Depth of Market is undoubtedly a very important element for executing fast trades, especially in High Frequency Trading (HFT) algorithms. In this series of articles, we will look at this type of trading events that can be obtained through a broker on many tradable symbols. We will start with an indicator, where you can customize the color palette, position and size of the histogram displayed directly on the chart. We will also look at how to generate BookEvent events to test the indicator under certain conditions. Other possible topics for future articles include how to store price distribution data and how to use it in a strategy tester.
The article is devoted to the metaheuristic Atmosphere Clouds Model Optimization (ACMO) algorithm, which simulates the behavior of clouds to solve optimization problems. The algorithm uses the principles of cloud generation, movement and propagation, adapting to the "weather conditions" in the solution space. The article reveals how the algorithm's meteorological simulation finds optimal solutions in a complex possibility space and describes in detail the stages of ACMO operation, including "sky" preparation, cloud birth, cloud movement, and rain concentration.
This article explores the potential of the Value at Risk (VaR) model for multi-currency portfolio optimization. Using the power of Python and the functionality of MetaTrader 5, we demonstrate how to implement VaR analysis for efficient capital allocation and position management. From theoretical foundations to practical implementation, the article covers all aspects of applying one of the most robust risk calculation systems – VaR – in algorithmic trading.
Discover practical techniques to optimize memory usage in MQL5 trading systems. Learn to build efficient, stable, and fast-performing Expert Advisors and indicators. We’ll explore how memory really works in MQL5, the common traps that slow your systems down or cause them to fail, and — most importantly — how to fix them.
The article takes a detailed look at the archery-inspired optimization algorithm, with an emphasis on using the roulette method as a mechanism for selecting promising areas for "arrows". The method allows evaluating the quality of solutions and selecting the most promising positions for further study.
In this article, we will begin to address the issue of tick excess that can impact application performance when using real data. This excess often interferes with the correct timing required to construct a one-minute bar in the appropriate window.
The article presents the original version of the Bacterial Chemotaxis Optimization (BCO) algorithm and its modified version. We will take a closer look at all the differences, with a special focus on the new version of BCOm, which simplifies the bacterial movement mechanism, reduces the dependence on positional history, and uses simpler math than the computationally heavy original version. We will also conduct the tests and summarize the results.
In this article, we will look at changes that will allow the replay/simulation system to operate more efficiently and securely. I will also not leave without attention those who want to get the most out of using classes. In addition, we will consider a specific problem in MQL5 that reduces code performance when working with classes, and explain how to solve it.
The first of a series of articles looking at the mathematics of Custom Criteria with a specific focus on non-linear functions used in Neural Networks, MQL5 code for implementation and the use of targeted and correctional offsets.
The article discusses the Tabu Search algorithm, one of the first and most well-known metaheuristic methods. We will go through the algorithm operation in detail, starting with choosing an initial solution and exploring neighboring options, with an emphasis on using a tabu list. The article covers the key aspects of the algorithm and its features.
Currently, our EA uses the database to obtain initialization strings for single instances of trading strategies. However, the database is quite large and contains a lot of information that is not needed for the actual EA operation. Let's try to ensure the EA's functionality without a mandatory connection to the database.
We have been working on just the indicators for a long time now, but now it's time to get the service working again and see how the chart is built based on the data provided. However, since the whole thing is not that simple, we will have to be attentive to understand what awaits us ahead.
Do the positions of planets and stars affect financial markets? Let's arm ourselves with statistics and big data, and embark on an exciting journey into the world where stars and stock charts intersect.
The article considers the Artificial Algae Algorithm (AAA) based on biological processes characteristic of microalgae. The algorithm includes spiral motion, evolutionary process and adaptation, which allows it to solve optimization problems. The article provides an in-depth analysis of the working principles of AAA and its potential in mathematical modeling, highlighting the connection between nature and algorithmic solutions.
We will create an indicator based on the Gann's Square of 9, built by squaring time and price. We will prepare the code and test the indicator in the platform on different time intervals.
The article is devoted to the AMO algorithm, which models the seasonal migration of animals in search of optimal conditions for life and reproduction. The main features of AMO include the use of topological neighborhood and a probabilistic update mechanism, which makes it easy to implement and flexible for various optimization tasks.
Having a proper understanding of different ideas allows us to do more with less effort. In this article, we'll look at why it's necessary to configure a template before the service can interact with the chart. Also, what if we improve the mouse pointer so we can do more things with it?
In strategy development, there are many intricate details to consider, many of which are not highlighted for beginner traders. As a result, many traders, myself included, have had to learn these lessons the hard way. This article is based on my observations of common pitfalls that most beginner traders encounter when developing strategies on MQL5. It will offer a range of tips, tricks, and examples to help identify the disqualification of an EA and test the robustness of our own EAs in an easy-to-implement way. The goal is to educate readers, helping them avoid future scams when purchasing EAs as well as preventing mistakes in their own strategy development.
