Developing a Replay System (Part 52): Things Get Complicated (IV)
In this article, we will change the mouse pointer to enable the interaction with the control indicator to ensure reliable and stable operation.
Population optimization algorithms: Charged System Search (CSS) algorithm
In this article, we will consider another optimization algorithm inspired by inanimate nature - Charged System Search (CSS) algorithm. The purpose of this article is to present a new optimization algorithm based on the principles of physics and mechanics.
Population optimization algorithms: Artificial Multi-Social Search Objects (MSO)
This is a continuation of the previous article considering the idea of social groups. The article explores the evolution of social groups using movement and memory algorithms. The results will help to understand the evolution of social systems and apply them in optimization and search for solutions.
Successful Restaurateur Algorithm (SRA)
Successful Restaurateur Algorithm (SRA) is an innovative optimization method inspired by restaurant business management principles. Unlike traditional approaches, SRA does not discard weak solutions, but improves them by combining with elements of successful ones. The algorithm shows competitive results and offers a fresh perspective on balancing exploration and exploitation in optimization problems.
Bacterial Chemotaxis Optimization (BCO)
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.
Developing a Replay System (Part 64): Playing the service (V)
In this article, we will look at how to fix two errors in the code. However, I will try to explain them in a way that will help you, beginner programmers, understand that things don't always go as you expect. Anyway, this is an opportunity to learn. The content presented here is intended solely for educational purposes. In no way should this application be considered as a final document with any purpose other than to explore the concepts presented.
Population optimization algorithms: Micro Artificial immune system (Micro-AIS)
The article considers an optimization method based on the principles of the body's immune system - Micro Artificial Immune System (Micro-AIS) - a modification of AIS. Micro-AIS uses a simpler model of the immune system and simple immune information processing operations. The article also discusses the advantages and disadvantages of Micro-AIS compared to conventional AIS.
Developing a Replay System (Part 30): Expert Advisor project — C_Mouse class (IV)
Today we will learn a technique that can help us a lot in different stages of our professional life as a programmer. Often it is not the platform itself that is limited, but the knowledge of the person who talks about the limitations. This article will tell you that with common sense and creativity you can make the MetaTrader 5 platform much more interesting and versatile without resorting to creating crazy programs or anything like that, and create simple yet safe and reliable code. We will use our creativity to modify existing code without deleting or adding a single line to the source code.
Population optimization algorithms: Binary Genetic Algorithm (BGA). Part I
In this article, we will explore various methods used in binary genetic and other population algorithms. We will look at the main components of the algorithm, such as selection, crossover and mutation, and their impact on the optimization. In addition, we will study data presentation methods and their impact on optimization results.
MQL5 Wizard Techniques you should know (Part 18): Neural Architecture Search with Eigen Vectors
Neural Architecture Search, an automated approach at determining the ideal neural network settings can be a plus when facing many options and large test data sets. We examine how when paired Eigen Vectors this process can be made even more efficient.
Market Simulation (Part 03): A Matter of Performance
Often we have to take a step back and then move forward. In this article, we will show all the changes necessary to ensure that the Mouse and Chart Trade indicators do not break. As a bonus, we'll also cover other changes that have occurred in other header files that will be widely used in the future.
Artificial Showering Algorithm (ASHA)
The article presents the Artificial Showering Algorithm (ASHA), a new metaheuristic method developed for solving general optimization problems. Based on simulation of water flow and accumulation processes, this algorithm constructs the concept of an ideal field, in which each unit of resource (water) is called upon to find an optimal solution. We will find out how ASHA adapts flow and accumulation principles to efficiently allocate resources in a search space, and see its implementation and test results.
Arithmetic Optimization Algorithm (AOA): From AOA to SOA (Simple Optimization Algorithm)
In this article, we present the Arithmetic Optimization Algorithm (AOA) based on simple arithmetic operations: addition, subtraction, multiplication and division. These basic mathematical operations serve as the foundation for finding optimal solutions to various problems.
Market Simulation (Part 07): Sockets (I)
Sockets. Do you know what they are for or how to use them in MetaTrader 5? If the answer is no, let's start by studying them. In today's article, we'll cover the basics. Since there are several ways to do the same thing, and we are always interested in the result, I want to show that there is indeed a simple way to transfer data from MetaTrader 5 to other programs, such as Excel. However, the main idea is not to transfer data from MetaTrader 5 to Excel, but the opposite, that is, to transfer data from Excel or any other program to MetaTrader 5.
Developing a Replay System (Part 57): Understanding a Test Service
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.
Developing a Replay System (Part 60): Playing the Service (I)
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.
Market Simulation (Part 05): Creating the C_Orders Class (II)
In this article, I will explain how Chart Trade, together with the Expert Advisor, will process a request to close all of the users' open positions. This may sound simple, but there are a few complications that you need to know how to manage.
MQL5 Wizard Techniques you should know (Part 23): CNNs
Convolutional Neural Networks are another machine learning algorithm that tend to specialize in decomposing multi-dimensioned data sets into key constituent parts. We look at how this is typically achieved and explore a possible application for traders in another MQL5 wizard signal class.
Bivariate Copulae in MQL5 (Part 1): Implementing Gaussian and Student's t-Copulae for Dependency Modeling
This is the first part of an article series presenting the implementation of bivariate copulae in MQL5. This article presents code implementing Gaussian and Student's t-copulae. It also delves into the fundamentals of statistical copulae and related topics. The code is based on the Arbitragelab Python package by Hudson and Thames.
Atmosphere Clouds Model Optimization (ACMO): Theory
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.
