![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.
![MQL5 Wizard Techniques you should know (Part 27): Moving Averages and the Angle of Attack](https://c.mql5.com/2/83/MQL5_Wizard_Techniques_you_should_know_Part_27__V2_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 27): Moving Averages and the Angle of Attack
The Angle of Attack is an often-quoted metric whose steepness is understood to strongly correlate with the strength of a prevailing trend. We look at how it is commonly used and understood and examine if there are changes that could be introduced in how it's measured for the benefit of a trade system that puts it in use.
![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.
![The Group Method of Data Handling: Implementing the Multilayered Iterative Algorithm in MQL5](https://c.mql5.com/2/74/The_Group_Method_of_Data_Handling_Implementing_the_Multilayered_Iterative_Algorithm_in_MQL5_600x314__1.jpg)
The Group Method of Data Handling: Implementing the Multilayered Iterative Algorithm in MQL5
In this article we describe the implementation of the Multilayered Iterative Algorithm of the Group Method of Data Handling in MQL5.
![Population optimization algorithms: Bird Swarm Algorithm (BSA)](https://c.mql5.com/2/74/Population_optimization_algorithms_Bird_Swarm_Algorithm_4BSA4_600x314.jpg)
Population optimization algorithms: Bird Swarm Algorithm (BSA)
The article explores the bird swarm-based algorithm (BSA) inspired by the collective flocking interactions of birds in nature. The different search strategies of individuals in BSA, including switching between flight, vigilance and foraging behavior, make this algorithm multifaceted. It uses the principles of bird flocking, communication, adaptability, leading and following to efficiently find optimal solutions.
![Developing a Replay System (Part 35): Making Adjustments (I)](https://c.mql5.com/2/60/Desenvolvendo_um_sistema_de_Replay_9Parte_355_600x314.jpg)
Developing a Replay System (Part 35): Making Adjustments (I)
Before we can move forward, we need to fix a few things. These are not actually the necessary fixes but rather improvements to the way the class is managed and used. The reason is that failures occurred due to some interaction within the system. Despite attempts to find out the cause of such failures in order to eliminate them, all these attempts were unsuccessful. Some of these cases make no sense, for example, when we use pointers or recursion in C/C++, the program crashes.
![Population optimization algorithms: Evolution Strategies, (μ,λ)-ES and (μ+λ)-ES](https://c.mql5.com/2/63/midjourney_image_13923_53_472_1_600x314.jpg)
Population optimization algorithms: Evolution Strategies, (μ,λ)-ES and (μ+λ)-ES
The article considers a group of optimization algorithms known as Evolution Strategies (ES). They are among the very first population algorithms to use evolutionary principles for finding optimal solutions. We will implement changes to the conventional ES variants and revise the test function and test stand methodology for the algorithms.
![Population optimization algorithms: Binary Genetic Algorithm (BGA). Part I](https://c.mql5.com/2/65/Population_optimization_algorithms_Binary_Genetic_Algorithm_uBGA0_600x314.jpg)
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.
![Time series clustering in causal inference](https://c.mql5.com/2/74/Time_series_clustering_in_causal_inference_600x314.jpg)
Time series clustering in causal inference
Clustering algorithms in machine learning are important unsupervised learning algorithms that can divide the original data into groups with similar observations. By using these groups, you can analyze the market for a specific cluster, search for the most stable clusters using new data, and make causal inferences. The article proposes an original method for time series clustering in Python.
![A feature selection algorithm using energy based learning in pure MQL5](https://c.mql5.com/2/78/A_feature_selection_algorithm_using_energy_based_learning_in_pure_MQL5_600x314.jpg)
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.
![Role of random number generator quality in the efficiency of optimization algorithms](https://c.mql5.com/2/73/The_role_of_the_quality_of_the_random_number_generator_600x314.jpg)
Role of random number generator quality in the efficiency of optimization algorithms
In this article, we will look at the Mersenne Twister random number generator and compare it with the standard one in MQL5. We will also find out the influence of the random number generator quality on the results of optimization algorithms.
![MQL5 Wizard Techniques you should know (Part 25): Multi-Timeframe Testing and Trading](https://c.mql5.com/2/82/MQL5_Wizard_Techniques_you_should_know_Part_25_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 25): Multi-Timeframe Testing and Trading
Strategies that are based on multiple time frames cannot be tested in wizard assembled Expert Advisors by default because of the MQL5 code architecture used in the assembly classes. We explore a possible work around this limitation for strategies that look to use multiple time frames in a case study with the quadratic moving average.
![Developing a Replay System (Part 34): Order System (III)](https://c.mql5.com/2/59/sistema_de_Replay_qParte_341_600x314.jpg)
Developing a Replay System (Part 34): Order System (III)
In this article, we will complete the first phase of construction. Although this part is fairly quick to complete, I will cover details that were not discussed previously. I will explain some points that many do not understand. Do you know why you have to press the Shift or Ctrl key?
