MQL4 and MQL5 Programming Articles

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Study the MQL5 language for programming trading strategies in numerous published articles mostly written by you - the community members. The articles are grouped into categories to help you quicker find answers to any questions related to programming: Integration, Tester, Trading Strategies, etc.

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GIT: What is it?

GIT: What is it?

In this article, I will introduce a very important tool for developers. If you are not familiar with GIT, read this article to get an idea of what it is and how to use it with MQL5.
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Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks

Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks

In the previous article, we discussed a simple RNN which despite its inability to understand long-term dependencies in the data, was able to make a profitable strategy. In this article, we are discussing both the Long-Short Term Memory(LSTM) and the Gated Recurrent Unit(GRU). These two were introduced to overcome the shortcomings of a simple RNN and to outsmart it.
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DoEasy. Service functions (Part 1): Price patterns

DoEasy. Service functions (Part 1): Price patterns

In this article, we will start developing methods for searching for price patterns using timeseries data. A pattern has a certain set of parameters, common to any type of patterns. All data of this kind will be concentrated in the object class of the base abstract pattern. In the current article, we will create an abstract pattern class and a Pin Bar pattern class.
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Introduction to MQL5 (Part 8): Beginner's Guide to Building Expert Advisors (II)

Introduction to MQL5 (Part 8): Beginner's Guide to Building Expert Advisors (II)

This article addresses common beginner questions from MQL5 forums and demonstrates practical solutions. Learn to perform essential tasks like buying and selling, obtaining candlestick prices, and managing automated trading aspects such as trade limits, trading periods, and profit/loss thresholds. Get step-by-step guidance to enhance your understanding and implementation of these concepts in MQL5.
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SP500 Trading Strategy in MQL5 For Beginners

SP500 Trading Strategy in MQL5 For Beginners

Discover how to leverage MQL5 to forecast the S&P 500 with precision, blending in classical technical analysis for added stability and combining algorithms with time-tested principles for robust market insights.
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Developing an Expert Advisor (EA) based on the Consolidation Range Breakout strategy in MQL5

Developing an Expert Advisor (EA) based on the Consolidation Range Breakout strategy in MQL5

This article outlines the steps to create an Expert Advisor (EA) that capitalizes on price breakouts after consolidation periods. By identifying consolidation ranges and setting breakout levels, traders can automate their trading decisions based on this strategy. The Expert Advisor aims to provide clear entry and exit points while avoiding false breakouts
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Portfolio Optimization in Python and MQL5

Portfolio Optimization in Python and MQL5

This article explores advanced portfolio optimization techniques using Python and MQL5 with MetaTrader 5. It demonstrates how to develop algorithms for data analysis, asset allocation, and trading signal generation, emphasizing the importance of data-driven decision-making in modern financial management and risk mitigation.
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Cascade Order Trading Strategy Based on EMA Crossovers for MetaTrader 5

Cascade Order Trading Strategy Based on EMA Crossovers for MetaTrader 5

The article guides in demonstrating an automated algorithm based on EMA Crossovers for MetaTrader 5. Detailed information on all aspects of demonstrating an Expert Advisor in MQL5 and testing it in MetaTrader 5 - from analyzing price range behaviors to risk management.
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Price Driven CGI Model: Theoretical Foundation

Price Driven CGI Model: Theoretical Foundation

Let's discuss the data manipulation algorithm, as we dive deeper into conceptualizing the idea of using price data to drive CGI objects. Think about transferring the effects of events, human emotions and actions on financial asset prices to a real-life model. This study delves into leveraging price data to influence the scale of a CGI object, controlling growth and emotions. These visible effects can establish a fresh analytical foundation for traders. Further insights are shared in the article.
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Creating an Interactive Graphical User Interface in MQL5 (Part 2): Adding Controls and Responsiveness

Creating an Interactive Graphical User Interface in MQL5 (Part 2): Adding Controls and Responsiveness

Enhancing the MQL5 GUI panel with dynamic features can significantly improve the trading experience for users. By incorporating interactive elements, hover effects, and real-time data updates, the panel becomes a powerful tool for modern traders.
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Using JSON Data API in your MQL projects

Using JSON Data API in your MQL projects

Imagine that you can use data that is not found in MetaTrader, you only get data from indicators by price analysis and technical analysis. Now imagine that you can access data that will take your trading power steps higher. You can multiply the power of the MetaTrader software if you mix the output of other software, macro analysis methods, and ultra-advanced tools through the ​​API data. In this article, we will teach you how to use APIs and introduce useful and valuable API data services.
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MQL5 Wizard Techniques you should know (Part 27): Moving Averages and the Angle of Attack

MQL5 Wizard Techniques you should know (Part 27): Moving Averages and the Angle of Attack

The Angle of Attack is an often-quoted metric whose steepness is understood to strongly correlate with the strength of a prevailing trend. We look at how it is commonly used and understood and examine if there are changes that could be introduced in how it's measured for the benefit of a trade system that puts it in use.
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Using PatchTST Machine Learning Algorithm for Predicting Next 24 Hours of Price Action

