Articles on manual and algorithmic trading in MetaTrader 5

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This category features articles on all aspects of trading - from manual to fully automatic trading, from Expert Advisor ideas to trading robot creation using the MQL5 Wizard. Position management, processing of trade events and money management - these integral parts of trading are covered in theses articles.

Learn how to copy trading signals and how to provide around-the-clock operation of Expert Advisors, how to create a trading robot and how to run MetaTrader on Linux and MacOS, what social trading is and how to order a trading robot.

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How to Create an Interactive MQL5 Dashboard/Panel Using the Controls Class (Part 2): Adding Button Responsiveness

How to Create an Interactive MQL5 Dashboard/Panel Using the Controls Class (Part 2): Adding Button Responsiveness

In this article, we focus on transforming our static MQL5 dashboard panel into an interactive tool by enabling button responsiveness. We explore how to automate the functionality of the GUI components, ensuring they react appropriately to user clicks. By the end of the article, we establish a dynamic interface that enhances user engagement and trading experience.
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Creating an MQL5 Expert Advisor Based on the Daily Range Breakout Strategy

Creating an MQL5 Expert Advisor Based on the Daily Range Breakout Strategy

In this article, we create an MQL5 Expert Advisor based on the Daily Range Breakout strategy. We cover the strategy’s key concepts, design the EA blueprint, and implement the breakout logic in MQL5. In the end, we explore techniques for backtesting and optimizing the EA to maximize its effectiveness.
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How to Create an Interactive MQL5 Dashboard/Panel Using the Controls Class (Part 1): Setting Up the Panel

How to Create an Interactive MQL5 Dashboard/Panel Using the Controls Class (Part 1): Setting Up the Panel

In this article, we create an interactive trading dashboard using the Controls class in MQL5, designed to streamline trading operations. The panel features a title, navigation buttons for Trade, Close, and Information, and specialized action buttons for executing trades and managing positions. By the end of the article, you will have a foundational panel ready for further enhancements in future installments.
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Data Science and ML (Part 31): Using CatBoost AI Models for Trading

Data Science and ML (Part 31): Using CatBoost AI Models for Trading

CatBoost AI models have gained massive popularity recently among machine learning communities due to their predictive accuracy, efficiency, and robustness to scattered and difficult datasets. In this article, we are going to discuss in detail how to implement these types of models in an attempt to beat the forex market.
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Integrate Your Own LLM into EA (Part 5): Develop and Test Trading Strategy with LLMs (II)-LoRA-Tuning

Integrate Your Own LLM into EA (Part 5): Develop and Test Trading Strategy with LLMs (II)-LoRA-Tuning

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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Developing a multi-currency Expert Advisor (Part 12): Developing prop trading level risk manager

Developing a multi-currency Expert Advisor (Part 12): Developing prop trading level risk manager

In the EA being developed, we already have a certain mechanism for controlling drawdown. But it is probabilistic in nature, as it is based on the results of testing on historical price data. Therefore, the drawdown can sometimes exceed the maximum expected values (although with a small probability). Let's try to add a mechanism that ensures guaranteed compliance with the specified drawdown level.
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How to create a trading journal with MetaTrader and Google Sheets

How to create a trading journal with MetaTrader and Google Sheets

Create a trading journal using MetaTrader and Google Sheets! You will learn how to sync your trading data via HTTP POST and retrieve it using HTTP requests. In the end, You have a trading journal that will help you keep track of your trades effectively and efficiently.
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Data Science and ML(Part 30): The Power Couple for Predicting the Stock Market, Convolutional Neural Networks(CNNs) and Recurrent Neural Networks(RNNs)

Data Science and ML(Part 30): The Power Couple for Predicting the Stock Market, Convolutional Neural Networks(CNNs) and Recurrent Neural Networks(RNNs)

In this article, We explore the dynamic integration of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in stock market prediction. By leveraging CNNs' ability to extract patterns and RNNs' proficiency in handling sequential data. Let us see how this powerful combination can enhance the accuracy and efficiency of trading algorithms.
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Risk manager for algorithmic trading

Risk manager for algorithmic trading

The objectives of this article are to prove the necessity of using a risk manager and to implement the principles of controlled risk in algorithmic trading in a separate class, so that everyone can verify the effectiveness of the risk standardization approach in intraday trading and investing in financial markets. In this article, we will create a risk manager class for algorithmic trading. This is a logical continuation of the previous article in which we discussed the creation of a risk manager for manual trading.
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Creating a Trading Administrator Panel in MQL5 (Part III): Enhancing the GUI with Visual Styling (I)