![Causal analysis of time series using transfer entropy](https://c.mql5.com/2/86/Causal_analysis_of_time_series_using_transfer_entropy_600x314.jpg)
Causal analysis of time series using transfer entropy
In this article, we discuss how statistical causality can be applied to identify predictive variables. We will explore the link between causality and transfer entropy, as well as present MQL5 code for detecting directional transfers of information between two variables.
![Population optimization algorithms: Boids Algorithm](https://c.mql5.com/2/74/Population_optimization_algorithms_Boyd_algorithm_or_flock_algorithm-transformed_600x314.jpg)
Population optimization algorithms: Boids Algorithm
The article considers Boids algorithm based on unique examples of animal flocking behavior. In turn, the Boids algorithm serves as the basis for the creation of the whole class of algorithms united under the name "Swarm Intelligence".
![Developing a Replay System (Part 39): Paving the Path (III)](https://c.mql5.com/2/64/Desenvolvendo_um_sistema_de_Replay_iParte_39x_Pavimentando_o_Terreno_sIIIs_600x314.jpg)
Developing a Replay System (Part 39): Paving the Path (III)
Before we proceed to the second stage of development, we need to revise some ideas. Do you know how to make MQL5 do what you need? Have you ever tried to go beyond what is contained in the documentation? If not, then get ready. Because we will be doing something that most people don't normally do.
![Developing a Replay System (Part 41): Starting the second phase (II)](https://c.mql5.com/2/65/Desenvolvendo_um_sistema_de_Replay_pParte_417_600x314.jpg)
Developing a Replay System (Part 41): Starting the second phase (II)
If everything seemed right to you up to this point, it means you're not really thinking about the long term, when you start developing applications. Over time you will no longer need to program new applications, you will just have to make them work together. So let's see how to finish assembling the mouse indicator.
![Causal inference in time series classification problems](https://c.mql5.com/2/65/Causal_inference_in_time_series_classification_problems_600x314.jpg)
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.
![Pattern Recognition Using Dynamic Time Warping in MQL5](https://c.mql5.com/2/89/midjourney_image_15572_396_3823_3_600x314.jpg)
Pattern Recognition Using Dynamic Time Warping in MQL5
In this article, we discuss the concept of dynamic time warping as a means of identifying predictive patterns in financial time series. We will look into how it works as well as present its implementation in pure MQL5.
![Population optimization algorithms: Bacterial Foraging Optimization - Genetic Algorithm (BFO-GA)](https://c.mql5.com/2/64/Bacterial_Foraging_Optimization_-_Genetic_Algorithmu_BFO-GA_600x314.jpg)
Population optimization algorithms: Bacterial Foraging Optimization - Genetic Algorithm (BFO-GA)
The article presents a new approach to solving optimization problems by combining ideas from bacterial foraging optimization (BFO) algorithms and techniques used in the genetic algorithm (GA) into a hybrid BFO-GA algorithm. It uses bacterial swarming to globally search for an optimal solution and genetic operators to refine local optima. Unlike the original BFO, bacteria can now mutate and inherit genes.
![Population optimization algorithms: Bird Swarm Algorithm (BSA)](https://c.mql5.com/2/74/Population_optimization_algorithms_Bird_Swarm_Algorithm_4BSA4_600x314.jpg)
Population optimization algorithms: Bird Swarm Algorithm (BSA)
The article explores the bird swarm-based algorithm (BSA) inspired by the collective flocking interactions of birds in nature. The different search strategies of individuals in BSA, including switching between flight, vigilance and foraging behavior, make this algorithm multifaceted. It uses the principles of bird flocking, communication, adaptability, leading and following to efficiently find optimal solutions.
![Population optimization algorithms: Resistance to getting stuck in local extrema (Part I)](https://c.mql5.com/2/71/Population_optimization_algorithms_Resistance_to_getting_stuck_in_local_extrema-transformed_600x314.jpg)
Population optimization algorithms: Resistance to getting stuck in local extrema (Part I)
This article presents a unique 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. Working in this direction will provide further insight into which specific algorithms can successfully continue their search using coordinates set by the user as a starting point, and what factors influence their success.
![Integrating MQL5 with data processing packages (Part 1): Advanced Data analysis and Statistical Processing](https://c.mql5.com/2/87/Integrating_MQL5_with_data_processing_packages_Part_1_600x314.jpg)
Integrating MQL5 with data processing packages (Part 1): Advanced Data analysis and Statistical Processing
Integration enables seamless workflow where raw financial data from MQL5 can be imported into data processing packages like Jupyter Lab for advanced analysis including statistical testing.
