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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Experiments with neural networks (Part 5): Normalizing inputs for passing to a neural network

Neural networks are an ultimate tool in traders' toolkit. Let's check if this assumption is true. MetaTrader 5 is approached as a self-sufficient medium for using neural networks in trading. A simple explanation is provided.
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Neural networks made easy (Part 67): Using past experience to solve new tasks

In this article, we continue discussing methods for collecting data into a training set. Obviously, the learning process requires constant interaction with the environment. However, situations can be different.
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MQL5 Wizard techniques you should know (Part 03): Shannon's Entropy

Todays trader is a philomath who is almost always looking up new ideas, trying them out, choosing to modify them or discard them; an exploratory process that should cost a fair amount of diligence. These series of articles will proposition that the MQL5 wizard should be a mainstay for traders.
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Advanced resampling and selection of CatBoost models by brute-force method

This article describes one of the possible approaches to data transformation aimed at improving the generalizability of the model, and also discusses sampling and selection of CatBoost models.
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Neural networks made easy (Part 21): Variational autoencoders (VAE)

In the last article, we got acquainted with the Autoencoder algorithm. Like any other algorithm, it has its advantages and disadvantages. In its original implementation, the autoenctoder is used to separate the objects from the training sample as much as possible. This time we will talk about how to deal with some of its disadvantages.
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Category Theory in MQL5 (Part 14): Functors with Linear-Orders

This article which is part of a broader series on Category Theory implementation in MQL5, delves into Functors. We examine how a Linear Order can be mapped to a set, thanks to Functors; by considering two sets of data that one would typically dismiss as having any connection.
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Data Science and Machine Learning (Part 12): Can Self-Training Neural Networks Help You Outsmart the Stock Market?

Are you tired of constantly trying to predict the stock market? Do you wish you had a crystal ball to help you make more informed investment decisions? Self-trained neural networks might be the solution you've been looking for. In this article, we explore whether these powerful algorithms can help you "ride the wave" and outsmart the stock market. By analyzing vast amounts of data and identifying patterns, self-trained neural networks can make predictions that are often more accurate than human traders. Discover how you can use this cutting-edge technology to maximize your profits and make smarter investment decisions.
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Data Science and Machine Learning (Part 07): Polynomial Regression

Unlike linear regression, polynomial regression is a flexible model aimed to perform better at tasks the linear regression model could not handle, Let's find out how to make polynomial models in MQL5 and make something positive out of it.
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Population optimization algorithms: Harmony Search (HS)

In the current article, I will study and test the most powerful optimization algorithm - harmonic search (HS) inspired by the process of finding the perfect sound harmony. So what algorithm is now the leader in our rating?
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Data Science and Machine Learning (Part 06): Gradient Descent

The gradient descent plays a significant role in training neural networks and many machine learning algorithms. It is a quick and intelligent algorithm despite its impressive work it is still misunderstood by a lot of data scientists let's see what it is all about.
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Experiments with neural networks (Part 2): Smart neural network optimization

Experiments with neural networks (Part 2): Smart neural network optimization

In this article, I will use experimentation and non-standard approaches to develop a profitable trading system and check whether neural networks can be of any help for traders. MetaTrader 5 as a self-sufficient tool for using neural networks in trading.
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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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Neural networks made easy (Part 15): Data clustering using MQL5

Neural networks made easy (Part 15): Data clustering using MQL5

We continue to consider the clustering method. In this article, we will create a new CKmeans class to implement one of the most common k-means clustering methods. During tests, the model managed to identify about 500 patterns.
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Backpropagation Neural Networks using MQL5 Matrices

Backpropagation Neural Networks using MQL5 Matrices

The article describes the theory and practice of applying the backpropagation algorithm in MQL5 using matrices. It provides ready-made classes along with script, indicator and Expert Advisor examples.
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Neural networks made easy (Part 32): Distributed Q-Learning

Neural networks made easy (Part 32): Distributed Q-Learning

We got acquainted with the Q-learning method in one of the earlier articles within this series. This method averages rewards for each action. Two works were presented in 2017, which show greater success when studying the reward distribution function. Let's consider the possibility of using such technology to solve our problems.
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Category Theory (Part 9): Monoid-Actions

Category Theory (Part 9): Monoid-Actions

This article continues the series on category theory implementation in MQL5. Here we continue monoid-actions as a means of transforming monoids, covered in the previous article, leading to increased applications.
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Neural networks made easy (Part 25): Practicing Transfer Learning

