGA), which models the natural processes that occur in the genetic material of living things in nature.
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Triangular arbitrage with predictions

Triangular arbitrage with predictions

This article simplifies triangular arbitrage, showing you how to use predictions and specialized software to trade currencies smarter, even if you're new to the market. Ready to trade with expertise?
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Neural networks made easy (Part 69): Density-based support constraint for the behavioral policy (SPOT)

Neural networks made easy (Part 69): Density-based support constraint for the behavioral policy (SPOT)

In offline learning, we use a fixed dataset, which limits the coverage of environmental diversity. During the learning process, our Agent can generate actions beyond this dataset. If there is no feedback from the environment, how can we be sure that the assessments of such actions are correct? Maintaining the Agent's policy within the training dataset becomes an important aspect to ensure the reliability of training. This is what we will talk about in this article.
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Developing an MQL5 RL agent with RestAPI integration (Part 3): Creating automatic moves and test scripts in MQL5

Developing an MQL5 RL agent with RestAPI integration (Part 3): Creating automatic moves and test scripts in MQL5

This article discusses the implementation of automatic moves in the tic-tac-toe game in Python, integrated with MQL5 functions and unit tests. The goal is to improve the interactivity of the game and ensure the reliability of the system through testing in MQL5. The presentation covers game logic development, integration, and hands-on testing, and concludes with the creation of a dynamic game environment and a robust integrated system.
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Population optimization algorithms: Binary Genetic Algorithm (BGA). Part I

Population optimization algorithms: Binary Genetic Algorithm (BGA). Part I

In this article, we will explore various methods used in binary genetic and other population algorithms. We will look at the main components of the algorithm, such as selection, crossover and mutation, and their impact on the optimization. In addition, we will study data presentation methods and their impact on optimization results.
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Statistical Arbitrage with predictions

Statistical Arbitrage with predictions

We will walk around statistical arbitrage, we will search with python for correlation and cointegration symbols, we will make an indicator for Pearson's coefficient and we will make an EA for trading statistical arbitrage with predictions done with python and ONNX models.
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A feature selection algorithm using energy based learning in pure MQL5

A feature selection algorithm using energy based learning in pure MQL5

In this article we present the implementation of a feature selection algorithm described in an academic paper titled,"FREL: A stable feature selection algorithm", called Feature weighting as regularized energy based learning.