![Interview with Alexandr Artapov (ATC 2012)](https://c.mql5.com/2/0/avatar__21.png)
![Interview with Alexandr Artapov (ATC 2012)](https://c.mql5.com/i/articles/overlay.png)
Interview with Alexandr Artapov (ATC 2012)
It was during the second week of the Championship when the Expert Advisor of Alexandr Artapov (artall) found itself on the third position trading EURUSD and EURJPY. Then it briefly left TOP-10 to appear again after one month of struggle for survival. As it turned out, this trading robot is still having something up its sleeve.
![DoEasy. Controls (Part 32): Horizontal ScrollBar, mouse wheel scrolling](https://c.mql5.com/2/55/MQL5-avatar-doeasy-library-2.png)
![DoEasy. Controls (Part 32): Horizontal ScrollBar, mouse wheel scrolling](https://c.mql5.com/i/articles/overlay.png)
DoEasy. Controls (Part 32): Horizontal ScrollBar, mouse wheel scrolling
In the article, we will complete the development of the horizontal scrollbar object functionality. We will also make it possible to scroll the contents of the container by moving the scrollbar slider and rotating the mouse wheel, as well as make additions to the library, taking into account the new order execution policy and new runtime error codes in MQL5.
![Category Theory in MQL5 (Part 15) : Functors with Graphs](https://c.mql5.com/2/57/Category-Theory-p15_600x314.jpg)
Category Theory in MQL5 (Part 15) : Functors with Graphs
This article on Category Theory implementation in MQL5, continues the series by looking at Functors but this time as a bridge between Graphs and a set. We revisit calendar data, and despite its limitations in Strategy Tester use, make the case using functors in forecasting volatility with the help of correlation.
![Neural networks made easy (Part 17): Dimensionality reduction](https://c.mql5.com/2/49/Neural_networks_made_easy_007_600x314.jpg)
Neural networks made easy (Part 17): Dimensionality reduction
In this part we continue discussing Artificial Intelligence models. Namely, we study unsupervised learning algorithms. We have already discussed one of the clustering algorithms. In this article, I am sharing a variant of solving problems related to dimensionality reduction.
![Discrete Hartley transform](https://c.mql5.com/2/57/discrete_hartley_transform_600x314.jpg)
Discrete Hartley transform
In this article, we will consider one of the methods of spectral analysis and signal processing - the discrete Hartley transform. It allows filtering signals, analyzing their spectrum and much more. The capabilities of DHT are no less than those of the discrete Fourier transform. However, unlike DFT, DHT uses only real numbers, which makes it more convenient for implementation in practice, and the results of its application are more visual.
![Neural networks made easy (Part 58): Decision Transformer (DT)](https://c.mql5.com/2/58/decision-transformer_600x314.jpg)
Neural networks made easy (Part 58): Decision Transformer (DT)
We continue to explore reinforcement learning methods. In this article, I will focus on a slightly different algorithm that considers the Agent’s policy in the paradigm of constructing a sequence of actions.
![Experiments with neural networks (Part 7): Passing indicators](https://c.mql5.com/2/59/Experiments_with_neural_networks_7_600x314.jpg)
Experiments with neural networks (Part 7): Passing indicators
Examples of passing indicators to a perceptron. The article describes general concepts and showcases the simplest ready-made Expert Advisor followed by the results of its optimization and forward test.
![Master MQL5 from beginner to pro (Part II): Basic data types and use of variable](https://c.mql5.com/2/64/Learning_MQL5_-_from_beginner_to_pro_mPart_II6_600x314.jpg)
Master MQL5 from beginner to pro (Part II): Basic data types and use of variable
This is a continuation of the series for beginners. In this article, we'll look at how to create constants and variables, write dates, colors, and other useful data. We will learn how to create enumerations like days of the week or line styles (solid, dotted, etc.). Variables and expressions are the basis of programming. They are definitely present in 99% of programs, so understanding them is critical. Therefore, if you are new to programming, this article can be very useful for you. Required programming knowledge level: very basic, within the limits of my previous article (see the link at the beginning).
