Articles on strategy testing in MQL5
How to develop, write and test a trading strategy, how to find the optimal system parameters and how to analyze the results? The MetaTrader platform offers developers of trading robots rich functionality for fast and accurate testing of trading ideas. Read these articles to learn how to test multi-currency robots and how to use MQL5 Cloud Network for optimization purposes.
Developers of automated trading systems are recommended to start with the testing fundamentals and tick generation algorithms in the strategy tester.
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William Gann methods (Part II): Creating Gann Square indicator
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.
Animal Migration Optimization (AMO) algorithm
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.
Developing a Replay System (Part 59): A New Future
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?
Robustness Testing on Expert Advisors
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.
Artificial Bee Hive Algorithm (ABHA): Tests and results
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.
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.
Custom Indicator: Plotting Partial Entry, Exit and Reversal Deals for Netting Accounts
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.
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 multi-currency Expert Advisor (Part 16): Impact of different quote histories on test results
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.
Chaos theory in trading (Part 1): Introduction, application in financial markets and Lyapunov exponent
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.
Developing a multi-currency Expert Advisor (Part 15): Preparing EA for real trading
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.
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.
Adaptive Social Behavior Optimization (ASBO): Schwefel, Box-Muller Method
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.
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.
Artificial Electric Field Algorithm (AEFA)
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.
Building a Candlestick Trend Constraint Model (Part 10): Strategic Golden and Death Cross (EA)
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. By automating them within the Trend Constraint EA using MQL5, these strategies can operate independently to handle market reversals efficiently, while constrained strategies align with broader trends. This approach revolutionizes performance by ensuring precise execution and seamless integration of reversal and trend-following systems.
Developing a Replay System (Part 54): The Birth of the First Module
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.
Chemical reaction optimization (CRO) algorithm (Part II): Assembling and results
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.
Developing a Replay System (Part 53): Things Get Complicated (V)
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.
Chemical reaction optimization (CRO) algorithm (Part I): Process chemistry in optimization
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.
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.
Developing a multi-currency Expert Advisor (Part 13): Automating the second stage — selection into groups
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.
Developing a Replay System (Part 51): Things Get Complicated (III)
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.
Most notable Artificial Cooperative Search algorithm modifications (ACSm)
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.
Developing a Replay System (Part 50): Things Get Complicated (II)
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) algorithm
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.
MQL5 Wizard Techniques you should know (Part 44): Average True Range (ATR) technical indicator
The ATR oscillator is a very popular indicator for acting as a volatility proxy, especially in the forex markets where volume data is scarce. We examine this, on a pattern basis as we have with prior indicators, and share strategies & test reports thanks to the MQL5 wizard library classes and assembly.
Developing a Replay System (Part 49): Things Get Complicated (I)
In this article, we'll complicate things a little. Using what was shown in the previous articles, we will start to open up the template file so that the user can use their own template. However, I will be making changes gradually, as I will also be refining the indicator to reduce the load on MetaTrader 5.
Developing a Replay System (Part 48): Understanding the concept of a service
How about learning something new? In this article, you will learn how to convert scripts into services and why it is useful to do so.
Developing a Replay System (Part 47): Chart Trade Project (VI)
Finally, our Chart Trade indicator starts interacting with the EA, allowing information to be transferred interactively. Therefore, in this article, we will improve the indicator, making it functional enough to be used together with any EA. This will allow us to access the Chart Trade indicator and work with it as if it were actually connected with an EA. But we will do it in a much more interesting way than before.
Developing a robot in Python and MQL5 (Part 2): Model selection, creation and training, Python custom tester
We continue the series of articles on developing a trading robot in Python and MQL5. Today we will solve the problem of selecting and training a model, testing it, implementing cross-validation, grid search, as well as the problem of model ensemble.
Developing a multi-currency Expert Advisor (Part 11): Automating the optimization (first steps)
To get a good EA, we need to select multiple good sets of parameters of trading strategy instances for it. This can be done manually by running optimization on different symbols and then selecting the best results. But it is better to delegate this work to the program and engage in more productive activities.
Developing a Replay System (Part 46): Chart Trade Project (V)
Tired of wasting time searching for that very file that you application needs in order to work? How about including everything in the executable? This way you won't have to search for the things. I know that many people use this form of distribution and storage, but there is a much more suitable way. At least as far as the distribution of executable files and their storage is concerned. The method that will be presented here can be very useful, since you can use MetaTrader 5 itself as an excellent assistant, as well as MQL5. Furthermore, it is not that difficult to understand.
MQL5 Wizard Techniques you should know (Part 37): Gaussian Process Regression with Linear and Matérn Kernels
Linear Kernels are the simplest matrix of its kind used in machine learning for linear regression and support vector machines. The Matérn kernel on the other hand is a more versatile version of the Radial Basis Function we looked at in an earlier article, and it is adept at mapping functions that are not as smooth as the RBF would assume. We build a custom signal class that utilizes both kernels in forecasting long and short conditions.
Developing a multi-currency Expert Advisor (Part 9): Collecting optimization results for single trading strategy instances
Let's outline the main stages of the EA development. One of the first things to be done will be to optimize a single instance of the developed trading strategy. Let's try to collect all the necessary information about the tester passes during the optimization in one place.
Introduction to MQL5 (Part 9): Understanding and Using Objects in MQL5
Learn to create and customize chart objects in MQL5 using current and historical data. This project-based guide helps you visualize trades and apply MQL5 concepts practically, making it easier to build tools tailored to your trading needs.
Building A Candlestick Trend Constraint Model (Part 8): Expert Advisor Development (II)
Think about an independent Expert Advisor. Previously, we discussed an indicator-based Expert Advisor that also partnered with an independent script for drawing risk and reward geometry. Today, we will discuss the architecture of an MQL5 Expert Advisor, that integrates, all the features in one program.
Developing a multi-currency Expert Advisor (Part 8): Load testing and handling a new bar
As we progressed, we used more and more simultaneously running instances of trading strategies in one EA. Let's try to figure out how many instances we can get to before we hit resource limitations.
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 44): Chart Trade Project (III)
In the previous article I explained how you can manipulate template data for use in OBJ_CHART. In that article, I only outlined the topic without going into details, since in that version the work was done in a very simplified way. This was done to make it easier to explain the content, because despite the apparent simplicity of many things, some of them were not so obvious, and without understanding the simplest and most basic part, you would not be able to truly understand the entire picture.