![Developing an MQTT client for MetaTrader 5: a TDD approach — Part 3](https://c.mql5.com/2/58/mqtt_p3_600x314.jpg)
Developing an MQTT client for MetaTrader 5: a TDD approach — Part 3
This article is the third part of a series describing our development steps of a native MQL5 client for the MQTT protocol. In this part, we describe in detail how we are using Test-Driven Development to implement the Operational Behavior part of the CONNECT/CONNACK packet exchange. At the end of this step, our client MUST be able to behave appropriately when dealing with any of the possible server outcomes from a connection attempt.
![Neural networks made easy (Part 35): Intrinsic Curiosity Module](https://c.mql5.com/2/50/Neural_Networks_Made_035_600x314.jpg)
Neural networks made easy (Part 35): Intrinsic Curiosity Module
We continue to study reinforcement learning algorithms. All the algorithms we have considered so far required the creation of a reward policy to enable the agent to evaluate each of its actions at each transition from one system state to another. However, this approach is rather artificial. In practice, there is some time lag between an action and a reward. In this article, we will get acquainted with a model training algorithm which can work with various time delays from the action to the reward.
![Understand and Efficiently use OpenCL API by Recreating built-in support as DLL on Linux (Part 2): OpenCL Simple DLL implementation](https://c.mql5.com/2/53/Recreating-built-in-OpenCL-p3_600x314.jpg)
Understand and Efficiently use OpenCL API by Recreating built-in support as DLL on Linux (Part 2): OpenCL Simple DLL implementation
Continued from the part 1 in the series, now we proceed to implement as a simple DLL then test with MetaTrader 5. This will prepare us well before developing a full-fledge OpenCL as DLL support in the following part to come.
![Category Theory in MQL5 (Part 20): A detour to Self-Attention and the Transformer](https://c.mql5.com/2/58/Category-Theory-p20_600x314.jpg)
Category Theory in MQL5 (Part 20): A detour to Self-Attention and the Transformer
We digress in our series by pondering at part of the algorithm to chatGPT. Are there any similarities or concepts borrowed from natural transformations? We attempt to answer these and other questions in a fun piece, with our code in a signal class format.
![Developing a Replay System (Part 27): Expert Advisor project — C_Mouse class (I)](https://c.mql5.com/2/58/Projeto_Expert_AdvisorcClasse_C_Mous_600x314.jpg)
Developing a Replay System (Part 27): Expert Advisor project — C_Mouse class (I)
In this article we will implement the C_Mouse class. It provides the ability to program at the highest level. However, talking about high-level or low-level programming languages is not about including obscene words or jargon in the code. It's the other way around. When we talk about high-level or low-level programming, we mean how easy or difficult the code is for other programmers to understand.
![Dimitar Manov: "I fear only extraordinary situations in the Championship" (ATC 2010)](https://c.mql5.com/2/0/manov_avatar.png)
![Dimitar Manov: "I fear only extraordinary situations in the Championship" (ATC 2010)](https://c.mql5.com/i/articles/overlay.png)
Dimitar Manov: "I fear only extraordinary situations in the Championship" (ATC 2010)
In the recent review by Boris Odintsov the Expert Advisor of the Bulgarian Participant Dimitar Manov appeared among the most stable and reliable EAs. We decided to interview this developer and try to find the secret of his success. In this interview Dimitar has told us what situation would be unfavorable for his robot, why he's not using indicators and whether he is expecting to win the competition.
![Data Science and Machine Learning (Part 19): Supercharge Your AI models with AdaBoost](https://c.mql5.com/2/65/Data_Science_and_Machine_Learning_7Part_191_Supercharge_Your_AI_models_with_AdaBoost_600x314.jpg)
Data Science and Machine Learning (Part 19): Supercharge Your AI models with AdaBoost
AdaBoost, a powerful boosting algorithm designed to elevate the performance of your AI models. AdaBoost, short for Adaptive Boosting, is a sophisticated ensemble learning technique that seamlessly integrates weak learners, enhancing their collective predictive strength.
