![Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)](https://c.mql5.com/2/82/Data_Science_and_ML_Part_25_600x314.jpg)
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.
![Population optimization algorithms: Intelligent Water Drops (IWD) algorithm](https://c.mql5.com/2/60/Intelligent_Water_Drops_IWD_600x314.jpg)
Population optimization algorithms: Intelligent Water Drops (IWD) algorithm
The article considers an interesting algorithm derived from inanimate nature - intelligent water drops (IWD) simulating the process of river bed formation. The ideas of this algorithm made it possible to significantly improve the previous leader of the rating - SDS. As usual, the new leader (modified SDSm) can be found in the attachment.
![Developing an MQL5 RL agent with RestAPI integration (Part 3): Creating automatic moves and test scripts in MQL5](https://c.mql5.com/2/61/RestAPI_Parte_3_-_Criando_jogadas_automyticas_e_Scripts_de_Teste_em_MQL5_600x314.jpg)
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.
![Introduction to MQL5 (Part 5): A Beginner's Guide to Array Functions in MQL5](https://c.mql5.com/2/73/Introduction_to_MQL5_Part_5_600x314.jpg)
Introduction to MQL5 (Part 5): A Beginner's Guide to Array Functions in MQL5
Explore the world of MQL5 arrays in Part 5, designed for absolute beginners. Simplifying complex coding concepts, this article focuses on clarity and inclusivity. Join our community of learners, where questions are embraced, and knowledge is shared!
![Population optimization algorithms: Resistance to getting stuck in local extrema (Part II)](https://c.mql5.com/2/72/Population_optimization_algorithms__Resistance___PART_II__600x314.jpg)
Population optimization algorithms: Resistance to getting stuck in local extrema (Part II)
We continue our experiment that aims to examine the behavior of population optimization algorithms in the context of their ability to efficiently escape local minima when population diversity is low and reach global maxima. Research results are provided.
![Neural Networks Made Easy (Part 81): Context-Guided Motion Analysis (CCMR)](https://c.mql5.com/2/73/Neural_networks_are_easy_Part_81_600x314.jpg)
Neural Networks Made Easy (Part 81): Context-Guided Motion Analysis (CCMR)
In previous works, we always assessed the current state of the environment. At the same time, the dynamics of changes in indicators always remained "behind the scenes". In this article I want to introduce you to an algorithm that allows you to evaluate the direct change in data between 2 successive environmental states.
![Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks](https://c.mql5.com/2/84/Data_Science_and_ML_Part_26_600x314.jpg)
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.
![Category Theory in MQL5 (Part 12): Orders](https://c.mql5.com/2/56/Category-Theory-p12_600x314.jpg)
Category Theory in MQL5 (Part 12): Orders
This article which is part of a series that follows Category Theory implementation of Graphs in MQL5, delves in Orders. We examine how concepts of Order-Theory can support monoid sets in informing trade decisions by considering two major ordering types.
![Data Science and ML (Part 29): Essential Tips for Selecting the Best Forex Data for AI Training Purposes](https://c.mql5.com/2/88/Data_Science_and_ML_Part_29_600x314.jpg)
Data Science and ML (Part 29): Essential Tips for Selecting the Best Forex Data for AI Training Purposes
In this article, we dive deep into the crucial aspects of choosing the most relevant and high-quality Forex data to enhance the performance of AI models.
![Propensity score in causal inference](https://c.mql5.com/2/72/Propensity_score_in_causal_inference___600x314.jpg)
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.
![MQL5 Wizard Techniques you should know (Part 31): Selecting the Loss Function](https://c.mql5.com/2/88/MQL5_Wizard_Techniques_you_should_know_Part_31_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 31): Selecting the Loss Function
Loss Function is the key metric of machine learning algorithms that provides feedback to the training process by quantifying how well a given set of parameters are performing when compared to their intended target. We explore the various formats of this function in an MQL5 custom wizard class.
![Overcoming ONNX Integration Challenges](https://c.mql5.com/2/75/Overcoming_ONNX_Integration_Challenges_600x314.jpg)
Overcoming ONNX Integration Challenges
ONNX is a great tool for integrating complex AI code between different platforms, it is a great tool that comes with some challenges that one must address to get the most out of it, In this article we discuss the common issues you might face and how to mitigate them.
![Data label for time series mining (Part 6):Apply and Test in EA Using ONNX](https://c.mql5.com/2/64/Data_label_for_time_series_mining_wPart_6v_Apply_and_Test_in_EA_Using_ONNX_600x314.jpg)
Data label for time series mining (Part 6):Apply and Test in EA Using ONNX
This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!
![Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state](https://c.mql5.com/2/71/Neural_networks_are_easy_Part_79_600x314.jpg)
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.
![Integrate Your Own LLM into EA (Part 3): Training Your Own LLM with CPU](https://c.mql5.com/2/79/Integrate_Your_Own_LLM_into_EA__Part_3_-_Training_Your_Own_LLM_with_CPU_600x314.jpg)
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.