![MQL5 Wizard Techniques you should know (Part 29): Continuation on Learning Rates with MLPs](https://c.mql5.com/2/86/MQL5_Wizard_Techniques_you_should_know_Part_29_600x314__1.jpg)
MQL5 Wizard Techniques you should know (Part 29): Continuation on Learning Rates with MLPs
We wrap up our look at learning rate sensitivity to the performance of Expert Advisors by primarily examining the Adaptive Learning Rates. These learning rates aim to be customized for each parameter in a layer during the training process and so we assess potential benefits vs the expected performance toll.
![Eigenvectors and eigenvalues: Exploratory data analysis in MetaTrader 5](https://c.mql5.com/2/83/Eigenvectors_and_eigenvalues__Exploratory_data_analysis_in_MetaTrader_600x314.jpg)
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.
![MQL5 Wizard Techniques you should know (Part 30): Spotlight on Batch-Normalization in Machine Learning](https://c.mql5.com/2/87/MQL5_Wizard_Techniques_you_should_know_Part_30____V2_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 30): Spotlight on Batch-Normalization in Machine Learning
Batch normalization is the pre-processing of data before it is fed into a machine learning algorithm, like a neural network. This is always done while being mindful of the type of Activation to be used by the algorithm. We therefore explore the different approaches that one can take in reaping the benefits of this, with the help of a wizard assembled Expert Advisor.
![MQL5 Wizard Techniques you should know (Part 33): Gaussian Process Kernels](https://c.mql5.com/2/89/midjourney_image_15615_403_3890_4_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 33): Gaussian Process Kernels
Gaussian Process Kernels are the covariance function of the Normal Distribution that could play a role in forecasting. We explore this unique algorithm in a custom signal class of MQL5 to see if it could be put to use as a prime entry and exit signal.
![Non-stationary processes and spurious regression](https://c.mql5.com/2/74/Non-stationary_processes_and_spurious_regression_600x314.jpg)
Non-stationary processes and spurious regression
The article demonstrates spurious regression occurring when attempting to apply regression analysis to non-stationary processes using Monte Carlo simulation.
![Integrating MQL5 with data processing packages (Part 2): Machine Learning and Predictive Analytics](https://c.mql5.com/2/89/midjourney_image_15578_406_3921_1_600x314.jpg)
Integrating MQL5 with data processing packages (Part 2): Machine Learning and Predictive Analytics
In our series on integrating MQL5 with data processing packages, we delve in to the powerful combination of machine learning and predictive analysis. We will explore how to seamlessly connect MQL5 with popular machine learning libraries, to enable sophisticated predictive models for financial markets.
![Developing a multi-currency Expert Advisor (Part 7): Selecting a group based on forward period](https://c.mql5.com/2/74/Developing_a_multi-currency_advisor_yPart_7c_600x314.jpg)
Developing a multi-currency Expert Advisor (Part 7): Selecting a group based on forward period
Previously, we evaluated the selection of a group of trading strategy instances, with the aim of improving the results of their joint operation, only on the same time period, in which the optimization of individual instances was carried out. Let's see what happens in the forward period.
![Creating a Trading Administrator Panel in MQL5 (Part I): Building a Messaging Interface](https://c.mql5.com/2/90/midjourney_image_15417_409_3949_4_600x314.jpg)
Creating a Trading Administrator Panel in MQL5 (Part I): Building a Messaging Interface
This article discusses the creation of a Messaging Interface for MetaTrader 5, aimed at System Administrators, to facilitate communication with other traders directly within the platform. Recent integrations of social platforms with MQL5 allow for quick signal broadcasting across different channels. Imagine being able to validate sent signals with just a click—either "YES" or "NO." Read on to learn more.
![MQL5 Wizard Techniques you should know (Part 34): Price-Embedding with an Unconventional RBM](https://c.mql5.com/2/90/midjourney_image_15652_414_4006_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 34): Price-Embedding with an Unconventional RBM
Restricted Boltzmann Machines are a form of neural network that was developed in the mid 1980s at a time when compute resources were prohibitively expensive. At its onset, it relied on Gibbs Sampling and Contrastive Divergence in order to reduce dimensionality or capture the hidden probabilities/properties over input training data sets. We examine how Backpropagation can perform similarly when the RBM ‘embeds’ prices for a forecasting Multi-Layer-Perceptron.
![Automating Trading Strategies with Parabolic SAR Trend Strategy in MQL5: Crafting an Effective Expert Advisor](https://c.mql5.com/2/90/midjourney_image_15589_412_3981_1_600x314.jpg)
Automating Trading Strategies with Parabolic SAR Trend Strategy in MQL5: Crafting an Effective Expert Advisor
In this article, we will automate the trading strategies with Parabolic SAR Strategy in MQL5: Crafting an Effective Expert Advisor. The EA will make trades based on trends identified by the Parabolic SAR indicator.