![Population optimization algorithms: Saplings Sowing and Growing up (SSG)](https://c.mql5.com/2/52/growing-trees_600x314.jpg)
Population optimization algorithms: Saplings Sowing and Growing up (SSG)
Saplings Sowing and Growing up (SSG) algorithm is inspired by one of the most resilient organisms on the planet demonstrating outstanding capability for survival in a wide variety of conditions.
![Category Theory in MQL5 (Part 2)](https://c.mql5.com/2/51/Category-Theory-part-2_600x314.jpg)
Category Theory in MQL5 (Part 2)
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 attracts comments and discussion while hopefully furthering the use of this remarkable field in Traders' strategy development.
![Building A Candlestick Trend Constraint Model(Part 3): Detecting changes in trends while using this system](https://c.mql5.com/2/78/Building_A_Candlestick_Trend_Constraint_Model_Part_3___NO_BG_600x314.jpg)
Building A Candlestick Trend Constraint Model(Part 3): Detecting changes in trends while using this system
This article explores how economic news releases, investor behavior, and various factors can influence market trend reversals. It includes a video explanation and proceeds by incorporating MQL5 code into our program to detect trend reversals, alert us, and take appropriate actions based on market conditions. This builds upon previous articles in the series.
![Introduction to MQL5 (Part 3): Mastering the Core Elements of MQL5](https://c.mql5.com/2/65/Introduction_to_MQL5_7Part_36_Mastering_the_Core_Elements_of_MQL5_600x314.jpg)
Introduction to MQL5 (Part 3): Mastering the Core Elements of MQL5
Explore the fundamentals of MQL5 programming in this beginner-friendly article, where we demystify arrays, custom functions, preprocessors, and event handling, all explained with clarity making every line of code accessible. Join us in unlocking the power of MQL5 with a unique approach that ensures understanding at every step. This article sets the foundation for mastering MQL5, emphasizing the explanation of each line of code, and providing a distinct and enriching learning experience.
![DoEasy. Controls (Part 24): Hint auxiliary WinForms object](https://c.mql5.com/2/50/doeasy_024_600x314.jpg)
DoEasy. Controls (Part 24): Hint auxiliary WinForms object
In this article, I will revise the logic of specifying the base and main objects for all WinForms library objects, develop a new Hint base object and several of its derived classes to indicate the possible direction of moving the separator.
![Developing an MQTT client for MetaTrader 5: a TDD approach](https://c.mql5.com/2/56/mqtt_600x314.jpg)
Developing an MQTT client for MetaTrader 5: a TDD approach
This article reports the first attempts in the development of a native MQTT client for MQL5. MQTT is a Client Server publish/subscribe messaging transport protocol. It is lightweight, open, simple, and designed to be easy to implement. These characteristics make it ideal for use in many situations.
![Neural networks made easy (Part 48): Methods for reducing overestimation of Q-function values](https://c.mql5.com/2/56/NN_part_48_600x314.jpg)
Neural networks made easy (Part 48): Methods for reducing overestimation of Q-function values
In the previous article, we introduced the DDPG method, which allows training models in a continuous action space. However, like other Q-learning methods, DDPG is prone to overestimating Q-function values. This problem often results in training an agent with a suboptimal strategy. In this article, we will look at some approaches to overcome the mentioned issue.
![Category Theory in MQL5 (Part 18): Naturality Square](https://c.mql5.com/2/57/category-theory-p18_600x314.jpg)
Category Theory in MQL5 (Part 18): Naturality Square
This article continues our series into category theory by introducing natural transformations, a key pillar within the subject. We look at the seemingly complex definition, then delve into examples and applications with this series’ ‘bread and butter’; volatility forecasting.
![Interview with Valery Mazurenko (ATC 2011)](https://c.mql5.com/2/0/ava_notused.png)
![Interview with Valery Mazurenko (ATC 2011)](https://c.mql5.com/i/articles/overlay.png)
Interview with Valery Mazurenko (ATC 2011)
The task of writing an Expert Advisor trading on multiple currency pairs is complex both in terms of finding suitable strategies and from the technological side. But if the goal is set clear, nothing is impossible then. It was four times already that Vitaly Mazurenko (notused) submitted his multi-currency Expert Advisor. It seems, he has managed to find the right way this time.
