Add a new article
Learn how to design different Moving Average systems
Learn how to design different Moving Average systems
There are many strategies that can be used to filter generated signals based on any strategy, even by using the moving average itself which is the subject of this article. So, the objective of this article is to share with you some of Moving Average Strategies and how to design an algorithmic trading system.
The correct way to choose an Expert Advisor from the Market
The correct way to choose an Expert Advisor from the Market
In this article, we will consider some of the essential points you should pay attention to when purchasing an Expert Advisor. We will also look for ways to increase profit, to spend money wisely, and to earn from this spending. Also, after reading the article, you will see that it is possible to earn even using simple and free products.
Developing a trading Expert Advisor from scratch
Developing a trading Expert Advisor from scratch
In this article, we will discuss how to develop a trading robot with minimum programming. Of course, MetaTrader 5 provides a high level of control over trading positions. However, using only the manual ability to place orders can be quite difficult and risky for less experienced users.
Advanced EA constructor for MetaTrader - botbrains.app
Advanced EA constructor for MetaTrader - botbrains.app
In this article, we demonstrate features of botbrains.app - a no-code platform for trading robots development. To create a trading robot you don't need to write any code - just drag and drop the necessary blocks onto the scheme, set their parameters, and establish connections between them.
How to Order an Expert Advisor and Obtain the Desired Result
How to Order an Expert Advisor and Obtain the Desired Result
How to write correctly the Requirement Specifications? What should and should not be expected from a programmer when ordering an Expert Advisor or an indicator? How to keep a dialog, what moments to pay special attention to? This article gives the answers to these, as well as to many other questions, which often don't seem obvious to many people.
Manual charting and trading toolkit (Part III). Optimization and new tools
Manual charting and trading toolkit (Part III). Optimization and new tools
In this article, we will further develop the idea of drawing graphical objects on charts using keyboard shortcuts. New tools have been added to the library, including a straight line plotted through arbitrary vertices, and a set of rectangles that enable the evaluation of the reversal time and level. Also, the article shows the possibility to optimize code for improved performance. The implementation example has been rewritten, allowing the use of Shortcuts alongside other trading programs. Required code knowledge level: slightly higher than a beginner.
Swaps (Part I): Locking and Synthetic Positions
Swaps (Part I): Locking and Synthetic Positions
In this article I will try to expand the classic concept of swap trading methods. I will explain why I have come to the conclusion that this concept deserves special attention and is absolutely recommended for study.
Patterns with Examples (Part I): Multiple Top
Patterns with Examples (Part I): Multiple Top
This is the first article in a series related to reversal patterns in the framework of algorithmic trading. We will begin with the most interesting pattern family, which originate from the Double Top and Double Bottom patterns.
MVC design pattern and its possible application
MVC design pattern and its possible application
The article discusses a popular MVC pattern, as well as the possibilities, pros and cons of its usage in MQL programs. The idea is to split an existing code into three separate components: Model, View and Controller.
Developing a self-adapting algorithm (Part II): Improving efficiency
Developing a self-adapting algorithm (Part II): Improving efficiency
In this article, I will continue the development of the topic by improving the flexibility of the previously created algorithm. The algorithm became more stable with an increase in the number of candles in the analysis window or with an increase in the threshold percentage of the overweight of falling or growing candles. I had to make a compromise and set a larger sample size for analysis or a larger percentage of the prevailing candle excess.
Self-adapting algorithm (Part IV): Additional functionality and tests
Self-adapting algorithm (Part IV): Additional functionality and tests
I continue filling the algorithm with the minimum necessary functionality and testing the results. The profitability is quite low but the articles demonstrate the model of the fully automated profitable trading on completely different instruments traded on fundamentally different markets.
Neural networks made easy (Part 11): A take on GPT
Neural networks made easy (Part 11): A take on GPT
Perhaps one of the most advanced models among currently existing language neural networks is GPT-3, the maximal variant of which contains 175 billion parameters. Of course, we are not going to create such a monster on our home PCs. However, we can view which architectural solutions can be used in our work and how we can benefit from them.
Self-adapting algorithm (Part III): Abandoning optimization
Self-adapting algorithm (Part III): Abandoning optimization
It is impossible to get a truly stable algorithm if we use optimization based on historical data to select parameters. A stable algorithm should be aware of what parameters are needed when working on any trading instrument at any time. It should not forecast or guess, it should know for sure.
Area method
Area method
The "area method" trading system works based on unusual interpretation of the RSI oscillator readings. The indicator that visualizes the area method, and the Expert Advisor that trades using this system are detailed here. The article is also supplemented with detailed findings of testing the Expert Advisor for various symbols, time frames and values of the area.