![MQL5 Wizard Techniques you should know (Part 21): Testing with Economic Calendar Data](https://c.mql5.com/2/79/MQL5_Wizard_Techniques_you_should_know_Part_21___Altrenative_600x314.jpg)
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.
![Population optimization algorithms: Spiral Dynamics Optimization (SDO) algorithm](https://c.mql5.com/2/61/Spiral_Dynamics_Optimization_SDO_600x314.jpg)
Population optimization algorithms: Spiral Dynamics Optimization (SDO) algorithm
The article presents an optimization algorithm based on the patterns of constructing spiral trajectories in nature, such as mollusk shells - the spiral dynamics optimization (SDO) algorithm. I have thoroughly revised and modified the algorithm proposed by the authors. The article will consider the necessity of these changes.
![Reimagining Classic Strategies: Crude Oil](https://c.mql5.com/2/79/Reimagining_Classic_Strategies____Crude_Oil_600x314.jpg)
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.
![MQL5 Wizard Techniques you should know (14): Multi Objective Timeseries Forecasting with STF](https://c.mql5.com/2/73/MQL5_Wizard_tPart_140._Multi_Objective_Timeseries_Forecasting_with_STF_600x314.jpg)
MQL5 Wizard Techniques you should know (14): Multi Objective Timeseries Forecasting with STF
Spatial Temporal Fusion which is using both ‘space’ and time metrics in modelling data is primarily useful in remote-sensing, and a host of other visual based activities in gaining a better understanding of our surroundings. Thanks to a published paper, we take a novel approach in using it by examining its potential to traders.
![Population optimization algorithms: Artificial Multi-Social Search Objects (MSO)](https://c.mql5.com/2/69/Population_optimization_algorithms___Artificial_Multi-Social_Search_Objects_zMSO4_600x314.jpg)
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.
![Population optimization algorithms: Evolution of Social Groups (ESG)](https://c.mql5.com/2/68/Population_optimization_algorithms_Evolution_of_Social_Groups_dESG4_600x314.jpg)
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.
![Mastering Model Interpretation: Gaining Deeper Insight From Your Machine Learning Models](https://c.mql5.com/2/61/Gaining_Deeper_Insight_From_Your_Machine_Learning_Models_600x314.jpg)
Mastering Model Interpretation: Gaining Deeper Insight From Your Machine Learning Models
Machine Learning is a complex and rewarding field for anyone of any experience. In this article we dive deep into the inner mechanisms powering the models you build, we explore the intricate world of features,predictions and impactful decisions unravelling the complexities and gaining a firm grasp of model interpretation. Learn the art of navigating tradeoffs , enhancing predictions, ranking feature importance all while ensuring robust decision making. This essential read helps you clock more performance from your machine learning models and extract more value for employing machine learning methodologies.
![Population optimization algorithms: Charged System Search (CSS) algorithm](https://c.mql5.com/2/59/Charged_System_Search_CSS___white_600x314.jpg)
Population optimization algorithms: Charged System Search (CSS) 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.
![MQL5 Wizard Techniques You Should Know (Part 15): Support Vector Machines with Newton's Polynomial](https://c.mql5.com/2/75/MQL5_Wizard_Techniques_You_Should_Know_wPart_15y_600x314.jpg)
MQL5 Wizard Techniques You Should Know (Part 15): Support Vector Machines with Newton's Polynomial
Support Vector Machines classify data based on predefined classes by exploring the effects of increasing its dimensionality. It is a supervised learning method that is fairly complex given its potential to deal with multi-dimensioned data. For this article we consider how it’s very basic implementation of 2-dimensioned data can be done more efficiently with Newton’s Polynomial when classifying price-action.
![Creating Time Series Predictions using LSTM Neural Networks: Normalizing Price and Tokenizing Time](https://c.mql5.com/2/82/Creating_Time_Series_Predictions_using_LSTM_Neural_Networks_600x314.jpg)
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.
![Data Science and Machine Learning (Part 23): Why LightGBM and XGBoost outperform a lot of AI models?](https://c.mql5.com/2/79/Data_Science_and_ML_Part_23__600x314.jpg)
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.
![Spurious Regressions in Python](https://c.mql5.com/2/78/Spurious_Regressions_in_Python_600x314.jpg)
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.
![Neural networks made easy (Part 63): Unsupervised Pretraining for Decision Transformer (PDT)](https://c.mql5.com/2/60/Neural_networks_are_easy_aPart_63n_600x314.jpg)
Neural networks made easy (Part 63): Unsupervised Pretraining for Decision Transformer (PDT)
We continue to discuss the family of Decision Transformer methods. From previous article, we have already noticed that training the transformer underlying the architecture of these methods is a rather complex task and requires a large labeled dataset for training. In this article we will look at an algorithm for using unlabeled trajectories for preliminary model training.
![Data label for time series mining (Part 5):Apply and Test in EA Using Socket](https://c.mql5.com/2/64/Data_label_for_time_series_miningcPart_5c_Apply_and_Test_in_EA_Using_Socket_600x314.jpg)
Data label for time series mining (Part 5):Apply and Test in EA Using Socket
This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!
