Articles on trading system automation in MQL5

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Read articles on the trading systems with a wide variety of ideas at the core. Learn how to use statistical methods and patterns on candlestick charts, how to filter signals and where to use semaphore indicators.

The MQL5 Wizard will help you create robots without programming to quickly check your trading ideas. Use the Wizard to learn about genetic algorithms.

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Developing a Replay System (Part 27): Expert Advisor project — C_Mouse class (I)

Developing a Replay System (Part 27): Expert Advisor project — C_Mouse class (I)

In this article we will implement the C_Mouse class. It provides the ability to program at the highest level. However, talking about high-level or low-level programming languages is not about including obscene words or jargon in the code. It's the other way around. When we talk about high-level or low-level programming, we mean how easy or difficult the code is for other programmers to understand.
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Developing a Replay System (Part 26): Expert Advisor project — C_Terminal class

Developing a Replay System (Part 26): Expert Advisor project — C_Terminal class

We can now start creating an Expert Advisor for use in the replay/simulation system. However, we need something improved, not a random solution. Despite this, we should not be intimidated by the initial complexity. It's important to start somewhere, otherwise we end up ruminating about the difficulty of a task without even trying to overcome it. That's what programming is all about: overcoming obstacles through learning, testing, and extensive research.
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MQL5 Wizard Techniques you should know (Part 12): Newton Polynomial

MQL5 Wizard Techniques you should know (Part 12): Newton Polynomial

Newton’s polynomial, which creates quadratic equations from a set of a few points, is an archaic but interesting approach at looking at a time series. In this article we try to explore what aspects could be of use to traders from this approach as well as address its limitations.
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Developing a Replay System — Market simulation (Part 25): Preparing for the next phase

Developing a Replay System — Market simulation (Part 25): Preparing for the next phase

In this article, we complete the first phase of developing our replay and simulation system. Dear reader, with this achievement I confirm that the system has reached an advanced level, paving the way for the introduction of new functionality. The goal is to enrich the system even further, turning it into a powerful tool for research and development of market analysis.
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Developing a Replay System — Market simulation (Part 24): FOREX (V)

Developing a Replay System — Market simulation (Part 24): FOREX (V)

Today we will remove a limitation that has been preventing simulations based on the Last price and will introduce a new entry point specifically for this type of simulation. The entire operating mechanism will be based on the principles of the forex market. The main difference in this procedure is the separation of Bid and Last simulations. However, it is important to note that the methodology used to randomize the time and adjust it to be compatible with the C_Replay class remains identical in both simulations. This is good because changes in one mode lead to automatic improvements in the other, especially when it comes to handling time between ticks.
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Developing a Replay System — Market simulation (Part 23): FOREX (IV)

Developing a Replay System — Market simulation (Part 23): FOREX (IV)

Now the creation occurs at the same point where we converted ticks into bars. This way, if something goes wrong during the conversion process, we will immediately notice the error. This is because the same code that places 1-minute bars on the chart during fast forwarding is also used for the positioning system to place bars during normal performance. In other words, the code that is responsible for this task is not duplicated anywhere else. This way we get a much better system for both maintenance and improvement.
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Neural networks are easy (Part 59): Dichotomy of Control (DoC)

Neural networks are easy (Part 59): Dichotomy of Control (DoC)

In the previous article, we got acquainted with the Decision Transformer. But the complex stochastic environment of the foreign exchange market did not allow us to fully implement the potential of the presented method. In this article, I will introduce an algorithm that is aimed at improving the performance of algorithms in stochastic environments.
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Developing a Replay System — Market simulation (Part 22): FOREX (III)

Developing a Replay System — Market simulation (Part 22): FOREX (III)

Although this is the third article on this topic, I must explain for those who have not yet understood the difference between the stock market and the foreign exchange market: the big difference is that in the Forex there is no, or rather, we are not given information about some points that actually occurred during the course of trading.
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Developing a Replay System — Market simulation (Part 21): FOREX (II)

Developing a Replay System — Market simulation (Part 21): FOREX (II)