![Interview with Berron Parker (ATC 2010)](https://c.mql5.com/2/0/Berron_ava.png)
![Interview with Berron Parker (ATC 2010)](https://c.mql5.com/i/articles/overlay.png)
Interview with Berron Parker (ATC 2010)
During the first week of the Championship Berron's Expert Advisor has been on the top position. He now tells us about his experience of EA development and difficulties of moving to MQL5. Berron says his EA is set up to work in a trend market, but can be weak in other market conditions. However, he is hopeful that his robot will show good results in this competition.
![Integrating ML models with the Strategy Tester (Part 3): Managing CSV files (II)](https://c.mql5.com/2/51/Perceptron_Multicamadas_e_o-Algoritmo_Backpropagation_Parte_3_02_600x314.jpg)
Integrating ML models with the Strategy Tester (Part 3): Managing CSV files (II)
This material provides a complete guide to creating a class in MQL5 for efficient management of CSV files. We will see the implementation of methods for opening, writing, reading, and transforming data. We will also consider how to use them to store and access information. In addition, we will discuss the limitations and the most important aspects of using such a class. This article ca be a valuable resource for those who want to learn how to process CSV files in MQL5.
![Interview with Igor Korepin (ATC 2011)](https://c.mql5.com/2/0/Xupypr_ava.png)
![Interview with Igor Korepin (ATC 2011)](https://c.mql5.com/i/articles/overlay.png)
Interview with Igor Korepin (ATC 2011)
Appearance of the Expert Advisor cs2011 by Igor Korepin (Xupypr) at the very top of the Automated Trading Championship 2011 was really impressive - its balance was almost twice that of the EA featured on the second place. However, despite such a sound breakaway, the Expert Advisor could not stay long on the first line. Igor frankly said that he relied much on a lucky start of his trading robot in the competition. We'll see if luck helps this simple EA to take the lead in the ATC 2011 race again.
![Developing Zone Recovery Martingale strategy in MQL5](https://c.mql5.com/2/82/Developing_Zone_Recovery_Martingale_strategy_in_MQL5_600x314.jpg)
Developing Zone Recovery Martingale strategy in MQL5
The article discusses, in a detailed perspective, the steps that need to be implemented towards the creation of an expert advisor based on the Zone Recovery trading algorithm. This helps aotomate the system saving time for algotraders.
![Interview with Andrey Bobryashov (ATC 2011)](https://c.mql5.com/2/0/tmt0086_ATC_2011.png)
![Interview with Andrey Bobryashov (ATC 2011)](https://c.mql5.com/i/articles/overlay.png)
Interview with Andrey Bobryashov (ATC 2011)
Since the first Automated Trading Championship we have seen plenty of trading robots in our TOP-10 created with the use of various methods. Excellent results were shown both by the Exper Advisors based on standard indicators, and complicated analytical complexes with weekly automatic optimization of their own parameters.
![Interview with Boris Odintsov (ATC 2010)](https://c.mql5.com/2/0/bobsley_ava.png)
![Interview with Boris Odintsov (ATC 2010)](https://c.mql5.com/i/articles/overlay.png)
Interview with Boris Odintsov (ATC 2010)
Boris Odintsov is one of the most impressive participants of the Championship who managed to go beyond $100,000 on the third week of the competition. Boris explains the rapid rise of his expert Advisor as a favorable combination of circumstances. In this interview he tells about what is important in trading, and what market would be unfavorable for his EA.
![DoEasy. Controls (Part 28): Bar styles in the ProgressBar control](https://c.mql5.com/2/50/doeasy_028_600x314.jpg)
DoEasy. Controls (Part 28): Bar styles in the ProgressBar control
In this article, I will develop display styles and description text for the progress bar of the ProgressBar control.
![News Trading Made Easy (Part 1): Creating a Database](https://c.mql5.com/2/74/News_Trading_Made_Easy_bPart_1b_Economic_Calendar_600x314.jpg)
News Trading Made Easy (Part 1): Creating a Database
News trading can be complicated and overwhelming, in this article we will go through steps to obtain news data. Additionally we will learn about the MQL5 Economic Calendar and what it has to offer.