![Population optimization algorithms: Micro Artificial immune system (Micro-AIS)](https://c.mql5.com/2/64/Population_optimization_algorithms_Micro-AIS_600x314.jpg)
Population optimization algorithms: Micro Artificial immune system (Micro-AIS)
The article considers an optimization method based on the principles of the body's immune system - Micro Artificial Immune System (Micro-AIS) - a modification of AIS. Micro-AIS uses a simpler model of the immune system and simple immune information processing operations. The article also discusses the advantages and disadvantages of Micro-AIS compared to conventional AIS.
![Reimagining Classic Strategies (Part IV): SP500 and US Treasury Notes](https://c.mql5.com/2/88/midjourney_image_15531_385_3699_3_600x314__1.jpg)
Reimagining Classic Strategies (Part IV): SP500 and US Treasury Notes
In this series of articles, we analyze classical trading strategies using modern algorithms to determine whether we can improve the strategy using AI. In today's article, we revisit a classical approach for trading the SP500 using the relationship it has with US Treasury Notes.
![MQL5 Wizard Techniques you should know (Part 18): Neural Architecture Search with Eigen Vectors](https://c.mql5.com/2/77/MQL5_Wizard_Techniques_you_should_know_5Part_187_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 18): Neural Architecture Search with Eigen Vectors
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.
![Data Science and ML (Part 27): Convolutional Neural Networks (CNNs) in MetaTrader 5 Trading Bots — Are They Worth It?](https://c.mql5.com/2/84/Data_Science_and_ML_Part_27_600x314.jpg)
Data Science and ML (Part 27): Convolutional Neural Networks (CNNs) in MetaTrader 5 Trading Bots — Are They Worth It?
Convolutional Neural Networks (CNNs) are renowned for their prowess in detecting patterns in images and videos, with applications spanning diverse fields. In this article, we explore the potential of CNNs to identify valuable patterns in financial markets and generate effective trading signals for MetaTrader 5 trading bots. Let us discover how this deep machine learning technique can be leveraged for smarter trading decisions.
![MQL5 Wizard Techniques you should know (Part 10). The Unconventional RBM](https://c.mql5.com/2/64/MQL5_Wizard_Techniques_you_should_know_6Part_10i_The_Unconventional_RBM_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 10). The Unconventional RBM
Restrictive Boltzmann Machines are at the basic level, a two-layer neural network that is proficient at unsupervised classification through dimensionality reduction. We take its basic principles and examine if we were to re-design and train it unorthodoxly, we could get a useful signal filter.
![The case for using Hospital-Performance Data with Perceptrons, this Q4, in weighing SPDR XLV's next Performance](https://c.mql5.com/2/60/Insurance_Claims_Data_with_Perceptrons_600x314.jpg)
The case for using Hospital-Performance Data with Perceptrons, this Q4, in weighing SPDR XLV's next Performance
XLV is SPDR healthcare ETF and in an age where it is common to be bombarded by a wide array of traditional news items plus social media feeds, it can be pressing to select a data set for use with a model. We try to tackle this problem for this ETF by sizing up some of its critical data sets in MQL5.
![The Disagreement Problem: Diving Deeper into The Complexity Explainability in AI](https://c.mql5.com/2/72/The_Disagreement_Problem_Diving_Deeper_into_The_Complexity_Explainability_in_AI_600x314.jpg)
The Disagreement Problem: Diving Deeper into The Complexity Explainability in AI
In this article, we explore the challenge of understanding how AI works. AI models often make decisions in ways that are hard to explain, leading to what's known as the "disagreement problem". This issue is key to making AI more transparent and trustworthy.
![MQL5 Wizard Techniques you should know (Part 20): Symbolic Regression](https://c.mql5.com/2/78/MQL5_Wizard_Techniques_you_should_know_mPart_20b_600x314.jpg)
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.
![Quantization in machine learning (Part 2): Data preprocessing, table selection, training CatBoost models](https://c.mql5.com/2/59/Quantization_in_Machine_Learning_Part_2___V2_600x314.jpg)
Quantization in machine learning (Part 2): Data preprocessing, table selection, training CatBoost models
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.
![MQL5 Wizard Techniques you should know (Part 32): Regularization](https://c.mql5.com/2/89/midjourney_image_15576_395_3798_2_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 32): Regularization
Regularization is a form of penalizing the loss function in proportion to the discrete weighting applied throughout the various layers of a neural network. We look at the significance, for some of the various regularization forms, this can have in test runs with a wizard assembled Expert Advisor.
![Hybridization of population algorithms. Sequential and parallel structures](https://c.mql5.com/2/73/Hybridization_of_population_algorithms_Series_and_parallel_circuit_600x314.jpg)
Hybridization of population algorithms. Sequential and parallel structures
Here we will dive into the world of hybridization of optimization algorithms by looking at three key types: strategy mixing, sequential and parallel hybridization. We will conduct a series of experiments combining and testing relevant optimization algorithms.