MQL5 Programming Articles

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Study the MQL5 language for programming trading strategies in numerous published articles mostly written by you - the community members. The articles are grouped into categories to help you quicker find answers to any questions related to programming: Integration, Tester, Trading Strategies, etc.

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Introduction to MQL5 (Part 3): Mastering the Core Elements of MQL5

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.
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Population optimization algorithms: Nelder–Mead, or simplex search (NM) method

Population optimization algorithms: Nelder–Mead, or simplex search (NM) method

The article presents a complete exploration of the Nelder-Mead method, explaining how the simplex (function parameter space) is modified and rearranged at each iteration to achieve an optimal solution, and describes how the method can be improved.
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Benefiting from Forex market seasonality

Benefiting from Forex market seasonality

We are all familiar with the concept of seasonality, for example, we are all accustomed to rising prices for fresh vegetables in winter or rising fuel prices during severe frosts, but few people know that similar patterns exist in the Forex market.
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Estimate future performance with confidence intervals

Estimate future performance with confidence intervals

In this article we delve into the application of boostrapping techniques as a means to estimate the future performance of an automated strategy.
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Developing a Replay System — Market simulation (Part 13): Birth of the SIMULATOR (III)

Developing a Replay System — Market simulation (Part 13): Birth of the SIMULATOR (III)

Here we will simplify a few elements related to the work in the next article. I'll also explain how you can visualize what the simulator generates in terms of randomness.
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Neural networks made easy (Part 42): Model procrastination, reasons and solutions

Neural networks made easy (Part 42): Model procrastination, reasons and solutions

In the context of reinforcement learning, model procrastination can be caused by several reasons. The article considers some of the possible causes of model procrastination and methods for overcoming them.
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DoEasy. Controls (Part 31): Scrolling the contents of the ScrollBar control

DoEasy. Controls (Part 31): Scrolling the contents of the ScrollBar control

In this article, I will implement the functionality of scrolling the contents of the container using the buttons of the horizontal scrollbar.
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MQL5 Wizard Techniques you should know (Part 13): DBSCAN for Expert Signal Class

MQL5 Wizard Techniques you should know (Part 13): DBSCAN for Expert Signal Class

Density Based Spatial Clustering for Applications with Noise is an unsupervised form of grouping data that hardly requires any input parameters, save for just 2, which when compared to other approaches like k-means, is a boon. We delve into how this could be constructive for testing and eventually trading with Wizard assembled Expert Advisers
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Classification models in the Scikit-Learn library and their export to ONNX

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.
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Developing a Replay System — Market simulation (Part 19): Necessary adjustments

Developing a Replay System — Market simulation (Part 19): Necessary adjustments

Here we will prepare the ground so that if we need to add new functions to the code, this will happen smoothly and easily. The current code cannot yet cover or handle some of the things that will be necessary to make meaningful progress. We need everything to be structured in order to enable the implementation of certain things with the minimal effort. If we do everything correctly, we can get a truly universal system that can very easily adapt to any situation that needs to be handled.
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Category Theory in MQL5 (Part 10): Monoid Groups

Category Theory in MQL5 (Part 10): Monoid Groups

This article continues the series on category theory implementation in MQL5. Here we look at monoid-groups as a means normalising monoid sets making them more comparable across a wider span of monoid sets and data types..
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Integrate Your Own LLM into EA (Part 1): Hardware and Environment Deployment

Integrate Your Own LLM into EA (Part 1): Hardware and Environment Deployment

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.
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Developing a Replay System — Market simulation (Part 23): FOREX (IV)

Developing a Replay System — Market simulation (Part 23): FOREX (IV)

Now the creation occurs at the same point where we converted ticks into bars. This way, if something goes wrong during the conversion process, we will immediately notice the error. This is because the same code that places 1-minute bars on the chart during fast forwarding is also used for the positioning system to place bars during normal performance. In other words, the code that is responsible for this task is not duplicated anywhere else. This way we get a much better system for both maintenance and improvement.
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Neural networks made easy (Part 40): Using Go-Explore on large amounts of data

Neural networks made easy (Part 40): Using Go-Explore on large amounts of data

This article discusses the use of the Go-Explore algorithm over a long training period, since the random action selection strategy may not lead to a profitable pass as training time increases.
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Developing a Replay System — Market simulation (Part 24): FOREX (V)

Developing a Replay System — Market simulation (Part 24): FOREX (V)

