![Developing a Replay System (Part 29): Expert Advisor project — C_Mouse class (III)](https://c.mql5.com/2/58/replay-p28_600x314.jpg)
Developing a Replay System (Part 29): Expert Advisor project — C_Mouse class (III)
After improving the C_Mouse class, we can focus on creating a class designed to create a completely new framework fr our analysis. We will not use inheritance or polymorphism to create this new class. Instead, we will change, or better said, add new objects to the price line. That's what we will do in this article. In the next one, we will look at how to change the analysis. All this will be done without changing the code of the C_Mouse class. Well, actually, it would be easier to achieve this using inheritance or polymorphism. However, there are other methods to achieve the same result.
![MQL5 Wizard Techniques you should know (Part 26): Moving Averages and the Hurst Exponent](https://c.mql5.com/2/83/MQL5_Wizard_Techniques_you_should_know_Part_26_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 26): Moving Averages and the Hurst Exponent
The Hurst Exponent is a measure of how much a time series auto-correlates over the long term. It is understood to be capturing the long-term properties of a time series and therefore carries some weight in time series analysis even outside of economic/ financial time series. We however, focus on its potential benefit to traders by examining how this metric could be paired with moving averages to build a potentially robust signal.
![The Group Method of Data Handling: Implementing the Combinatorial Algorithm in MQL5](https://c.mql5.com/2/76/The_Group_Method_of_Data_Handling_600x314.jpg)
The Group Method of Data Handling: Implementing the Combinatorial Algorithm in MQL5
In this article we continue our exploration of the Group Method of Data Handling family of algorithms, with the implementation of the Combinatorial Algorithm along with its refined incarnation, the Combinatorial Selective Algorithm in MQL5.
![Population optimization algorithms: Binary Genetic Algorithm (BGA). Part II](https://c.mql5.com/2/65/Population_optimization_algorithms__Binary_Genetic_Algorithm_dBGAf___Part_2_600x314.jpg)
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.
![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.
![Price Driven CGI Model: Theoretical Foundation](https://c.mql5.com/2/84/Price_Driven_CGI_Model___Theoretical_Foundation_600x314.jpg)
Price Driven CGI Model: Theoretical Foundation
Let's discuss the data manipulation algorithm, as we dive deeper into conceptualizing the idea of using price data to drive CGI objects. Think about transferring the effects of events, human emotions and actions on financial asset prices to a real-life model. This study delves into leveraging price data to influence the scale of a CGI object, controlling growth and emotions. These visible effects can establish a fresh analytical foundation for traders. Further insights are shared in the article.
![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 a Replay System (Part 33): Order System (II)](https://c.mql5.com/2/59/Desenvolvendo_um_sistema_de_Replay_uParte_33b_600x314.jpg)
Developing a Replay System (Part 33): Order System (II)
Today we will continue to develop the order system. As you will see, we will be massively reusing what has already been shown in other articles. Nevertheless, you will receive a small reward in this article. First, we will develop a system that can be used with a real trading server, both from a demo account or from a real one. We will make extensive use of the MetaTrader 5 platform, which will provide us with all the necessary support from the beginning.
![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.
![Developing a Replay System (Part 40): Starting the second phase (I)](https://c.mql5.com/2/64/Desenvolvendo_um_sistema_de_Replay_oParte_40r_Iniciando_a_segunda_fase__600x314.jpg)
Developing a Replay System (Part 40): Starting the second phase (I)
Today we'll talk about the new phase of the replay/simulator system. At this stage, the conversation will become truly interesting and quite rich in content. I strongly recommend that you read the article carefully and use the links provided in it. This will help you understand the content better.
![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.
![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.
![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.
![Permuting price bars in MQL5](https://c.mql5.com/2/59/Permuting_price_bars_600x314.jpg)
Permuting price bars in MQL5
In this article we present an algorithm for permuting price bars and detail how permutation tests can be used to recognize instances where strategy performance has been fabricated to deceive potential buyers of Expert Advisors.
![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!
![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.
![Developing a Replay System (Part 42): Chart Trader Project (I)](https://c.mql5.com/2/69/Desenvolvendo_um_sistema_de_Replay_bParte_421_Projeto_do_Chart_Trade_1Il__600x314.jpg)
Developing a Replay System (Part 42): Chart Trader Project (I)
Let's create something more interesting. I don't want to spoil the surprise, so follow the article for a better understanding. From the very beginning of this series on developing the replay/simulator system, I was saying that the idea is to use the MetaTrader 5 platform in the same way both in the system we are developing and in the real market. It is important that this is done properly. No one wants to train and learn to fight using one tool while having to use another one during the fight.
![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.
![Combinatorially Symmetric Cross Validation In MQL5](https://c.mql5.com/2/60/Combinatorially_Symmetric_Cross_Validation_600x314.jpg)
Combinatorially Symmetric Cross Validation In MQL5
In this article we present the implementation of Combinatorially Symmetric Cross Validation in pure MQL5, to measure the degree to which a overfitting may occure after optimizing a strategy using the slow complete algorithm of the Strategy Tester.
![Category Theory in MQL5 (Part 11): Graphs](https://c.mql5.com/2/55/Category-Theory-p11_600x314.jpg)
Category Theory in MQL5 (Part 11): Graphs
This article is a continuation in a series that look at Category Theory implementation in MQL5. In here we examine how Graph-Theory could be integrated with monoids and other data structures when developing a close-out strategy to a trading system.
![News Trading Made Easy (Part 3): Performing Trades](https://c.mql5.com/2/88/news-trading-made-easy-3_600x314.jpg)
News Trading Made Easy (Part 3): Performing Trades
In this article, our news trading expert will begin opening trades based on the economic calendar stored in our database. In addition, we will improve the expert's graphics to display more relevant information about upcoming economic calendar events.
![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.
![Developing a Replay System (Part 43): Chart Trader Project (II)](https://c.mql5.com/2/70/Desenvolvendo_um_sistema_de_Replay_Parte_43_Projeto_do_Chart_Trade__600x314.jpg)
Developing a Replay System (Part 43): Chart Trader Project (II)
Most people who want or dream of learning to program don't actually have a clue what they're doing. Their activity consists of trying to create things in a certain way. However, programming is not about tailoring suitable solutions. Doing it this way can create more problems than solutions. Here we will be doing something more advanced and therefore different.
![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.
![Implementation of the Augmented Dickey Fuller test in MQL5](https://c.mql5.com/2/64/Implementation_of_the_Augmented_Dickey_Fuller_test_in_MQL5_600x314.jpg)
Implementation of the Augmented Dickey Fuller test in MQL5
In this article we demonstrate the implementation of the Augmented Dickey-Fuller test, and apply it to conduct cointegration tests using the Engle-Granger method.
![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 32): Regularization](https://c.mql5.com/2/90/image_15576_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.
![Developing a Replay System (Part 30): Expert Advisor project — C_Mouse class (IV)](https://c.mql5.com/2/58/replay-p30_600x314.jpg)
Developing a Replay System (Part 30): Expert Advisor project — C_Mouse class (IV)
Today we will learn a technique that can help us a lot in different stages of our professional life as a programmer. Often it is not the platform itself that is limited, but the knowledge of the person who talks about the limitations. This article will tell you that with common sense and creativity you can make the MetaTrader 5 platform much more interesting and versatile without resorting to creating crazy programs or anything like that, and create simple yet safe and reliable code. We will use our creativity to modify existing code without deleting or adding a single line to the source code.
![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.
![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.
![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.
![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.