![Classification models in the Scikit-Learn library and their export to ONNX](https://c.mql5.com/2/58/Scikit_learn_to-ONNX_600x314.jpg)
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.
![Interview with Antonio Morillas (ATC 2011)](https://c.mql5.com/2/0/salliromrava.png)
![Interview with Antonio Morillas (ATC 2011)](https://c.mql5.com/i/articles/overlay.png)
Interview with Antonio Morillas (ATC 2011)
Antonio Morillas from Spain (sallirom, by the way - it is reversed surname!) was first who doubled his starting balance from the beginning of the Championship and thus attracted our attention. His trading strategy is extremely risky. We decided to talk to Antonio about risk and luck as these are part and parcel of Automated Trading Championship.
![Interview with Vitaly Antonov (ATC 2011)](https://c.mql5.com/2/0/beast_ava.png)
![Interview with Vitaly Antonov (ATC 2011)](https://c.mql5.com/i/articles/overlay.png)
Interview with Vitaly Antonov (ATC 2011)
It was only this summer that Vitaly Antonov (beast) has learned about the upcoming Automated Trading Championship and got to know MetaTrader 5 terminal. Time was running out, besides, Vitaly was a newcomer. So, he randomly chose GBPUSD currency pair to develop his trading system. And the choice turned out to be successful. It would have been impossible to use other symbols with the strategy.
![Neural networks made easy (Part 47): Continuous action space](https://c.mql5.com/2/55/Neural_Networks_Part_47_600x314.jpg)
Neural networks made easy (Part 47): Continuous action space
In this article, we expand the range of tasks of our agent. The training process will include some aspects of money and risk management, which are an integral part of any trading strategy.
![Matrix Utils, Extending the Matrices and Vector Standard Library Functionality](https://c.mql5.com/2/50/Matrix-Utils_600x314.jpg)
Matrix Utils, Extending the Matrices and Vector Standard Library Functionality
Matrix serves as the foundation of machine learning algorithms and computers in general because of their ability to effectively handle large mathematical operations, The Standard library has everything one needs but let's see how we can extend it by introducing several functions in the utils file, that are not yet available in the library
![Automated exchange grid trading using stop pending orders on Moscow Exchange (MOEX)](https://c.mql5.com/2/54/moex-mesh-trading_600x314.jpg)
Automated exchange grid trading using stop pending orders on Moscow Exchange (MOEX)
The article considers the grid trading approach based on stop pending orders and implemented in an MQL5 Expert Advisor on the Moscow Exchange (MOEX). When trading in the market, one of the simplest strategies is a grid of orders designed to "catch" the market price.
![Developing a Replay System (Part 32): Order System (I)](https://c.mql5.com/2/59/sistema_de_Replay_32__600x314.jpg)
Developing a Replay System (Part 32): Order System (I)
Of all the things that we have developed so far, this system, as you will probably notice and eventually agree, is the most complex. Now we need to do something very simple: make our system simulate the operation of a trading server. This need to accurately implement the way the trading server operates seems like a no-brainer. At least in words. But we need to do this so that the everything is seamless and transparent for the user of the replay/simulation system.
![DoEasy. Controls (Part 22): SplitContainer. Changing the properties of the created object](https://c.mql5.com/2/49/DoEasy_22_SplitContainer_600x314.jpg)
DoEasy. Controls (Part 22): SplitContainer. Changing the properties of the created object
In the current article, I will implement the ability to change the properties and appearance of the newly created SplitContainer control.
![ATC Champions League: Interview with Roman Zamozhniy (ATC 2011)](https://c.mql5.com/2/0/avatar1__2.png)
![ATC Champions League: Interview with Roman Zamozhniy (ATC 2011)](https://c.mql5.com/i/articles/overlay.png)
ATC Champions League: Interview with Roman Zamozhniy (ATC 2011)
This is the first interview in the "ATC Champions League" project. Roman Zamozhniy (Rich) from Ukraine was the winner of the first Automated Trading Championship in 2006. In addition, he is a regular participant of our Championships - he has not missed a single contest. In this interview, we talked about Roman's first place and tried to figure out what is necessary for successful participation.