Today we will remove a limitation that has been preventing simulations based on the Last price and will introduce a new entry point specifically for this type of simulation. The entire operating mechanism will be based on the principles of the forex market. The main difference in this procedure is the separation of Bid and Last simulations. However, it is important to note that the methodology used to randomize the time and adjust it to be compatible with the C_Replay class remains identical in both simulations. This is good because changes in one mode lead to automatic improvements in the other, especially when it comes to handling time between ticks.
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Quantitative analysis in MQL5: Implementing a promising algorithm

Quantitative analysis in MQL5: Implementing a promising algorithm

We will analyze the question of what quantitative analysis is and how it is used by major players. We will create one of the quantitative analysis algorithms in the MQL5 language.
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Population optimization algorithms: Mind Evolutionary Computation (MEC) algorithm

Population optimization algorithms: Mind Evolutionary Computation (MEC) algorithm

The article considers the algorithm of the MEC family called the simple mind evolutionary computation algorithm (Simple MEC, SMEC). The algorithm is distinguished by the beauty of its idea and ease of implementation.
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Developing a Replay System — Market simulation (Part 12): Birth of the SIMULATOR (II)

Developing a Replay System — Market simulation (Part 12): Birth of the SIMULATOR (II)

Developing a simulator can be much more interesting than it seems. Today we'll take a few more steps in this direction because things are getting more interesting.
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DoEasy. Controls (Part 19): Scrolling tabs in TabControl, WinForms object events

DoEasy. Controls (Part 19): Scrolling tabs in TabControl, WinForms object events

In this article, I will create the functionality for scrolling tab headers in TabControl using scrolling buttons. The functionality is meant to place tab headers into a single line from either side of the control.
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DoEasy. Controls (Part 12): Base list object, ListBox and ButtonListBox WinForms objects

DoEasy. Controls (Part 12): Base list object, ListBox and ButtonListBox WinForms objects

In this article, I am going to create the base object of WinForms object lists, as well as the two new objects: ListBox and ButtonListBox.
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Introduction to MQL5 (Part 2): Navigating Predefined Variables, Common Functions, and  Control Flow Statements

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!
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Data Science and Machine Learning (Part 20): Algorithmic Trading Insights, A Faceoff Between LDA and PCA in MQL5

Data Science and Machine Learning (Part 20): Algorithmic Trading Insights, A Faceoff Between LDA and PCA in MQL5

Uncover the secrets behind these powerful dimensionality reduction techniques as we dissect their applications within the MQL5 trading environment. Delve into the nuances of Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA), gaining a profound understanding of their impact on strategy development and market analysis,
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Design Patterns in software development and MQL5 (Part 2): Structural Patterns

Design Patterns in software development and MQL5 (Part 2): Structural Patterns

In this article, we will continue our articles about Design Patterns after learning how much this topic is more important for us as developers to develop extendable, reliable applications not only by the MQL5 programming language but others as well. We will learn about another type of Design Patterns which is the structural one to learn how to design systems by using what we have as classes to form larger structures.
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Developing a Replay System — Market simulation (Part 22): FOREX (III)

Developing a Replay System — Market simulation (Part 22): FOREX (III)

Although this is the third article on this topic, I must explain for those who have not yet understood the difference between the stock market and the foreign exchange market: the big difference is that in the Forex there is no, or rather, we are not given information about some points that actually occurred during the course of trading.
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Data label for time series mining (Part 4):Interpretability Decomposition Using Label Data

Data label for time series mining (Part 4):Interpretability Decomposition Using Label Data

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!
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Category Theory in MQL5 (Part 4): Spans, Experiments, and Compositions

Category Theory in MQL5 (Part 4): Spans, Experiments, and Compositions

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.
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Neural networks made easy (Part 52): Research with optimism and distribution correction

Neural networks made easy (Part 52): Research with optimism and distribution correction

As the model is trained based on the experience reproduction buffer, the current Actor policy moves further and further away from the stored examples, which reduces the efficiency of training the model as a whole. In this article, we will look at the algorithm of improving the efficiency of using samples in reinforcement learning algorithms.
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Category Theory in MQL5 (Part 23): A different look at the Double Exponential Moving Average

Category Theory in MQL5 (Part 23): A different look at the Double Exponential Moving Average

In this article we continue with our theme in the last of tackling everyday trading indicators viewed in a ‘new’ light. We are handling horizontal composition of natural transformations for this piece and the best indicator for this, that expands on what we just covered, is the double exponential moving average (DEMA).
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Neural networks made easy (Part 57): Stochastic Marginal Actor-Critic (SMAC)

