Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part III)
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.
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.
MQL5 Wizard Techniques you should know (Part 25): Multi-Timeframe Testing and Trading
Strategies that are based on multiple time frames cannot be tested in wizard assembled Expert Advisors by default because of the MQL5 code architecture used in the assembly classes. We explore a possible work around this limitation for strategies that look to use multiple time frames in a case study with the quadratic moving average.
Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)
Recurrent neural networks (RNNs) excel at leveraging past information to predict future events. Their remarkable predictive capabilities have been applied across various domains with great success. In this article, we will deploy RNN models to predict trends in the forex market, demonstrating their potential to enhance forecasting accuracy in forex trading.
Developing an MQL5 RL agent with RestAPI integration (Part 4): Organizing functions in classes in MQL5
This article discusses the transition from procedural coding to object-oriented programming (OOP) in MQL5 with an emphasis on integration with the REST API. Today we will discuss how to organize HTTP request functions (GET and POST) into classes. We will take a closer look at code refactoring and show how to replace isolated functions with class methods. The article contains practical examples and tests.
Developing a Replay System (Part 40): Starting the second phase (I)
Today we'll talk about the new phase of the replay/simulator system. At this stage, the conversation will become truly interesting and quite rich in content. I strongly recommend that you read the article carefully and use the links provided in it. This will help you understand the content better.
Propensity score in causal inference
The article examines the topic of matching in causal inference. Matching is used to compare similar observations in a data set. This is necessary to correctly determine causal effects and get rid of bias. The author explains how this helps in building trading systems based on machine learning, which become more stable on new data they were not trained on. The propensity score plays a central role and is widely used in causal inference.
Automated Parameter Optimization for Trading Strategies Using Python and MQL5
There are several types of algorithms for self-optimization of trading strategies and parameters. These algorithms are used to automatically improve trading strategies based on historical and current market data. In this article we will look at one of them with python and MQL5 examples.
Developing Zone Recovery Martingale strategy in MQL5
The article discusses, in a detailed perspective, the steps that need to be implemented towards the creation of an expert advisor based on the Zone Recovery trading algorithm. This helps aotomate the system saving time for algotraders.
Mastering Market Dynamics: Creating a Support and Resistance Strategy Expert Advisor (EA)
A comprehensive guide to developing an automated trading algorithm based on the Support and Resistance strategy. Detailed information on all aspects of creating an expert advisor in MQL5 and testing it in MetaTrader 5 – from analyzing price range behaviors to risk management.
Creating Time Series Predictions using LSTM Neural Networks: Normalizing Price and Tokenizing Time
This article outlines a simple strategy for normalizing the market data using the daily range and training a neural network to enhance market predictions. The developed models may be used in conjunction with an existing technical analysis frameworks or on a standalone basis to assist in predicting the overall market direction. The framework outlined in this article may be further refined by any technical analyst to develop models suitable for both manual and automated trading strategies.
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.
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 multi-currency Expert Advisor (Part 4): Pending virtual orders and saving status
Having started developing a multi-currency EA, we have already achieved some results and managed to carry out several code improvement iterations. However, our EA was unable to work with pending orders and resume operation after the terminal restart. Let's add these features.
Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part II)
Today, we are discussing a working Telegram integration for MetaTrader 5 Indicator notifications using the power of MQL5, in partnership with Python and the Telegram Bot API. We will explain everything in detail so that no one misses any point. By the end of this project, you will have gained valuable insights to apply in your projects.
Integrate Your Own LLM into EA (Part 4): Training Your Own LLM with GPU
With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
MQL5 Wizard Techniques you should know (Part 24): Moving Averages
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.
The base class of population algorithms as the backbone of efficient optimization
The article represents a unique research attempt to combine a variety of population algorithms into a single class to simplify the application of optimization methods. This approach not only opens up opportunities for the development of new algorithms, including hybrid variants, but also creates a universal basic test stand. This stand becomes a key tool for choosing the optimal algorithm depending on a specific task.
Multibot in MetaTrader (Part II): Improved dynamic template
Developing the theme of the previous article, I decided to create a more flexible and functional template that has greater capabilities and can be effectively used both in freelancing and as a base for developing multi-currency and multi-period EAs with the ability to integrate with external solutions.
Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part I)
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.
Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models
In the forex markets It is very challenging to predict the future trend without having an idea of the past, Very few machine learning models are capable of making the future predictions by considering past values. In this article, we are going to discuss how we can use classical(Non-time series) Artificial Intelligence models to beat the market
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.
Angle-based operations for traders
This article will cover angle-based operations. We will look at methods for constructing angles and using them in trading.
DoEasy. Controls (Part 33): Vertical ScrollBar
In this article, we will continue the development of graphical elements of the DoEasy library and add vertical scrolling of form object controls, as well as some useful functions and methods that will be required in the future.
How to earn money by fulfilling traders' orders in the Freelance service
MQL5 Freelance is an online service where developers are paid to create trading applications for traders customers. The service has been successfully operating since 2010, with over 100,000 projects completed to date, totaling $7 million in value. As we can see, a substantial amount of money is involved here.
Neural networks made easy (Part 76): Exploring diverse interaction patterns with Multi-future Transformer
This article continues the topic of predicting the upcoming price movement. I invite you to get acquainted with the Multi-future Transformer architecture. Its main idea is to decompose the multimodal distribution of the future into several unimodal distributions, which allows you to effectively simulate various models of interaction between agents on the scene.
Neural networks made easy (Part 75): Improving the performance of trajectory prediction models
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.
Neural networks made easy (Part 74): Trajectory prediction with adaptation
This article introduces a fairly effective method of multi-agent trajectory forecasting, which is able to adapt to various environmental conditions.
Developing a multi-currency Expert Advisor (Part 3): Architecture revision
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.
MQL5 Trading Toolkit (Part 1): Developing A Positions Management EX5 Library
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.
A Step-by-Step Guide on Trading the Break of Structure (BoS) Strategy
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
Building A Candlestick Trend Constraint Model (Part 4): Customizing Display Style For Each Trend Wave
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.
Integrating Hidden Markov Models in MetaTrader 5
In this article we demonstrate how Hidden Markov Models trained using Python can be integrated into MetaTrader 5 applications. Hidden Markov Models are a powerful statistical tool used for modeling time series data, where the system being modeled is characterized by unobservable (hidden) states. A fundamental premise of HMMs is that the probability of being in a given state at a particular time depends on the process's state at the previous time slot.
Gain An Edge Over Any Market (Part II): Forecasting Technical Indicators
Did you know that we can gain more accuracy forecasting certain technical indicators than predicting the underlying price of a traded symbol? Join us to explore how to leverage this insight for better trading strategies.
Balancing risk when trading multiple instruments simultaneously
This article will allow a beginner to write an implementation of a script from scratch for balancing risks when trading multiple instruments simultaneously. Besides, it may give experienced users new ideas for implementing their solutions in relation to the options proposed in this article.
Using optimization algorithms to configure EA parameters on the fly
The article discusses the practical aspects of using optimization algorithms to find the best EA parameters on the fly, as well as virtualization of trading operations and EA logic. The article can be used as an instruction for implementing optimization algorithms into an EA.
MQL5 Wizard Techniques you should know (Part 22): Conditional GANs
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.
Neural networks made easy (Part 73): AutoBots for predicting price movements
We continue to discuss algorithms for training trajectory prediction models. In this article, we will get acquainted with a method called "AutoBots".
Neural networks made easy (Part 72): Trajectory prediction in noisy environments
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.
News Trading Made Easy (Part 2): Risk Management
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.