Neural networks made easy (Part 25): Practicing Transfer Learning

In the last two articles, we developed a tool for creating and editing neural network models. Now it is time to evaluate the potential use of Transfer Learning technology using practical examples.
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Population optimization algorithms: Grey Wolf Optimizer (GWO)

Population optimization algorithms: Grey Wolf Optimizer (GWO)

Let's consider one of the newest modern optimization algorithms - Grey Wolf Optimization. The original behavior on test functions makes this algorithm one of the most interesting among the ones considered earlier. This is one of the top algorithms for use in training neural networks, smooth functions with many variables.
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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 31): Evolutionary algorithms

Neural networks made easy (Part 31): Evolutionary algorithms

In the previous article, we started exploring non-gradient optimization methods. We got acquainted with the genetic algorithm. Today, we will continue this topic and will consider another class of evolutionary algorithms.
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Developing a robot in Python and MQL5 (Part 1): Data preprocessing

Developing a robot in Python and MQL5 (Part 1): Data preprocessing

Developing a trading robot based on machine learning: A detailed guide. The first article in the series deals with collecting and preparing data and features. The project is implemented using the Python programming language and libraries, as well as the MetaTrader 5 platform.
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Data Science and Machine Learning (Part 13): Improve your financial market analysis with Principal Component Analysis (PCA)

Data Science and Machine Learning (Part 13): Improve your financial market analysis with Principal Component Analysis (PCA)

Revolutionize your financial market analysis with Principal Component Analysis (PCA)! Discover how this powerful technique can unlock hidden patterns in your data, uncover latent market trends, and optimize your investment strategies. In this article, we explore how PCA can provide a new lens for analyzing complex financial data, revealing insights that would be missed by traditional approaches. Find out how applying PCA to financial market data can give you a competitive edge and help you stay ahead of the curve
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Population optimization algorithms: Gravitational Search Algorithm (GSA)

Population optimization algorithms: Gravitational Search Algorithm (GSA)

GSA is a population optimization algorithm inspired by inanimate nature. Thanks to Newton's law of gravity implemented in the algorithm, the high reliability of modeling the interaction of physical bodies allows us to observe the enchanting dance of planetary systems and galactic clusters. In this article, I will consider one of the most interesting and original optimization algorithms. The simulator of the space objects movement is provided as well.
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Neural networks made easy (Part 49): Soft Actor-Critic

Neural networks made easy (Part 49): Soft Actor-Critic

We continue our discussion of reinforcement learning algorithms for solving continuous action space problems. In this article, I will present the Soft Actor-Critic (SAC) algorithm. The main advantage of SAC is the ability to find optimal policies that not only maximize the expected reward, but also have maximum entropy (diversity) of actions.
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Data Science and Machine Learning (Part 21): Unlocking Neural Networks, Optimization algorithms demystified

Data Science and Machine Learning (Part 21): Unlocking Neural Networks, Optimization algorithms demystified

Dive into the heart of neural networks as we demystify the optimization algorithms used inside the neural network. In this article, discover the key techniques that unlock the full potential of neural networks, propelling your models to new heights of accuracy and efficiency.
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Matrices and vectors in MQL5: Activation functions

Matrices and vectors in MQL5: Activation functions

Here we will describe only one of the aspects of machine learning - activation functions. In artificial neural networks, a neuron activation function calculates an output signal value based on the values of an input signal or a set of input signals. We will delve into the inner workings of the process.
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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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Classification models in the Scikit-Learn library and their export to ONNX

Classification models in the Scikit-Learn library and their export to ONNX

In this article, we will explore the application of all classification models available in the Scikit-Learn library to solve the classification task of Fisher's Iris dataset. We will attempt to convert these models into ONNX format and utilize the resulting models in MQL5 programs. Additionally, we will compare the accuracy of the original models with their ONNX versions on the full Iris dataset.
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Population optimization algorithms: Fish School Search (FSS)

Population optimization algorithms: Fish School Search (FSS)

Fish School Search (FSS) is a new optimization algorithm inspired by the behavior of fish in a school, most of which (up to 80%) swim in an organized community of relatives. It has been proven that fish aggregations play an important role in the efficiency of foraging and protection from predators.
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Introduction to MQL5 (Part 4): Mastering Structures, Classes, and Time Functions