Neural networks made easy (Part 57): Stochastic Marginal Actor-Critic (SMAC)

Here I will consider the fairly new Stochastic Marginal Actor-Critic (SMAC) algorithm, which allows building latent variable policies within the framework of entropy maximization.
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Introduction to MQL5 (Part 6): A Beginner's Guide to Array Functions in MQL5

Introduction to MQL5 (Part 6): A Beginner's Guide to Array Functions in MQL5

Embark on the next phase of our MQL5 journey. In this insightful and beginner-friendly article, we'll look into the remaining array functions, demystifying complex concepts to empower you to craft efficient trading strategies. We’ll be discussing ArrayPrint, ArrayInsert, ArraySize, ArrayRange, ArrarRemove, ArraySwap, ArrayReverse, and ArraySort. Elevate your algorithmic trading expertise with these essential array functions. Join us on the path to MQL5 mastery!
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Alternative risk return metrics in MQL5

Alternative risk return metrics in MQL5

In this article we present the implementation of several risk return metrics billed as alternatives to the Sharpe ratio and examine hypothetical equity curves to analyze their characteristics.
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Developing a Replay System (Part 28): Expert Advisor project — C_Mouse class (II)

Developing a Replay System (Part 28): Expert Advisor project — C_Mouse class (II)

When people started creating the first systems capable of computing, everything required the participation of engineers, who had to know the project very well. We are talking about the dawn of computer technology, a time when there were not even terminals for programming. As it developed and more people got interested in being able to create something, new ideas and ways of programming emerged which replaced the previous-style changing of connector positions. This is when the first terminals appeared.
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Advanced Variables and Data Types in MQL5

Advanced Variables and Data Types in MQL5

Variables and data types are very important topics not only in MQL5 programming but also in any programming language. MQL5 variables and data types can be categorized as simple and advanced ones. In this article, we will identify and learn about advanced ones because we already mentioned simple ones in a previous article.
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MQL5 Wizard Techniques you should know (Part 11): Number Walls

MQL5 Wizard Techniques you should know (Part 11): Number Walls

Number Walls are a variant of Linear Shift Back Registers that prescreen sequences for predictability by checking for convergence. We look at how these ideas could be of use in MQL5.
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Population optimization algorithms: Differential Evolution (DE)

Population optimization algorithms: Differential Evolution (DE)

In this article, we will consider the algorithm that demonstrates the most controversial results of all those discussed previously - the differential evolution (DE) algorithm.
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Developing an MQTT client for Metatrader 5: a TDD approach — Part 4

Developing an MQTT client for Metatrader 5: a TDD approach — Part 4

This article is the fourth part of a series describing our development steps of a native MQL5 client for the MQTT protocol. In this part, we describe what MQTT v5.0 Properties are, their semantics, how we are reading some of them, and provide a brief example of how Properties can be used to extend the protocol.
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MQL5 Wizard Techniques you should know (Part 16): Principal Component Analysis with Eigen Vectors

MQL5 Wizard Techniques you should know (Part 16): Principal Component Analysis with Eigen Vectors

Principal Component Analysis, a dimensionality reducing technique in data analysis, is looked at in this article, with how it could be implemented with Eigen values and vectors. As always, we aim to develop a prototype expert-signal-class usable in the MQL5 wizard.
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Developing a Replay System — Market simulation (Part 25): Preparing for the next phase

Developing a Replay System — Market simulation (Part 25): Preparing for the next phase

In this article, we complete the first phase of developing our replay and simulation system. Dear reader, with this achievement I confirm that the system has reached an advanced level, paving the way for the introduction of new functionality. The goal is to enrich the system even further, turning it into a powerful tool for research and development of market analysis.
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Data Science and Machine Learning (Part 17): Money in the Trees? The Art and Science of Random Forests in Forex Trading

Data Science and Machine Learning (Part 17): Money in the Trees? The Art and Science of Random Forests in Forex Trading

Discover the secrets of algorithmic alchemy as we guide you through the blend of artistry and precision in decoding financial landscapes. Unearth how Random Forests transform data into predictive prowess, offering a unique perspective on navigating the complex terrain of stock markets. Join us on this journey into the heart of financial wizardry, where we demystify the role of Random Forests in shaping market destiny and unlocking the doors to lucrative opportunities
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Developing a Replay System — Market simulation (Part 07): First improvements (II)

Developing a Replay System — Market simulation (Part 07): First improvements (II)

In the previous article, we made some fixes and added tests to our replication system to ensure the best possible stability. We also started creating and using a configuration file for this system.