Neural networks made easy (Part 10): Multi-Head Attention
Neural networks made easy (Part 10): Multi-Head Attention
We have previously considered the mechanism of self-attention in neural networks. In practice, modern neural network architectures use several parallel self-attention threads to find various dependencies between the elements of a sequence. Let us consider the implementation of such an approach and evaluate its impact on the overall network performance.
Finding seasonal patterns in the forex market using the CatBoost algorithm
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.
The market and the physics of its global patterns
The market and the physics of its global patterns
In this article, I will try to test the assumption that any system with even a small understanding of the market can operate on a global scale. I will not invent any theories or patterns, but I will only use known facts, gradually translating these facts into the language of mathematical analysis.
Developing a self-adapting algorithm (Part I): Finding a basic pattern
Developing a self-adapting algorithm (Part I): Finding a basic pattern
In the upcoming series of articles, I will demonstrate the development of self-adapting algorithms considering most market factors, as well as show how to systematize these situations, describe them in logic and take them into account in your trading activity. I will start with a very simple algorithm that will gradually acquire theory and evolve into a very complex project.
Manual charting and trading toolkit (Part II). Chart graphics drawing tools
Manual charting and trading toolkit (Part II). Chart graphics drawing tools
This is the next article within the series, in which I show how I created a convenient library for manual application of chart graphics by utilizing keyboard shortcuts. The tools used include straight lines and their combinations. In this part, we will view how the drawing tools are applied using the functions described in the first part. The library can be connected to any Expert Advisor or indicator which will greatly simplify the charting tasks. This solution DOES NOT use external dlls, while all the commands are implemented using built-in MQL tools.
Neural networks made easy (Part 7): Adaptive optimization methods
Neural networks made easy (Part 7): Adaptive optimization methods
In previous articles, we used stochastic gradient descent to train a neural network using the same learning rate for all neurons within the network. In this article, I propose to look towards adaptive learning methods which enable changing of the learning rate for each neuron. We will also consider the pros and cons of this approach.
Grid and martingale: what are they and how to use them?
Grid and martingale: what are they and how to use them?
In this article, I will try to explain in detail what grid and martingale are, as well as what they have in common. Besides, I will try to analyze how viable these strategies really are. The article features mathematical and practical sections.
Gradient boosting in transductive and active machine learning
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).
A scientific approach to the development of trading algorithms
A scientific approach to the development of trading algorithms
The article considers the methodology for developing trading algorithms, in which a consistent scientific approach is used to analyze possible price patterns and to build trading algorithms based on these patterns. Development ideals are demonstrated using examples.
Practical application of neural networks in trading. Python (Part I)
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.
Neural networks made easy (Part 5): Multithreaded calculations in OpenCL
Neural networks made easy (Part 5): Multithreaded calculations in OpenCL
We have earlier discussed some types of neural network implementations. In the considered networks, the same operations are repeated for each neuron. A logical further step is to utilize multithreaded computing capabilities provided by modern technology in an effort to speed up the neural network learning process. One of the possible implementations is described in this article.
Neural networks made easy (Part 4): Recurrent networks
Neural networks made easy (Part 4): Recurrent networks
We continue studying the world of neural networks. In this article, we will consider another type of neural networks, recurrent networks. This type is proposed for use with time series, which are represented in the MetaTrader 5 trading platform by price charts.
Basic math behind Forex trading
Basic math behind Forex trading
The article aims to describe the main features of Forex trading as simply and quickly as possible, as well as share some basic ideas with beginners. It also attempts to answer the most tantalizing questions in the trading community along with showcasing the development of a simple indicator.
Brute force approach to pattern search
Brute force approach to pattern search
In this article, we will search for market patterns, create Expert Advisors based on the identified patterns, and check how long these patterns remain valid, if they ever retain their validity.
Neural networks made easy (Part 3): Convolutional networks
Neural networks made easy (Part 3): Convolutional networks
As a continuation of the neural network topic, I propose considering convolutional neural networks. This type of neural network are usually applied to analyzing visual imagery. In this article, we will consider the application of these networks in the financial markets.
CatBoost machine learning algorithm from Yandex with no Python or R knowledge required
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.
Neural networks made easy (Part 2): Network training and testing
Neural networks made easy (Part 2): Network training and testing
In this second article, we will continue to study neural networks and will consider an example of using our created CNet class in Expert Advisors. We will work with two neural network models, which show similar results both in terms of training time and prediction accuracy.