Introduction to MQL5 (Part 4): Mastering Structures, Classes, and Time Functions

Unlock the secrets of MQL5 programming in our latest article! Delve into the essentials of structures, classes, and time functions, empowering your coding journey. Whether you're a beginner or an experienced developer, our guide simplifies complex concepts, providing valuable insights for mastering MQL5. Elevate your programming skills and stay ahead in the world of algorithmic trading!
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MQL5 Wizard Techniques you should know (Part 09): Pairing K-Means Clustering with Fractal Waves

MQL5 Wizard Techniques you should know (Part 09): Pairing K-Means Clustering with Fractal Waves

K-Means clustering takes the approach to grouping data points as a process that’s initially focused on the macro view of a data set that uses random generated cluster centroids before zooming in and adjusting these centroids to accurately represent the data set. We will look at this and exploit a few of its use cases.
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Neural networks made easy (Part 55): Contrastive intrinsic control (CIC)

Neural networks made easy (Part 55): Contrastive intrinsic control (CIC)

Contrastive training is an unsupervised method of training representation. Its goal is to train a model to highlight similarities and differences in data sets. In this article, we will talk about using contrastive training approaches to explore different Actor skills.
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Deep Learning GRU model with Python to ONNX  with EA, and GRU vs LSTM models

Deep Learning GRU model with Python to ONNX with EA, and GRU vs LSTM models

We will guide you through the entire process of DL with python to make a GRU ONNX model, culminating in the creation of an Expert Advisor (EA) designed for trading, and subsequently comparing GRU model with LSTM model.
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Neural networks made easy (Part 33): Quantile regression in distributed Q-learning

Neural networks made easy (Part 33): Quantile regression in distributed Q-learning

We continue studying distributed Q-learning. Today we will look at this approach from the other side. We will consider the possibility of using quantile regression to solve price prediction tasks.
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Gain An Edge Over Any Market (Part V): FRED EURUSD Alternative Data

Gain An Edge Over Any Market (Part V): FRED EURUSD Alternative Data

In today’s discussion, we used alternative Daily data from the St. Louis Federal Reserve on the Broad US-Dollar Index and a collection of other macroeconomic indicators to predict the EURUSD future exchange rate. Unfortunately, while the data appears to have almost perfect correlation, we failed to realize any material gains in our model accuracy, possibly suggesting to us that investors may be better off using ordinary market quotes instead.
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Exploring Advanced Machine Learning Techniques on the Darvas Box Breakout Strategy

Exploring Advanced Machine Learning Techniques on the Darvas Box Breakout Strategy

The Darvas Box Breakout Strategy, created by Nicolas Darvas, is a technical trading approach that spots potential buy signals when a stock’s price rises above a set "box" range, suggesting strong upward momentum. In this article, we will apply this strategy concept as an example to explore three advanced machine learning techniques. These include using a machine learning model to generate signals rather than to filter trades, employing continuous signals rather than discrete ones, and using models trained on different timeframes to confirm trades.
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Building Your First Glass-box Model Using Python And MQL5

Building Your First Glass-box Model Using Python And MQL5

Machine learning models are difficult to interpret and understanding why our models deviate from our expectations is critical if we want to gain any value from using such advanced techniques. Without comprehensive insight into the inner workings of our model, we might fail to spot bugs that are corrupting our model's performance, we may waste time over engineering features that aren't predictive and in the long run we risk underutilizing the power of these models. Fortunately, there is a sophisticated and well maintained all in one solution that allows us to see exactly what our model is doing underneath the hood.
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Experiments with neural networks (Part 3): Practical application

Experiments with neural networks (Part 3): Practical application

In this article series, I use experimentation and non-standard approaches to develop a profitable trading system and check whether neural networks can be of any help for traders. MetaTrader 5 is approached as a self-sufficient tool for using neural networks in trading.
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Filtering and feature extraction in the frequency domain

Filtering and feature extraction in the frequency domain

In this article we explore the application of digital filters on time series represented in the frequency domain so as to extract unique features that may be useful to prediction models.
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Artificial Algae Algorithm (AAA)

Artificial Algae Algorithm (AAA)

The article considers the Artificial Algae Algorithm (AAA) based on biological processes characteristic of microalgae. The algorithm includes spiral motion, evolutionary process and adaptation, which allows it to solve optimization problems. The article provides an in-depth analysis of the working principles of AAA and its potential in mathematical modeling, highlighting the connection between nature and algorithmic solutions.
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