![Graphics in DoEasy library (Part 99): Moving an extended graphical object using a single control point](https://c.mql5.com/2/49/doeasy_099_600x314.jpg)
Graphics in DoEasy library (Part 99): Moving an extended graphical object using a single control point
In the previous article, I implemented the ability to move pivot points of an extended graphical object using control forms. Now I am going to implement the ability to move a composite graphical object using a single graphical object control point (form).
![DoEasy. Controls (Part 23): Improving TabControl and SplitContainer WinForms objects](https://c.mql5.com/2/50/Do_Easy_Part_23_600x314.jpg)
DoEasy. Controls (Part 23): Improving TabControl and SplitContainer WinForms objects
In this article, I will add new mouse events relative to the boundaries of the working areas of WinForms objects and fix some shortcomings in the functioning of the TabControl and SplitContainer controls.
![Interview with Ruslan Ziyatdinov (ATC 2012)](https://c.mql5.com/2/0/rusland1962_avatarb19.png)
![Interview with Ruslan Ziyatdinov (ATC 2012)](https://c.mql5.com/i/articles/overlay.png)
Interview with Ruslan Ziyatdinov (ATC 2012)
The Championship keeps providing us with new discoveries, as well as new interesting Participants and unusual ideas implemented in the competition trading robots. While interviewing Ruslan Ziyatdinov (rusland1962), we learned about his simple approach to trading and found out why it is better to trade less frequently.
![Population optimization algorithms: Monkey algorithm (MA)](https://c.mql5.com/2/52/monkey_600x314.jpg)
Population optimization algorithms: Monkey algorithm (MA)
In this article, I will consider the Monkey Algorithm (MA) optimization algorithm. The ability of these animals to overcome difficult obstacles and get to the most inaccessible tree tops formed the basis of the idea of the MA algorithm.
![DoEasy. Controls (Part 26): Finalizing the ToolTip WinForms object and moving on to ProgressBar development](https://c.mql5.com/2/50/doeasy_026_600x314.jpg)
DoEasy. Controls (Part 26): Finalizing the ToolTip WinForms object and moving on to ProgressBar development
In this article, I will complete the development of the ToolTip control and start the development of the ProgressBar WinForms object. While working on objects, I will develop universal functionality for animating controls and their components.
![DoEasy. Controls (Part 29): ScrollBar auxiliary control](https://c.mql5.com/2/50/DoEasy_part_29_600x314.jpg)
DoEasy. Controls (Part 29): ScrollBar auxiliary control
In this article, I will start developing the ScrollBar auxiliary control element and its derivative objects — vertical and horizontal scrollbars. A scrollbar is used to scroll the content of the form if it goes beyond the container. Scrollbars are usually located at the bottom and to the right of the form. The horizontal one at the bottom scrolls content left and right, while the vertical one scrolls up and down.
![Custom Indicators (Part 1): A Step-by-Step Introductory Guide to Developing Simple Custom Indicators in MQL5](https://c.mql5.com/2/76/Custom_Indicators_hPart_1q_600x314.jpg)
Custom Indicators (Part 1): A Step-by-Step Introductory Guide to Developing Simple Custom Indicators in MQL5
Learn how to create custom indicators using MQL5. This introductory article will guide you through the fundamentals of building simple custom indicators and demonstrate a hands-on approach to coding different custom indicators for any MQL5 programmer new to this interesting topic.
![Timeseries in DoEasy library (part 50): Multi-period multi-symbol standard indicators with a shift](https://c.mql5.com/2/49/doeasy_050_600x314.jpg)
Timeseries in DoEasy library (part 50): Multi-period multi-symbol standard indicators with a shift
In the article, let’s improve library methods for correct display of multi-symbol multi-period standard indicators, which lines are displayed on the current symbol chart with a shift set in the settings. As well, let’s put things in order in methods of work with standard indicators and remove the redundant code to the library area in the final indicator program.