Developing a Replay System (Part 45): Chart Trade Project (IV)
The main purpose of this article is to introduce and explain the C_ChartFloatingRAD class. We have a Chart Trade indicator that works in a rather interesting way. As you may have noticed, we still have a fairly small number of objects on the chart, and yet we get the expected functionality. The values present in the indicator can be edited. The question is, how is this possible? This article will start to make things clearer.
Developing a Replay System (Part 55): Control Module
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.
Developing a Replay System (Part 35): Making Adjustments (I)
Before we can move forward, we need to fix a few things. These are not actually the necessary fixes but rather improvements to the way the class is managed and used. The reason is that failures occurred due to some interaction within the system. Despite attempts to find out the cause of such failures in order to eliminate them, all these attempts were unsuccessful. Some of these cases make no sense, for example, when we use pointers or recursion in C/C++, the program crashes.
Population optimization algorithms: Bacterial Foraging Optimization - Genetic Algorithm (BFO-GA)
The article presents a new approach to solving optimization problems by combining ideas from bacterial foraging optimization (BFO) algorithms and techniques used in the genetic algorithm (GA) into a hybrid BFO-GA algorithm. It uses bacterial swarming to globally search for an optimal solution and genetic operators to refine local optima. Unlike the original BFO, bacteria can now mutate and inherit genes.
Atmosphere Clouds Model Optimization (ACMO): Practice
In this article, we will continue diving into the implementation of the ACMO (Atmospheric Cloud Model Optimization) algorithm. In particular, we will discuss two key aspects: the movement of clouds into low-pressure regions and the rain simulation, including the initialization of droplets and their distribution among clouds. We will also look at other methods that play an important role in managing the state of clouds and ensuring their interaction with the environment.
Developing a Replay System (Part 39): Paving the Path (III)
Before we proceed to the second stage of development, we need to revise some ideas. Do you know how to make MQL5 do what you need? Have you ever tried to go beyond what is contained in the documentation? If not, then get ready. Because we will be doing something that most people don't normally do.
Developing a Replay System (Part 41): Starting the second phase (II)
If everything seemed right to you up to this point, it means you're not really thinking about the long term, when you start developing applications. Over time you will no longer need to program new applications, you will just have to make them work together. So let's see how to finish assembling the mouse indicator.
Developing a Replay System (Part 34): Order System (III)
In this article, we will complete the first phase of construction. Although this part is fairly quick to complete, I will cover details that were not discussed previously. I will explain some points that many do not understand. Do you know why you have to press the Shift or Ctrl key?
Neuroboids Optimization Algorithm (NOA)
A new bioinspired optimization metaheuristic, NOA (Neuroboids Optimization Algorithm), combines the principles of collective intelligence and neural networks. Unlike conventional methods, the algorithm uses a population of self-learning "neuroboids", each with its own neural network that adapts its search strategy in real time. The article reveals the architecture of the algorithm, the mechanisms of self-learning of agents, and the prospects for applying this hybrid approach to complex optimization problems.
Developing a multi-currency Expert Advisor (Part 24): Adding a new strategy (II)
In this article, we will continue to connect the new strategy to the created auto optimization system. Let's look at what changes need to be made to the optimization project creation EA, as well as the second and third stage EAs.
Price-Driven CGI Model: Advanced Data Post-Processing and Implementation
In this article, we will explore the development of a fully customizable Price Data export script using MQL5, marking new advancements in the simulation of the Price Man CGI Model. We have implemented advanced refinement techniques to ensure that the data is user-friendly and optimized for animation purposes. Additionally, we will uncover the capabilities of Blender 3D in effectively working with and visualizing price data, demonstrating its potential for creating dynamic and engaging animations.
Population optimization algorithms: Resistance to getting stuck in local extrema (Part I)
This article presents a unique experiment that aims to examine the behavior of population optimization algorithms in the context of their ability to efficiently escape local minima when population diversity is low and reach global maxima. Working in this direction will provide further insight into which specific algorithms can successfully continue their search using coordinates set by the user as a starting point, and what factors influence their success.
Developing a Replay System (Part 56): Adapting the Modules
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.
Developing a Replay System (Part 63): Playing the service (IV)
In this article, we will finally solve the problems with the simulation of ticks on a one-minute bar so that they can coexist with real ticks. This will help us avoid problems in the future. The material presented here is for educational purposes only. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
Developing a Replay System (Part 58): Returning to Work on the Service
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.
Markets Positioning Codex in MQL5 (Part 2): Bitwise Learning, with Multi-Patterns for Nvidia
We continue our new series on Market-Positioning, where we study particular assets, with specific trade directions over manageable test windows. We started this by considering Nvidia Corp stock in the last article, where we covered 5 signal patterns from the complimentary pairing of the RSI and DeMarker oscillators. For this article, we cover the remaining 5 patterns and also delve into multi-pattern options that not only feature untethered combinations of all ten, but also specialized combinations of just a pair.
Python-MetaTrader 5 Strategy Tester (Part 02): Dealing with Bars, Ticks, and Overloading Built-in Functions in a Simulator
In this article, we introduce functions similar to those provided by the Python-MetaTrader 5 module, providing a simulator with a familiar interface and a custom way of handling bars and ticks internally.
Developing Market Memory Zones Indicator: Where Price Is Likely To Return
In this discussion, we will develop an indicator to identify price zones created by strong market activity, such as impulsive moves, structure shifts, and liquidity events. These zones represent areas where the market has left “memory” due to unfilled orders or rapid price displacement. By marking these regions on the chart, the indicator highlights where price is statistically more likely to revisit and react in the future.