We will continue to build a system for working in the FOREX market. In order to solve this problem, we must first declare the loading of ticks before loading the previous bars. This solves the problem, but at the same time forces the user to follow some structure in the configuration file, which, personally, does not make much sense to me. The reason is that by designing a program that is responsible for analyzing and executing what is in the configuration file, we can allow the user to declare the elements he needs in any order.
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Building and testing Keltner Channel trading systems

Building and testing Keltner Channel trading systems

In this article, we will try to provide trading systems using a very important concept in the financial market which is volatility. We will provide a trading system based on the Keltner Channel indicator after understanding it and how we can code it and how we can create a trading system based on a simple trading strategy and then test it on different assets.
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Pair trading

Pair trading

In this article, we will consider pair trading, namely what its principles are and if there are any prospects for its practical application. We will also try to create a pair trading strategy.
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Building and testing Aroon Trading Systems

Building and testing Aroon Trading Systems

In this article, we will learn how we can build an Aroon trading system after learning the basics of the indicators and the needed steps to build a trading system based on the Aroon indicator. After building this trading system, we will test it to see if it can be profitable or needs more optimization.
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Modified Grid-Hedge EA in MQL5 (Part II): Making a Simple Grid EA

Modified Grid-Hedge EA in MQL5 (Part II): Making a Simple Grid EA

In this article, we explored the classic grid strategy, detailing its automation using an Expert Advisor in MQL5 and analyzing initial backtest results. We highlighted the strategy's need for high holding capacity and outlined plans for optimizing key parameters like distance, takeProfit, and lot sizes in future installments. The series aims to enhance trading strategy efficiency and adaptability to different market conditions.
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Algorithmic Trading With MetaTrader 5 And R For Beginners

Algorithmic Trading With MetaTrader 5 And R For Beginners

Embark on a compelling exploration where financial analysis meets algorithmic trading as we unravel the art of seamlessly uniting R and MetaTrader 5. This article is your guide to bridging the realms of analytical finesse in R with the formidable trading capabilities of MetaTrader 5.
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MQL5 Wizard Techniques you should know (Part 10). The Unconventional RBM

MQL5 Wizard Techniques you should know (Part 10). The Unconventional RBM

Restrictive Boltzmann Machines are at the basic level, a two-layer neural network that is proficient at unsupervised classification through dimensionality reduction. We take its basic principles and examine if we were to re-design and train it unorthodoxly, we could get a useful signal filter.
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Building Your First Glass-box Model Using Python And MQL5

Building Your First Glass-box Model Using Python And MQL5

Machine learning models are difficult to interpret and understanding why our models deviate from our expectations is critical if we want to gain any value from using such advanced techniques. Without comprehensive insight into the inner workings of our model, we might fail to spot bugs that are corrupting our model's performance, we may waste time over engineering features that aren't predictive and in the long run we risk underutilizing the power of these models. Fortunately, there is a sophisticated and well maintained all in one solution that allows us to see exactly what our model is doing underneath the hood.
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Neural networks made easy (Part 56): Using nuclear norm to drive research

Neural networks made easy (Part 56): Using nuclear norm to drive research

The study of the environment in reinforcement learning is a pressing problem. We have already looked at some approaches previously. In this article, we will have a look at yet another method based on maximizing the nuclear norm. It allows agents to identify environmental states with a high degree of novelty and diversity.
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Design Patterns in software development and MQL5 (Part 4): Behavioral Patterns 2

Design Patterns in software development and MQL5 (Part 4): Behavioral Patterns 2

In this article, we will complete our series about the Design Patterns topic, we mentioned that there are three types of design patterns creational, structural, and behavioral. We will complete the remaining patterns of the behavioral type which can help set the method of interaction between objects in a way that makes our code clean.
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Data Science and Machine Learning (Part 17): Money in the Trees? The Art and Science of Random Forests in Forex Trading

Data Science and Machine Learning (Part 17): Money in the Trees? The Art and Science of Random Forests in Forex Trading