![ATC Champions League: Interview with Olexandr Topchylo (ATC 2011)](https://c.mql5.com/2/0/avatar2__1.png)
![ATC Champions League: Interview with Olexandr Topchylo (ATC 2011)](https://c.mql5.com/i/articles/overlay.png)
ATC Champions League: Interview with Olexandr Topchylo (ATC 2011)
Interview with Olexandr Topchylo (Better) is the second publication within the "ATC Champions League" project. Having won the Automated Trading Championship 2007, this professional trader caught the attention of investors. Olexandr says that his first place in the ATC 2007 is one of the major events of his trading experience. However, later on this popularity helped him discover the biggest disappointment - it is so easy to lose investors after the first drawdown on an investor account.
![Developing a Replay System (Part 26): Expert Advisor project — C_Terminal class](https://c.mql5.com/2/58/replay-p26_600x314.jpg)
Developing a Replay System (Part 26): Expert Advisor project — C_Terminal class
We can now start creating an Expert Advisor for use in the replay/simulation system. However, we need something improved, not a random solution. Despite this, we should not be intimidated by the initial complexity. It's important to start somewhere, otherwise we end up ruminating about the difficulty of a task without even trying to overcome it. That's what programming is all about: overcoming obstacles through learning, testing, and extensive research.
![Building A Candlestick Trend Constraint Model (Part 1): For EAs And Technical Indicators](https://c.mql5.com/2/76/Building_A_Candlestick_Trend_Constraint_Model_lPart_13__600x314.jpg)
Building A Candlestick Trend Constraint Model (Part 1): For EAs And Technical Indicators
This article is aimed at beginners and pro-MQL5 developers. It provides a piece of code to define and constrain signal-generating indicators to trends in higher timeframes. In this way, traders can enhance their strategies by incorporating a broader market perspective, leading to potentially more robust and reliable trading signals.
![Seasonality Filtering and time period for Deep Learning ONNX models with python for EA](https://c.mql5.com/2/73/Seasonality_Filtering_and_time_period_for_Deep_Learning_ONNX_models_600x314.jpg)
Seasonality Filtering and time period for Deep Learning ONNX models with python for EA
Can we benefit from seasonality when creating models for Deep Learning with Python? Does filtering data for the ONNX models help to get better results? What time period should we use? We will cover all of this over this article.
![Introduction to MQL5 (Part 2): Navigating Predefined Variables, Common Functions, and Control Flow Statements](https://c.mql5.com/2/64/Introduction_to_MQL5_3Part_2v_Navigating_Predefined_Variablesn_Common_Functionsc_and_Control_Flow_St.jpg)
Introduction to MQL5 (Part 2): Navigating Predefined Variables, Common Functions, and Control Flow Statements
Embark on an illuminating journey with Part Two of our MQL5 series. These articles are not just tutorials, they're doorways to an enchanted realm where programming novices and wizards alike unite. What makes this journey truly magical? Part Two of our MQL5 series stands out with its refreshing simplicity, making complex concepts accessible to all. Engage with us interactively as we answer your questions, ensuring an enriching and personalized learning experience. Let's build a community where understanding MQL5 is an adventure for everyone. Welcome to the enchantment!
![Integrating ML models with the Strategy Tester (Conclusion): Implementing a regression model for price prediction](https://c.mql5.com/2/58/implementation_regression_model_600x314.jpg)
Integrating ML models with the Strategy Tester (Conclusion): Implementing a regression model for price prediction
This article describes the implementation of a regression model based on a decision tree. The model should predict prices of financial assets. We have already prepared the data, trained and evaluated the model, as well as adjusted and optimized it. However, it is important to note that this model is intended for study purposes only and should not be used in real trading.
![Interview with Andrei Moraru (ATC 2011)](https://c.mql5.com/2/0/Moraru_ava.png)
![Interview with Andrei Moraru (ATC 2011)](https://c.mql5.com/i/articles/overlay.png)
Interview with Andrei Moraru (ATC 2011)
Ukrainian programmer Andrei Moraru (enivid) is an active participant of the Automated Trading Championship beginning from 2007. Andrei had already come in our view at that time and now we have decided to find out if there occured any changes in his attitude towards trading and selection of trading strategies for the past four years, and also to know about his new Expert Advisor.