In this article, we will continue exploring the Artificial Bee Hive Algorithm (ABHA) by diving into the code and considering the remaining methods. As you might remember, each bee in the model is represented as an individual agent whose behavior depends on internal and external information, as well as motivational state. We will test the algorithm on various functions and summarize the results by presenting them in the rating table.
After a break in development and improvement of the service used for replay/simulator, we are resuming work on it. Now that we've abandoned the use of resources like terminal globals, we'll have to completely restructure some parts of it. Don't worry, this process will be explained in detail so that everyone can follow the development of our service.
In this article, we will look at a non-standard way of creating an indicator in MQL5. Instead of focusing on a trend or chart pattern, our goal will be to manage our own positions, including partial entries and exits. We will make extensive use of dynamic matrices and some trading functions related to trade history and open positions to indicate on the chart where these trades were made.
One point to note: although the service code is not included in this article and will only be provided in the next one, I'll explain it since we'll be using that same code as a springboard for what we're actually developing. So, be attentive and patient. Wait for the next article, because every day everything becomes more interesting.
The EA under development is expected to show good results when trading with different brokers. But for now we have been using quotes from a MetaQuotes demo account to perform tests. Let's see if our EA is ready to work on a trading account with different quotes compared to those used during testing and optimization.
Can chaos theory be applied to financial markets? In this article, we will consider how conventional Chaos theory and chaotic systems are different from the concept proposed by Bill Williams.
As we gradually approach to obtaining a ready-made EA, we need to pay attention to issues that seem secondary at the stage of testing a trading strategy, but become important when moving on to real trading.
Although the modules already interact with each other properly, an error occurs when trying to use the mouse pointer in the replay service. We need to fix this before moving on to the next step. Additionally, we will fix an issue in the mouse indicator code. So this version will be finally stable and properly polished.
This article provides a fascinating insight into the world of social behavior in living organisms and its influence on the creation of a new mathematical model - ASBO (Adaptive Social Behavior Optimization). We will examine how the principles of leadership, neighborhood, and cooperation observed in living societies inspire the development of innovative optimization algorithms.
In this article, we will implement a control indicator so that it can be integrated into the message system we are developing. Although it is not very difficult, there are some details that need to be understood about the initialization of this module. The material presented here is for educational purposes only. In no way should it be considered as an application for any purpose other than learning and mastering the concepts shown.
The article presents an artificial electric field algorithm (AEFA) inspired by Coulomb's law of electrostatic force. The algorithm simulates electrical phenomena to solve complex optimization problems using charged particles and their interactions. AEFA exhibits unique properties in the context of other algorithms related to laws of nature.
Did you know that the Golden Cross and Death Cross strategies, based on moving average crossovers, are some of the most reliable indicators for identifying long-term market trends? A Golden Cross signals a bullish trend when a shorter moving average crosses above a longer one, while a Death Cross indicates a bearish trend when the shorter average moves below. Despite their simplicity and effectiveness, manually applying these strategies often leads to missed opportunities or delayed trades.
In this article, we will look at how to put together the first of a number of truly functional modules for use in the replay/simulator system that will also be of general purpose to serve other purposes. We are talking about the mouse module.
In the second part, we will collect chemical operators into a single algorithm and present a detailed analysis of its results. Let's find out how the Chemical reaction optimization (CRO) method copes with solving complex problems on test functions.
In this article, we'll cover an important topic that few people understand: Custom Events. Dangers. Advantages and disadvantages of these elements. This topic is key for those who want to become a professional programmer in MQL5 or any other language. Here we will focus on MQL5 and MetaTrader 5.
In the first part of this article, we will dive into the world of chemical reactions and discover a new approach to optimization! Chemical reaction optimization (CRO) uses principles derived from the laws of thermodynamics to achieve efficient results. We will reveal the secrets of decomposition, synthesis and other chemical processes that became the basis of this innovative method.
We have already implemented the first stage of the automated optimization. We perform optimization for different symbols and timeframes according to several criteria and store information about the results of each pass in the database. Now we are going to select the best groups of parameter sets from those found at the first stage.
In this article, we will look into one of the most difficult issues in the field of MQL5 programming: how to correctly obtain a chart ID, and why objects are sometimes not plotted on the chart. The materials presented here are for didactic purposes only. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
Here we will consider the evolution of the ACS algorithm: three modifications aimed at improving the convergence characteristics and the algorithm efficiency. Transformation of one of the leading optimization algorithms. From matrix modifications to revolutionary approaches regarding population formation.
We will solve the chart ID problem and at the same time we will begin to provide the user with the ability to use a personal template for the analysis and simulation of the desired asset. The materials presented here are for didactic purposes only and should in no way be considered as an application for any purpose other than studying and mastering the concepts presented.
Artificial Cooperative Search (ACS) is an innovative method using a binary matrix and multiple dynamic populations based on mutualistic relationships and cooperation to find optimal solutions quickly and accurately. ACS unique approach to predators and prey enables it to achieve excellent results in numerical optimization problems.