Discover the secrets of algorithmic alchemy as we guide you through the blend of artistry and precision in decoding financial landscapes. Unearth how Random Forests transform data into predictive prowess, offering a unique perspective on navigating the complex terrain of stock markets. Join us on this journey into the heart of financial wizardry, where we demystify the role of Random Forests in shaping market destiny and unlocking the doors to lucrative opportunities
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Developing a Replay System — Market simulation (Part 20): FOREX (I)

Developing a Replay System — Market simulation (Part 20): FOREX (I)

The initial goal of this article is not to cover all the possibilities of Forex trading, but rather to adapt the system so that you can perform at least one market replay. We'll leave simulation for another moment. However, if we don't have ticks and only bars, with a little effort we can simulate possible trades that could happen in the Forex market. This will be the case until we look at how to adapt the simulator. An attempt to work with Forex data inside the system without modifying it leads to a range of errors.
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Data Science and Machine Learning (Part 16): A Refreshing Look at Decision Trees

Data Science and Machine Learning (Part 16): A Refreshing Look at Decision Trees

Dive into the intricate world of decision trees in the latest installment of our Data Science and Machine Learning series. Tailored for traders seeking strategic insights, this article serves as a comprehensive recap, shedding light on the powerful role decision trees play in the analysis of market trends. Explore the roots and branches of these algorithmic trees, unlocking their potential to enhance your trading decisions. Join us for a refreshing perspective on decision trees and discover how they can be your allies in navigating the complexities of financial markets.
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Developing a Replay System — Market simulation (Part 19): Necessary adjustments

Developing a Replay System — Market simulation (Part 19): Necessary adjustments

Here we will prepare the ground so that if we need to add new functions to the code, this will happen smoothly and easily. The current code cannot yet cover or handle some of the things that will be necessary to make meaningful progress. We need everything to be structured in order to enable the implementation of certain things with the minimal effort. If we do everything correctly, we can get a truly universal system that can very easily adapt to any situation that needs to be handled.
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Modified Grid-Hedge EA in MQL5 (Part I): Making a Simple Hedge EA

Modified Grid-Hedge EA in MQL5 (Part I): Making a Simple Hedge EA

We will be creating a simple hedge EA as a base for our more advanced Grid-Hedge EA, which will be a mixture of classic grid and classic hedge strategies. By the end of this article, you will know how to create a simple hedge strategy, and you will also get to know what people say about whether this strategy is truly 100% profitable.
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Developing a Replay System — Market simulation (Part 18): Ticks and more ticks (II)

Developing a Replay System — Market simulation (Part 18): Ticks and more ticks (II)

Obviously the current metrics are very far from the ideal time for creating a 1-minute bar. That's the first thing we are going to fix. Fixing the synchronization problem is not difficult. This may seem hard, but it's actually quite simple. We did not make the required correction in the previous article since its purpose was to explain how to transfer the tick data that was used to create the 1-minute bars on the chart into the Market Watch window.
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Design Patterns in software development and MQL5 (Part 3): Behavioral Patterns 1

Design Patterns in software development and MQL5 (Part 3): Behavioral Patterns 1

A new article from Design Patterns articles and we will take a look at one of its types which is behavioral patterns to understand how we can build communication methods between created objects effectively. By completing these Behavior patterns we will be able to understand how we can create and build a reusable, extendable, tested software.
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MQL5 Wizard Techniques you should know (Part 08): Perceptrons

MQL5 Wizard Techniques you should know (Part 08): Perceptrons

Perceptrons, single hidden layer networks, can be a good segue for anyone familiar with basic automated trading and is looking to dip into neural networks. We take a step by step look at how this could be realized in a signal class assembly that is part of the MQL5 Wizard classes for expert advisors.
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Neural networks made easy (Part 53): Reward decomposition

Neural networks made easy (Part 53): Reward decomposition

We have already talked more than once about the importance of correctly selecting the reward function, which we use to stimulate the desired behavior of the Agent by adding rewards or penalties for individual actions. But the question remains open about the decryption of our signals by the Agent. In this article, we will talk about reward decomposition in terms of transmitting individual signals to the trained Agent.