![Neural networks made easy (Part 53): Reward decomposition](https://c.mql5.com/2/57/decomposition_of_remuneration_053_600x314.jpg)
Neural networks made easy (Part 53): Reward decomposition
We have already talked more than once about the importance of correctly selecting the reward function, which we use to stimulate the desired behavior of the Agent by adding rewards or penalties for individual actions. But the question remains open about the decryption of our signals by the Agent. In this article, we will talk about reward decomposition in terms of transmitting individual signals to the trained Agent.
![Developing a Replay System — Market simulation (Part 05): Adding Previews](https://c.mql5.com/2/53/replay-p5_600x314.jpg)
Developing a Replay System — Market simulation (Part 05): Adding Previews
We have managed to develop a way to implement the market replay system in a realistic and accessible way. Now let's continue our project and add data to improve the replay behavior.
![Graphics in DoEasy library (Part 98): Moving pivot points of extended standard graphical objects](https://c.mql5.com/2/49/doeasy_098_600x314.jpg)
Graphics in DoEasy library (Part 98): Moving pivot points of extended standard graphical objects
In the article, I continue the development of extended standard graphical objects and create the functionality for moving pivot points of composite graphical objects using the control points for managing the coordinates of the graphical object pivot points.
![Graphical Interfaces X: Updates for the Rendered table and code optimization (build 10)](https://c.mql5.com/2/26/MQL5-avatar-X-Auto-table-001.png)
![Graphical Interfaces X: Updates for the Rendered table and code optimization (build 10)](https://c.mql5.com/i/articles/overlay.png)
Graphical Interfaces X: Updates for the Rendered table and code optimization (build 10)
We continue to complement the Rendered table (CCanvasTable) with new features. The table will now have: highlighting of the rows when hovered; ability to add an array of icons for each cell and a method for switching them; ability to set or modify the cell text during the runtime, and more.
![Neural networks made easy (Part 73): AutoBots for predicting price movements](https://c.mql5.com/2/64/Neural_networks_are_easy_8Part_73g__AutoBots_for_predicting_price_movement_600x314.jpg)
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".
![Using optimization algorithms to configure EA parameters on the fly](https://c.mql5.com/2/69/Using_optimization_algorithms_to_configure_EA_parameters_on_the_fly_600x314.jpg)
Using optimization algorithms to configure EA parameters on the fly
The article discusses the practical aspects of using optimization algorithms to find the best EA parameters on the fly, as well as virtualization of trading operations and EA logic. The article can be used as an instruction for implementing optimization algorithms into an EA.
![Neural networks made easy (Part 22): Unsupervised learning of recurrent models](https://c.mql5.com/2/49/Neural_Networks_Easy_014_600x314.jpg)
Neural networks made easy (Part 22): Unsupervised learning of recurrent models
We continue to study unsupervised learning algorithms. This time I suggest that we discuss the features of autoencoders when applied to recurrent model training.
![Data label for timeseries mining (Part 2):Make datasets with trend markers using Python](https://c.mql5.com/2/58/Make_datasets_with_trend_markers_using_Python_600x314.jpg)
Data label for timeseries mining (Part 2):Make datasets with trend markers using Python
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!
![Developing an MQL5 RL agent with RestAPI integration (Part 2): MQL5 functions for HTTP interaction with the tic-tac-toe game REST API](https://c.mql5.com/2/61/DALLwE_2023-11-27_16.08.45_-_A_minimalistic_illustration_showing_the_concept_of_system_integration_u.jpg)
Developing an MQL5 RL agent with RestAPI integration (Part 2): MQL5 functions for HTTP interaction with the tic-tac-toe game REST API
In this article we will talk about how MQL5 can interact with Python and FastAPI, using HTTP calls in MQL5 to interact with the tic-tac-toe game in Python. The article discusses the creation of an API using FastAPI for this integration and provides a test script in MQL5, highlighting the versatility of MQL5, the simplicity of Python, and the effectiveness of FastAPI in connecting different technologies to create innovative solutions.
