MQL5 Programming Articles
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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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.
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.
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!
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.
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.
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.
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.
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.
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).
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.
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.
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.
Developing a Replay System (Part 37): Paving the Path (I)
In this article, we will finally begin to do what we wanted to do much earlier. However, due to the lack of "solid ground", I did not feel confident to present this part publicly. Now I have the basis to do this. I suggest that you focus as much as possible on understanding the content of this article. I mean not simply reading it. I want to emphasize that if you do not understand this article, you can completely give up hope of understanding the content of the following ones.
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.
Neural networks made easy (Part 64): ConserWeightive Behavioral Cloning (CWBC) method
As a result of tests performed in previous articles, we came to the conclusion that the optimality of the trained strategy largely depends on the training set used. In this article, we will get acquainted with a fairly simple yet effective method for selecting trajectories to train models.
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
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.
Neural networks made easy (Part 62): Using Decision Transformer in hierarchical models
In recent articles, we have seen several options for using the Decision Transformer method. The method allows analyzing not only the current state, but also the trajectory of previous states and actions performed in them. In this article, we will focus on using this method in hierarchical models.
Population optimization algorithms: Changing shape, shifting probability distributions and testing on Smart Cephalopod (SC)
The article examines the impact of changing the shape of probability distributions on the performance of optimization algorithms. We will conduct experiments using the Smart Cephalopod (SC) test algorithm to evaluate the efficiency of various probability distributions in the context of optimization problems.
Neural networks made easy (Part 65): Distance Weighted Supervised Learning (DWSL)
In this article, we will get acquainted with an interesting algorithm that is built at the intersection of supervised and reinforcement learning methods.
Developing a Replay System (Part 31): Expert Advisor project — C_Mouse class (V)
We need a timer that can show how much time is left till the end of the replay/simulation run. This may seem at first glance to be a simple and quick solution. Many simply try to adapt and use the same system that the trading server uses. But there's one thing that many people don't consider when thinking about this solution: with replay, and even m ore with simulation, the clock works differently. All this complicates the creation of such a system.
Population optimization algorithms: Simulated Annealing (SA) algorithm. Part I
The Simulated Annealing algorithm is a metaheuristic inspired by the metal annealing process. In the article, we will conduct a thorough analysis of the algorithm and debunk a number of common beliefs and myths surrounding this widely known optimization method. The second part of the article will consider the custom Simulated Isotropic Annealing (SIA) algorithm.
Data Science and Machine Learning (Part 16): A Refreshing Look at Decision Trees
Dive into the intricate world of decision trees in the latest installment of our Data Science and Machine Learning series. Tailored for traders seeking strategic insights, this article serves as a comprehensive recap, shedding light on the powerful role decision trees play in the analysis of market trends. Explore the roots and branches of these algorithmic trees, unlocking their potential to enhance your trading decisions. Join us for a refreshing perspective on decision trees and discover how they can be your allies in navigating the complexities of financial markets.
DRAW_ARROW drawing type in multi-symbol multi-period indicators
In this article, we will look at drawing arrow multi-symbol multi-period indicators. We will also improve the class methods for correct display of arrows showing data from arrow indicators calculated on a symbol/period that does not correspond to the symbol/period of the current chart.
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.
Category Theory in MQL5 (Part 6): Monomorphic Pull-Backs and Epimorphic Push-Outs
Category Theory is a diverse and expanding branch of Mathematics which is only recently getting some coverage in the MQL5 community. These series of articles look to explore and examine some of its concepts & axioms with the overall goal of establishing an open library that provides insight while also hopefully furthering the use of this remarkable field in Traders' strategy development.
Developing a quality factor for Expert Advisors
In this article, we will see how to develop a quality score that your Expert Advisor can display in the strategy tester. We will look at two well-known calculation methods – Van Tharp and Sunny Harris.
The case for using a Composite Data Set this Q4 in weighing SPDR XLY's next performance
We consider XLY, SPDR’s consumer discretionary spending ETF and see if with tools in MetaTrader’s IDE we can sift through an array of data sets in selecting what could work with a forecasting model with a forward outlook of not more than a year.
Developing a multi-currency Expert Advisor (Part 1): Collaboration of several trading strategies
There are quite a lot of different trading strategies. So, it might be useful to apply several strategies working in parallel to diversify risks and increase the stability of trading results. But if each strategy is implemented as a separate Expert Advisor (EA), then managing their work on one trading account becomes much more difficult. To solve this problem, it would be reasonable to implement the operation of different trading strategies within a single EA.
StringFormat(). Review and ready-made examples
The article continues the review of the PrintFormat() function. We will briefly look at formatting strings using StringFormat() and their further use in the program. We will also write templates to display symbol data in the terminal journal. The article will be useful for both beginners and experienced developers.
DoEasy. Controls (Part 11): WinForms objects — groups, CheckedListBox WinForms object
The article considers grouping WinForms objects and creation of the CheckBox objects list object.
Developing a Replay System (Part 36): Making Adjustments (II)
One of the things that can make our lives as programmers difficult is assumptions. In this article, I will show you how dangerous it is to make assumptions: both in MQL5 programming, where you assume that the type will have a certain value, and in MetaTrader 5, where you assume that different servers work the same.
Developing a Replay System — Market simulation (Part 18): Ticks and more ticks (II)
Obviously the current metrics are very far from the ideal time for creating a 1-minute bar. That's the first thing we are going to fix. Fixing the synchronization problem is not difficult. This may seem hard, but it's actually quite simple. We did not make the required correction in the previous article since its purpose was to explain how to transfer the tick data that was used to create the 1-minute bars on the chart into the Market Watch window.
Developing a Replay System — Market simulation (Part 16): New class system
We need to organize our work better. The code is growing, and if this is not done now, then it will become impossible. Let's divide and conquer. MQL5 allows the use of classes which will assist in implementing this task, but for this we need to have some knowledge about classes. Probably the thing that confuses beginners the most is inheritance. In this article, we will look at how to use these mechanisms in a practical and simple way.
Category Theory in MQL5 (Part 21): Natural Transformations with LDA
This article, the 21st in our series, continues with a look at Natural Transformations and how they can be implemented using linear discriminant analysis. We present applications of this in a signal class format, like in the previous article.
Neural networks made easy (Part 41): Hierarchical models
The article describes hierarchical training models that offer an effective approach to solving complex machine learning problems. Hierarchical models consist of several levels, each of which is responsible for different aspects of the task.
Category Theory in MQL5 (Part 19): Naturality Square Induction
We continue our look at natural transformations by considering naturality square induction. Slight restraints on multicurrency implementation for experts assembled with the MQL5 wizard mean we are showcasing our data classification abilities with a script. Principle applications considered are price change classification and thus its forecasting.
DoEasy. Controls (Part 14): New algorithm for naming graphical elements. Continuing work on the TabControl WinForms object
In this article, I will create a new algorithm for naming all graphical elements meant for building custom graphics, as well as continue developing the TabControl WinForms object.
Neural networks made easy (Part 61): Optimism issue in offline reinforcement learning
During the offline learning, we optimize the Agent's policy based on the training sample data. The resulting strategy gives the Agent confidence in its actions. However, such optimism is not always justified and can cause increased risks during the model operation. Today we will look at one of the methods to reduce these risks.
MQL5 Wizard Techniques you should know (Part 07): Dendrograms
Data classification for purposes of analysis and forecasting is a very diverse arena within machine learning and it features a large number of approaches and methods. This piece looks at one such approach, namely Agglomerative Hierarchical Classification.