Articles on MetaTrader 5 integration using MQL5
Traders meet interesting challenges which often require an innovative approach. This category features articles that offer the most unexpected solutions for evaluating, analyzing and processing price data and trading results. The articles describe various integration solutions, including connection of databases and ICQ, use of OpenCL and social networks, use of Delphi and C#.
Read on to learn how to use specialized mathematical and neural packages, and much more. Become an author and share unique ideas with the MQL5.community members.
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Population optimization algorithms: Bacterial Foraging Optimization (BFO)
E. coli bacterium foraging strategy inspired scientists to create the BFO optimization algorithm. The algorithm contains original ideas and promising approaches to optimization and is worthy of further study.
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.
Population optimization algorithms: Invasive Weed Optimization (IWO)
The amazing ability of weeds to survive in a wide variety of conditions has become the idea for a powerful optimization algorithm. IWO is one of the best algorithms among the previously reviewed ones.
Understand and efficiently use OpenCL API by recreating built-in support as DLL on Linux (Part 1): Motivation and validation
Bulit-in OpenCL support in MetaTrader 5 still has a major problem especially the one about device selection error 5114 resulting from unable to create an OpenCL context using CL_USE_GPU_ONLY, or CL_USE_GPU_DOUBLE_ONLY although it properly detects GPU. It works fine with directly using of ordinal number of GPU device we found in Journal tab, but that's still considered a bug, and users should not hard-code a device. We will solve it by recreating an OpenCL support as DLL with C++ on Linux. Along the journey, we will get to know OpenCL from concept to best practices in its API usage just enough for us to put into great use later when we deal with DLL implementation in C++ and consume it with MQL5.
Population optimization algorithms: Bat algorithm (BA)
In this article, I will consider the Bat Algorithm (BA), which shows good convergence on smooth functions.
Creating a ticker tape panel: Basic version
Here I will show how to create screens with price tickers which are usually used to display quotes on the exchange. I will do it by only using MQL5, without using complex external programming.
Population optimization algorithms: Firefly Algorithm (FA)
In this article, I will consider the Firefly Algorithm (FA) optimization method. Thanks to the modification, the algorithm has turned from an outsider into a real rating table leader.
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.
Category Theory in MQL5 (Part 1)
Category Theory is a diverse and expanding branch of Mathematics which as of yet is relatively uncovered in the MQL 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.
MQL5 Wizard techniques you should know (Part 04): Linear Discriminant Analysis
Todays trader is a philomath who is almost always looking up new ideas, trying them out, choosing to modify them or discard them; an exploratory process that should cost a fair amount of diligence. These series of articles will proposition that the MQL5 wizard should be a mainstay for traders in this effort.
Neural networks made easy (Part 26): Reinforcement Learning
We continue to study machine learning methods. With this article, we begin another big topic, Reinforcement Learning. This approach allows the models to set up certain strategies for solving the problems. We can expect that this property of reinforcement learning will open up new horizons for building trading strategies.
Neural networks made easy (Part 25): Practicing Transfer Learning
In the last two articles, we developed a tool for creating and editing neural network models. Now it is time to evaluate the potential use of Transfer Learning technology using practical examples.
Neural networks made easy (Part 24): Improving the tool for Transfer Learning
In the previous article, we created a tool for creating and editing the architecture of neural networks. Today we will continue working on this tool. We will try to make it more user friendly. This may see, top be a step away form our topic. But don't you think that a well organized workspace plays an important role in achieving the result.
Population optimization algorithms
This is an introductory article on optimization algorithm (OA) classification. The article attempts to create a test stand (a set of functions), which is to be used for comparing OAs and, perhaps, identifying the most universal algorithm out of all widely known ones.
Neural networks made easy (Part 23): Building a tool for Transfer Learning
In this series of articles, we have already mentioned Transfer Learning more than once. However, this was only mentioning. in this article, I suggest filling this gap and taking a closer look at Transfer Learning.
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.
Matrix and Vector operations in MQL5
Matrices and vectors have been introduced in MQL5 for efficient operations with mathematical solutions. The new types offer built-in methods for creating concise and understandable code that is close to mathematical notation. Arrays provide extensive capabilities, but there are many cases in which matrices are much more efficient.
Metamodels in machine learning and trading: Original timing of trading orders
Metamodels in machine learning: Auto creation of trading systems with little or no human intervention — The model decides when and how to trade on its own.
Developing a trading Expert Advisor from scratch (Part 17): Accessing data on the web (III)
In this article we continue considering how to obtain data from the web and to use it in an Expert Advisor. This time we will proceed to developing an alternative system.
Developing a trading Expert Advisor from scratch (Part 16): Accessing data on the web (II)
Knowing how to input data from the Web into an Expert Advisor is not so obvious. It is not so easy to do without understanding all the possibilities offered by MetaTrader 5.
Developing a trading Expert Advisor from scratch (Part 15): Accessing data on the web (I)
How to access online data via MetaTrader 5? There are a lot of websites and places on the web, featuring a huge amount information. What you need to know is where to look and how best to use this information.