![Wrapping ONNX models in classes](https://c.mql5.com/2/54/ONNX_Models_in_the_Class_600x314.jpg)
Wrapping ONNX models in classes
Object-oriented programming enables creation of a more compact code that is easy to read and modify. Here we will have a look at the example for three ONNX models.
![Population optimization algorithms: Artificial Bee Colony (ABC)](https://c.mql5.com/2/50/artificial_bee_colony_600x314.jpg)
Population optimization algorithms: Artificial Bee Colony (ABC)
In this article, we will study the algorithm of an artificial bee colony and supplement our knowledge with new principles of studying functional spaces. In this article, I will showcase my interpretation of the classic version of the algorithm.
![Interview with Alexander Prishchenko (ATC 2012)](https://c.mql5.com/2/0/avatar_Crucianc1a.png)
![Interview with Alexander Prishchenko (ATC 2012)](https://c.mql5.com/i/articles/overlay.png)
Interview with Alexander Prishchenko (ATC 2012)
What can be more complicated than a multicurrency trading robot? Surely, it is an automated strategy based on Elliott Wave Principle. Can we imagine something more complicated than that? Yes, we can. It is a multicurrency Expert Advisor using Elliott Waves on each currency pair! Alexander Prishchenko (Crucian) believes that even a newcomer can learn the rules.
![Category Theory in MQL5 (Part 3)](https://c.mql5.com/2/52/Category-Theory-part-3_600x314.jpg)
Category Theory in MQL5 (Part 3)
Category Theory is a diverse and expanding branch of Mathematics which as of yet is relatively uncovered in the MQL5 community. These series of articles look to introduce and examine some of its concepts with the overall goal of establishing an open library that provides insight while hopefully furthering the use of this remarkable field in Traders' strategy development.
![Automated Parameter Optimization for Trading Strategies Using Python and MQL5](https://c.mql5.com/2/82/Automated_Parameter_Optimization_for_Trading_Strategies_Using_Python_and_MQL5___2_600x314.jpg)
Automated Parameter Optimization for Trading Strategies Using Python and MQL5
There are several types of algorithms for self-optimization of trading strategies and parameters. These algorithms are used to automatically improve trading strategies based on historical and current market data. In this article we will look at one of them with python and MQL5 examples.
![Data Science and Machine Learning (Part 22): Leveraging Autoencoders Neural Networks for Smarter Trades by Moving from Noise to Signal](https://c.mql5.com/2/77/Data_Science_and_ML_9Part_22t_600x314.jpg)
Data Science and Machine Learning (Part 22): Leveraging Autoencoders Neural Networks for Smarter Trades by Moving from Noise to Signal
In the fast-paced world of financial markets, separating meaningful signals from the noise is crucial for successful trading. By employing sophisticated neural network architectures, autoencoders excel at uncovering hidden patterns within market data, transforming noisy input into actionable insights. In this article, we explore how autoencoders are revolutionizing trading practices, offering traders a powerful tool to enhance decision-making and gain a competitive edge in today's dynamic markets.
![How to build and optimize a volatility-based trading system (Chaikin Volatility - CHV)](https://c.mql5.com/2/76/How_to_build_and_optimize_a_volatility-based_trading_system_cChaikin_Volatility_-_CHVq_600x314.jpg)
How to build and optimize a volatility-based trading system (Chaikin Volatility - CHV)
In this article, we will provide another volatility-based indicator named Chaikin Volatility. We will understand how to build a custom indicator after identifying how it can be used and constructed. We will share some simple strategies that can be used and then test them to understand which one can be better.
