Articles on how to integrate algorithmic/automated trading systems for MetaTrader 5

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 Binary.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.

Websockets for MetaTrader 5

Before the introduction of the network functionality provided with the updated MQL5 API, MetaTrader programs have been limited in their ability to connect and interface with websocket based services. But of course this has all changed, in this article we will explore the implementation of a websocket library in pure MQL5. A brief description of the websocket protocol will be given along with a step by step guide on how to use the resulting library.

Gradient Boosting (CatBoost) in the development of trading systems. A naive approach

Training the CatBoost classifier in Python and exporting the model to mql5, as well as parsing the model parameters and a custom strategy tester. The Python language and the MetaTrader 5 library are used for preparing the data and for training the model.

Using cryptography with external applications

In this article, we consider encryption/decryption of objects in MetaTrader and in external applications. Our purpose is to determine the conditions under which the same results will be obtained with the same initial data.

Calculating mathematical expressions (Part 2). Pratt and shunting yard parsers

In this article, we consider the principles of mathematical expression parsing and evaluation using parsers based on operator precedence. We will implement Pratt and shunting-yard parser, byte-code generation and calculations by this code, as well as view how to use indicators as functions in expressions and how to set up trading signals in Expert Advisors based on these indicators.

Calculating mathematical expressions (Part 1). Recursive descent parsers

The article considers the basic principles of mathematical expression parsing and calculation. We will implement recursive descent parsers operating in the interpreter and fast calculation modes, based on a pre-built syntax tree.

Practical application of neural networks in trading

In this article, we will consider the main aspects of integration of neural networks and the trading terminal, with the purpose of creating a fully featured trading robot.

MQL as a Markup Tool for the Graphical Interface of MQL Programs (Part 3). Form Designer

In this paper, we are completing the description of our concept of building the window interface of MQL programs, using the structures of MQL. Specialized graphical editor will allow to interactively set up the layout that consists of the basic classes of the GUI elements and then export it into the MQL description to use it in your MQL project. The paper presents the internal design of the editor and a user guide. Source codes are attached.

Articles on MetaTrader 5 integration using MQL5