Using PatchTST Machine Learning Algorithm for Predicting Next 24 Hours of Price Action

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.
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Eigenvectors and eigenvalues: Exploratory data analysis in MetaTrader 5

Eigenvectors and eigenvalues: Exploratory data analysis in MetaTrader 5

In this article we explore different ways in which the eigenvectors and eigenvalues can be applied in exploratory data analysis to reveal unique relationships in data.
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How to Integrate Smart Money Concepts (BOS) Coupled with the RSI Indicator into an EA

How to Integrate Smart Money Concepts (BOS) Coupled with the RSI Indicator into an EA

Smart Money Concept (Break Of Structure) coupled with the RSI Indicator to make in informed automated trading decisions based on the market structure.
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Creating a Daily Drawdown Limiter EA in MQL5

Creating a Daily Drawdown Limiter EA in MQL5

The article discusses, from a detailed perspective, how to implement the creation of an Expert Advisor (EA) based on the trading algorithm. This helps to automate the system in the MQL5 and take control of the Daily Drawdown.
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Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state

Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state

In the previous article, we got acquainted with one of the methods for detecting objects in an image. However, processing a static image is somewhat different from working with dynamic time series, such as the dynamics of the prices we analyze. In this article, we will consider the method of detecting objects in video, which is somewhat closer to the problem we are solving.
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Neural networks made easy (Part 78): Decoder-free Object Detector with Transformer (DFFT)

Neural networks made easy (Part 78): Decoder-free Object Detector with Transformer (DFFT)

In this article, I propose to look at the issue of building a trading strategy from a different angle. We will not predict future price movements, but will try to build a trading system based on the analysis of historical data.
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Creating an Interactive Graphical User Interface in MQL5 (Part 1): Making the Panel

Creating an Interactive Graphical User Interface in MQL5 (Part 1): Making the Panel

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.
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MQL5 Wizard Techniques you should know (Part 26): Moving Averages and the Hurst Exponent

MQL5 Wizard Techniques you should know (Part 26): Moving Averages and the Hurst Exponent

The Hurst Exponent is a measure of how much a time series auto-correlates over the long term. It is understood to be capturing the long-term properties of a time series and therefore carries some weight in time series analysis even outside of economic/ financial time series. We however, focus on its potential benefit to traders by examining how this metric could be paired with moving averages to build a potentially robust signal.
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Sentiment Analysis and Deep Learning for Trading with EA and Backtesting with Python

Sentiment Analysis and Deep Learning for Trading with EA and Backtesting with Python

In this article, we will introduce Sentiment Analysis and ONNX Models with Python to be used in an EA. One script runs a trained ONNX model from TensorFlow for deep learning predictions, while another fetches news headlines and quantifies sentiment using AI.
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Reimagining Classic Strategies in Python: MA Crossovers

Reimagining Classic Strategies in Python: MA Crossovers

In this article, we revisit the classic moving average crossover strategy to assess its current effectiveness. Given the amount of time time since its inception, we explore the potential enhancements that AI can bring to this traditional trading strategy. By incorporating AI techniques, we aim to leverage advanced predictive capabilities to potentially optimize trade entry and exit points, adapt to varying market conditions, and enhance overall performance compared to conventional approaches.
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Neural networks made easy (Part 77): Cross-Covariance Transformer (XCiT)

Neural networks made easy (Part 77): Cross-Covariance Transformer (XCiT)

In our models, we often use various attention algorithms. And, probably, most often we use Transformers. Their main disadvantage is the resource requirement. In this article, we will consider a new algorithm that can help reduce computing costs without losing quality.
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Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part III)

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.
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Developing a Replay System (Part 41): Starting the second phase (II)

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.
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MQL5 Wizard Techniques you should know (Part 25): Multi-Timeframe Testing and Trading

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.
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Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)

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.
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Developing an MQL5 RL agent with RestAPI integration (Part 4): Organizing functions in classes in MQL5

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.
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Developing a Replay System (Part 40): Starting the second phase (I)

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.
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Propensity score in causal inference

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.
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Automated Parameter Optimization for Trading Strategies Using Python and MQL5

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.
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Developing Zone Recovery Martingale strategy in MQL5

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.
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Mastering Market Dynamics: Creating a Support and Resistance Strategy Expert Advisor (EA)

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.
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Creating Time Series Predictions using LSTM Neural Networks: Normalizing Price and Tokenizing Time

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.
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Developing a Replay System (Part 39): Paving the Path (III)

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.
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Population optimization algorithms: Resistance to getting stuck in local extrema (Part I)

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.
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Developing a multi-currency Expert Advisor (Part 4): Pending virtual orders and saving status

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.
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Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part II)

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.
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Integrate Your Own LLM into EA (Part 4): Training Your Own LLM with GPU

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.
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MQL5 Wizard Techniques you should know (Part 24): Moving Averages

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.