DirectX Tutorial (Part I): Drawing the first triangle
It is an introductory article on DirectX, which describes specifics of operation with the API. It should help to understand the order in which its components are initialized. The article contains an example of how to write an MQL5 script which renders a triangle using DirectX.
WebSockets for MetaTrader 5 — Using the Windows API
In this article, we will use the WinHttp.dll to create a WebSocket client for MetaTrader 5 programs. The client will ultimately be implemented as a class and also tested against the Deriv.com WebSocket API.
Using AutoIt With MQL5
Short description. In this article we will explore scripting the MetraTrader 5 terminal by integrating MQL5 with AutoIt. In it we will cover how to automate various tasks by manipulating the terminals' user interface and also present a class that uses the AutoItX library.
Multilayer perceptron and backpropagation algorithm (Part II): Implementation in Python and integration with MQL5
There is a Python package available for developing integrations with MQL, which enables a plethora of opportunities such as data exploration, creation and use of machine learning models. The built in Python integration in MQL5 enables the creation of various solutions, from simple linear regression to deep learning models. Let's take a look at how to set up and prepare a development environment and how to use use some of the machine learning libraries.
Tips from a professional programmer (Part I): Code storing, debugging and compiling. Working with projects and logs
These are some tips from a professional programmer about methods, techniques and auxiliary tools which can make programming easier.
Brute force approach to pattern search (Part IV): Minimal functionality
The article presents an improved brute force version, based on the goals set in the previous article. I will try to cover this topic as broadly as possible using Expert Advisors with settings obtained using this method. A new program version is attached to this article.
Machine learning in Grid and Martingale trading systems. Would you bet on it?
This article describes the machine learning technique applied to grid and martingale trading. Surprisingly, this approach has little to no coverage in the global network. After reading the article, you will be able to create your own trading bots.
Practical application of neural networks in trading (Part 2). Computer vision
The use of computer vision allows training neural networks on the visual representation of the price chart and indicators. This method enables wider operations with the whole complex of technical indicators, since there is no need to feed them digitally into the neural network.
Brute force approach to pattern search (Part III): New horizons
This article provides a continuation to the brute force topic, and it introduces new opportunities for market analysis into the program algorithm, thereby accelerating the speed of analysis and improving the quality of results. New additions enable the highest-quality view of global patterns within this approach.
Finding seasonal patterns in the forex market using the CatBoost algorithm
The article considers the creation of machine learning models with time filters and discusses the effectiveness of this approach. The human factor can be eliminated now by simply instructing the model to trade at a certain hour of a certain day of the week. Pattern search can be provided by a separate algorithm.
Neural networks made easy (Part 9): Documenting the work
We have already passed a long way and the code in our library is becoming bigger and bigger. This makes it difficult to keep track of all connections and dependencies. Therefore, I suggest creating documentation for the earlier created code and to keep it updating with each new step. Properly prepared documentation will help us see the integrity of our work.
Brute force approach to pattern search (Part II): Immersion
In this article we will continue discussing the brute force approach. I will try to provide a better explanation of the pattern using the new improved version of my application. I will also try to find the difference in stability using different time intervals and timeframes.
How to make $1,000,000 off algorithmic trading? Use MQL5.com services!
All traders visit the market with the goal of earning their first million dollars. How to do that without excessive risk and start-up budget? MQL5 services provide such opportunity for developers and traders from around the world.
Gradient boosting in transductive and active machine learning
In this article, we will consider active machine learning methods utilizing real data, as well discuss their pros and cons. Perhaps you will find these methods useful and will include them in your arsenal of machine learning models. Transduction was introduced by Vladimir Vapnik, who is the co-inventor of the Support-Vector Machine (SVM).
Practical application of neural networks in trading. Python (Part I)
In this article, we will analyze the step-by-step implementation of a trading system based on the programming of deep neural networks in Python. This will be performed using the TensorFlow machine learning library developed by Google. We will also use the Keras library for describing neural networks.
Parallel Particle Swarm Optimization
The article describes a method of fast optimization using the particle swarm algorithm. It also presents the method implementation in MQL, which is ready for use both in single-threaded mode inside an Expert Advisor and in a parallel multi-threaded mode as an add-on that runs on local tester agents.
Continuous walk-forward optimization (Part 8): Program improvements and fixes
The program has been modified based on comments and requests from users and readers of this article series. This article contains a new version of the auto optimizer. This version implements requested features and provides other improvements, which I found when working with the program.
Advanced resampling and selection of CatBoost models by brute-force method
This article describes one of the possible approaches to data transformation aimed at improving the generalizability of the model, and also discusses sampling and selection of CatBoost models.
CatBoost machine learning algorithm from Yandex with no Python or R knowledge required
The article provides the code and the description of the main stages of the machine learning process using a specific example. To obtain the model, you do not need Python or R knowledge. Furthermore, basic MQL5 knowledge is enough — this is exactly my level. Therefore, I hope that the article will serve as a good tutorial for a broad audience, assisting those interested in evaluating machine learning capabilities and in implementing them in their programs.