![Interview with Atsushi Yamanaka (ATC 2011)](https://c.mql5.com/2/0/alohafx_avatar.png)
![Interview with Atsushi Yamanaka (ATC 2011)](https://c.mql5.com/i/articles/overlay.png)
Interview with Atsushi Yamanaka (ATC 2011)
What is common between skydiving, Futures, Hawaii, translations and spies? We didn't know it until we've managed to communicate with disqualified participant Atsushi Yamanaka (alohafx). His has a creed "Life is Good!", and one can hardly doubt that. It was interesting to know that distances between the continents are not an obstacle for communication among our Championship's participants.
![Ready-made templates for including indicators to Expert Advisors (Part 1): Oscillators](https://c.mql5.com/2/57/ready_made_templates_for_connecting_indicators_001_600x314.jpg)
Ready-made templates for including indicators to Expert Advisors (Part 1): Oscillators
The article considers standard indicators from the oscillator category. We will create ready-to-use templates for their use in EAs - declaring and setting parameters, indicator initialization and deinitialization, as well as receiving data and signals from indicator buffers in EAs.
![Implementing the Generalized Hurst Exponent and the Variance Ratio test in MQL5](https://c.mql5.com/2/69/Implementing_the_Generalized_Hurst_Exponent_and_the_Variance_Ratio_test_in_MQL5_600x314__3.jpg)
Implementing the Generalized Hurst Exponent and the Variance Ratio test in MQL5
In this article, we investigate how the Generalized Hurst Exponent and the Variance Ratio test can be utilized to analyze the behaviour of price series in MQL5.
![Data Science and Machine Learning(Part 14): Finding Your Way in the Markets with Kohonen Maps](https://c.mql5.com/2/52/data_science_ml_kohonen_maps_014_600x314.jpg)
Data Science and Machine Learning(Part 14): Finding Your Way in the Markets with Kohonen Maps
Are you looking for a cutting-edge approach to trading that can help you navigate complex and ever-changing markets? Look no further than Kohonen maps, an innovative form of artificial neural networks that can help you uncover hidden patterns and trends in market data. In this article, we'll explore how Kohonen maps work, and how they can be used to develop smarter, more effective trading strategies. Whether you're a seasoned trader or just starting out, you won't want to miss this exciting new approach to trading.
![Neural networks made easy (Part 66): Exploration problems in offline learning](https://c.mql5.com/2/61/Neural_networks_are_easy_Part_66_600x314.jpg)
Neural networks made easy (Part 66): Exploration problems in offline learning
Models are trained offline using data from a prepared training dataset. While providing certain advantages, its negative side is that information about the environment is greatly compressed to the size of the training dataset. Which, in turn, limits the possibilities of exploration. In this article, we will consider a method that enables the filling of a training dataset with the most diverse data possible.
![Interview with Egidijus Bockus (ATC 2012)](https://c.mql5.com/2/0/avatar__16.png)
![Interview with Egidijus Bockus (ATC 2012)](https://c.mql5.com/i/articles/overlay.png)
Interview with Egidijus Bockus (ATC 2012)
"I examined many indicators before realizing that they are not necessary for making money on Forex" - our present interviewee Egidijus Bockus (Egidijus) told us boldly. We have all reasons to take his words seriously, as his Expert Advisor occupies the third place with more than $32 000 beginning from the third week of the Automated Trading Championship 2012.
![Data Science and Machine Learning (Part 08): K-Means Clustering in plain MQL5](https://c.mql5.com/2/50/k-means_clustering_600x314.jpg)
Data Science and Machine Learning (Part 08): K-Means Clustering in plain MQL5
Data mining is crucial to a data scientist and a trader because very often, the data isn't as straightforward as we think it is. The human eye can not understand the minor underlying pattern and relationships in the dataset, maybe the K-means algorithm can help us with that. Let's find out...
![Statistical Arbitrage with predictions](https://c.mql5.com/2/77/Statistical_Arbitrage_with_predictions_600x314.jpg)
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.