Multiple indicators on one chart (Part 06): Turning MetaTrader 5 into a RAD system (II)
Multiple indicators on one chart (Part 06): Turning MetaTrader 5 into a RAD system (II)
In my previous article, I showed you how to create a Chart Trade using MetaTrader 5 objects and thus to turn the platform into a RAD system. The system works very well, and for sure many of the readers might have thought about creating a library, which would allow having extended functionality in the proposed system. Based on this, it would be possible to develop a more intuitive Expert Advisor with a nicer and easier to use interface.
Learn how to design a trading system by Momentum
Learn how to design a trading system by Momentum
In my previous article, I mentioned the importance of identifying the trend which is the direction of prices. In this article I will share one of the most important concepts and indicators which is the Momentum indicator. I will share how to design a trading system based on this Momentum indicator.
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.
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.
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.
Useful and exotic techniques for automated trading
Useful and exotic techniques for automated trading
In this article I will demonstrate some very interesting and useful techniques for automated trading. Some of them may be familiar to you. I will try to cover the most interesting methods and will explain why they are worth using. Furthermore, I will show what these techniques are apt to in practice. We will create Expert Advisors and test all the described techniques using historic quotes.
Brute force approach to pattern search (Part III): New horizons
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.
Brute force approach to pattern search (Part II): Immersion
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.
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.
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.
How to Order a Trading Robot in MQL5 and MQL4
How to Order a Trading Robot in MQL5 and MQL4
"Freelance" is the largest freelance service for ordering MQL4/MQL5 trading robots and technical indicators. Hundreds of professional developers are ready to develop a custom trading application for the MetaTrader 4/5 terminal.
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.
Neural networks made easy (Part 8): Attention mechanisms
Neural networks made easy (Part 8): Attention mechanisms
In previous articles, we have already tested various options for organizing neural networks. We also considered convolutional networks borrowed from image processing algorithms. In this article, I suggest considering Attention Mechanisms, the appearance of which gave impetus to the development of language models.
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.
Neural networks made easy (Part 6): Experimenting with the neural network learning rate
Neural networks made easy (Part 6): Experimenting with the neural network learning rate
We have previously considered various types of neural networks along with their implementations. In all cases, the neural networks were trained using the gradient decent method, for which we need to choose a learning rate. In this article, I want to show the importance of a correctly selected rate and its impact on the neural network training, using examples.
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.
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.
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.
Parallel Particle Swarm Optimization
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.
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.
Custom symbols: Practical basics
Custom symbols: Practical basics
The article is devoted to the programmatic generation of custom symbols which are used to demonstrate some popular methods for displaying quotes. It describes a suggested variant of minimally invasive adaptation of Expert Advisors for trading a real symbol from a derived custom symbol chart. MQL source codes are attached to this article.