Learn about the object-oriented programming paradigm and its application in MQL5 code. This second article goes deeper into the specifics of object-oriented programming, offering hands-on experience through a practical example. You'll learn how to convert our earlier developed procedural price action expert advisor using the EMA indicator and candlestick price data to object-oriented code.
The quality of future state predictions plays an important role in the Goal-Conditioned Predictive Coding method, which we discussed in the previous article. In this article I want to introduce you to an algorithm that can significantly improve the prediction quality in stochastic environments, such as financial markets.
In this article, we apply a relatively complex neural network algorithm released in 2023 called PatchTST for predicting the price action for the next 24 hours. We will use the official repository, make slight modifications, train a model for EURUSD, and apply it to making future predictions both in Python and MQL5.
We will walk around statistical arbitrage, we will search with python for correlation and cointegration symbols, we will make an indicator for Pearson's coefficient and we will make an EA for trading statistical arbitrage with predictions done with python and ONNX models.
This article explores the fundamental steps in crafting and implementing a Graphical User Interface (GUI) panel using MetaQuotes Language 5 (MQL5). Custom utility panels enhance user interaction in trading by simplifying common tasks and visualizing essential trading information. By creating custom panels, traders can streamline their workflow and save time during trading operations.
This part of the article series is dedicated to integrating WhatsApp with MetaTrader 5 for notifications. We have included a flow chart to simplify understanding and will discuss the importance of security measures in integration. The primary purpose of indicators is to simplify analysis through automation, and they should include notification methods for alerting users when specific conditions are met. Discover more in this article.
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.
This article simplifies triangular arbitrage, showing you how to use predictions and specialized software to trade currencies smarter, even if you're new to the market. Ready to trade with expertise?
Economic Calendar Data is not available for testing with Expert Advisors within Strategy Tester, by default. We look at how Databases could help in providing a work around this limitation. So, for this article we explore how SQLite databases can be used to archive Economic Calendar news such that wizard assembled Expert Advisors can use this to generate trade signals.
Learn how to create a developer's toolkit for managing various position operations with MQL5. In this article, I will demonstrate how to create a library of functions (ex5) that will perform simple to advanced position management operations, including automatic handling and reporting of the different errors that arise when dealing with position management tasks with MQL5.
In this article, we will explore the capabilities of the powerful MQL5 language in drawing various indicator styles on Meta Trader 5. We will also look at scripts and how they can be used in our model.
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.
Symbolic Regression is a form of regression that starts with minimal to no assumptions on what the underlying model that maps the sets of data under study would look like. Even though it can be implemented by Bayesian Methods or Neural Networks, we look at how an implementation with Genetic Algorithms can help customize an expert signal class usable in the MQL5 wizard.
Uncover the secrets behind these powerful dimensionality reduction techniques as we dissect their applications within the MQL5 trading environment. Delve into the nuances of Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA), gaining a profound understanding of their impact on strategy development and market analysis.
Moving Averages are a very common indicator that are used and understood by most Traders. We explore possible use cases that may not be so common within MQL5 Wizard assembled Expert Advisors.
We will breakdown the main MQL5 code into specified code snippets to illustrate the integration of Telegram and WhatsApp for receiving signal notifications from the Trend Constraint indicator we are creating in this article series. This will help traders, both novices and experienced developers, grasp the concept easily. First, we will cover the setup of MetaTrader 5 for notifications and its significance to the user. This will help developers in advance to take notes to further apply in their systems.
Many people who consider themselves MQL5 programmers do not have the basic knowledge that I will outline in this article. Many people consider MQL5 to be a limited tool, but the actual reason is that they do not have the required knowledge. So, if you don't know something, don't be ashamed of it. It's better to feel ashamed for not asking. Simply forcing MetaTrader 5 to disable indicator duplication in no way ensures two-way communication between the indicator and the Expert Advisor. We are still very far from this, but the fact that the indicator is not duplicated on the chart gives us some confidence.
The models we create are becoming larger and more complex. This increases the costs of not only their training as well as operation. However, the time required to make a decision is often critical. In this regard, let us consider methods for optimizing model performance without loss of quality.
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.
We have already made some progress in developing a multi-currency EA with several strategies working in parallel. Considering the accumulated experience, let's review the architecture of our solution and try to improve it before we go too far ahead.
A comprehensive guide to developing an automated trading algorithm based on the Break of Structure (BoS) strategy. Detailed information on all aspects of creating an advisor in MQL5 and testing it in MetaTrader 5 — from analyzing price support and resistance to risk management
In previous articles, we discussed the Decision Transformer method and several algorithms derived from it. We experimented with different goal setting methods. During the experiments, we worked with various ways of setting goals. However, the model's study of the earlier passed trajectory always remained outside our attention. In this article. I want to introduce you to a method that fills this gap.
In this article, we will get acquainted with an algorithm that uses closed-form policy improvement operators to optimize Agent actions in offline mode.
AdaBoost, a powerful boosting algorithm designed to elevate the performance of your AI models. AdaBoost, short for Adaptive Boosting, is a sophisticated ensemble learning technique that seamlessly integrates weak learners, enhancing their collective predictive strength.
Generative Adversarial Networks are a pairing of Neural Networks that train off of each other for more accurate results. We adopt the conditional type of these networks as we look to possible application in forecasting Financial time series within an Expert Signal Class.
In this article, inheritance will be introduced into our previous and new code. A new database design will be implemented to provide efficiency. Additionally, a risk management class will be created to tackle volume calculations.
Let's continue developing a multi-currency EA with several strategies working in parallel. Let's try to move all the work associated with opening market positions from the strategy level to the level of the EA managing the strategies. The strategies themselves will trade only virtually, without opening market positions.
Support Vector Machines classify data based on predefined classes by exploring the effects of increasing its dimensionality. It is a supervised learning method that is fairly complex given its potential to deal with multi-dimensioned data. For this article we consider how it’s very basic implementation of 2-dimensioned data can be done more efficiently with Newton’s Polynomial when classifying price-action.
In this article, we will look at the theory of causal inference using machine learning, as well as the custom approach implementation in Python. Causal inference and causal thinking have their roots in philosophy and psychology and play an important role in our understanding of reality.
Density Based Spatial Clustering for Applications with Noise is an unsupervised form of grouping data that hardly requires any input parameters, save for just 2, which when compared to other approaches like k-means, is a boon. We delve into how this could be constructive for testing and eventually trading with Wizard assembled Expert Advisers
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.
In offline learning, we use a fixed dataset, which limits the coverage of environmental diversity. During the learning process, our Agent can generate actions beyond this dataset. If there is no feedback from the environment, how can we be sure that the assessments of such actions are correct? Maintaining the Agent's policy within the training dataset becomes an important aspect to ensure the reliability of training. This is what we will talk about in this article.
Variables and data types are very important topics not only in MQL5 programming but also in any programming language. MQL5 variables and data types can be categorized as simple and advanced ones. In this article, we will identify and learn about advanced ones because we already mentioned simple ones in a previous article.
Bayesian inference is the adoption of Bayes Theorem to update probability hypothesis as new information is made available. This intuitively leans to adaptation in time series analysis, and so we have a look at how we could use this in building custom classes not just for the signal but also money-management and trailing-stops.
The project involves using Python for deep learning-based forecasting in financial markets. We will explore the intricacies of testing the model's performance using key metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2) and we will learn how to wrap everything into an executable. We will also make a ONNX model file with its EA.
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.
This article focuses on taking advantage of in-built meta trader 5 indicators to screen out off-trend signals. Advancing from the previous article we will explore how to do it using MQL5 code to communicate our idea to the final program.
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.