Creating a Trading Administrator Panel in MQL5 (Part III): Enhancing the GUI with Visual Styling (I)

In this article, we will focus on visually styling the graphical user interface (GUI) of our Trading Administrator Panel using MQL5. We’ll explore various techniques and features available in MQL5 that allow for customization and optimization of the interface, ensuring it meets the needs of traders while maintaining an attractive aesthetic.
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Developing a multi-currency Expert Advisor (Part 10): Creating objects from a string

Developing a multi-currency Expert Advisor (Part 10): Creating objects from a string

The EA development plan includes several stages with intermediate results being saved in the database. They can only be retrieved from there again as strings or numbers, not objects. So we need a way to recreate the desired objects in the EA from the strings read from the database.
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Example of Stochastic Optimization and Optimal Control

Example of Stochastic Optimization and Optimal Control

This Expert Advisor, named SMOC (likely standing for Stochastic Model Optimal Control), is a simple example of an advanced algorithmic trading system for MetaTrader 5. It uses a combination of technical indicators, model predictive control, and dynamic risk management to make trading decisions. The EA incorporates adaptive parameters, volatility-based position sizing, and trend analysis to optimize its performance across varying market conditions.
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Formulating Dynamic Multi-Pair EA (Part 1): Currency Correlation and Inverse Correlation

Formulating Dynamic Multi-Pair EA (Part 1): Currency Correlation and Inverse Correlation

Dynamic multi pair Expert Advisor leverages both on correlation and inverse correlation strategies to optimize trading performance. By analyzing real-time market data, it identifies and exploits the relationship between currency pairs.
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Building A Candlestick Trend Constraint Model (Part 8): Expert Advisor Development (II)

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.
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Automating Trading Strategies with Parabolic SAR Trend Strategy in MQL5: Crafting an Effective Expert Advisor

Automating Trading Strategies with Parabolic SAR Trend Strategy in MQL5: Crafting an Effective Expert Advisor

In this article, we will automate the trading strategies with Parabolic SAR Strategy in MQL5: Crafting an Effective Expert Advisor. The EA will make trades based on trends identified by the Parabolic SAR indicator.
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Building a Candlestick Trend Constraint Model (Part 8): Expert Advisor Development (I)

Building a Candlestick Trend Constraint Model (Part 8): Expert Advisor Development (I)

In this discussion, we will create our first Expert Advisor in MQL5 based on the indicator we made in the prior article. We will cover all the features required to make the process automatic, including risk management. This will extensively benefit the users to advance from manual execution of trades to automated systems.
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News Trading Made Easy (Part 3): Performing Trades

News Trading Made Easy (Part 3): Performing Trades

In this article, our news trading expert will begin opening trades based on the economic calendar stored in our database. In addition, we will improve the expert's graphics to display more relevant information about upcoming economic calendar events.
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Developing a multi-currency Expert Advisor (Part 6): Automating the selection of an instance group

Developing a multi-currency Expert Advisor (Part 6): Automating the selection of an instance group

After optimizing the trading strategy, we receive sets of parameters. We can use them to create several instances of trading strategies combined in one EA. Previously, we did this manually. Here we will try to automate this process.
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Example of Auto Optimized Take Profits and Indicator Parameters with SMA and EMA

Example of Auto Optimized Take Profits and Indicator Parameters with SMA and EMA

This article presents a sophisticated Expert Advisor for forex trading, combining machine learning with technical analysis. It focuses on trading Apple stock, featuring adaptive optimization, risk management, and multiple strategies. Backtesting shows promising results with high profitability but also significant drawdowns, indicating potential for further refinement.
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Integrate Your Own LLM into EA (Part 5): Develop and Test Trading Strategy with LLMs(I)-Fine-tuning

Integrate Your Own LLM into EA (Part 5): Develop and Test Trading Strategy with LLMs(I)-Fine-tuning

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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Implementing the Deus EA: Automated Trading with RSI and Moving Averages in MQL5

Implementing the Deus EA: Automated Trading with RSI and Moving Averages in MQL5

This article outlines the steps to implement the Deus EA based on the RSI and Moving Average indicators for guiding automated trading.
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Implementing a Bollinger Bands Trading Strategy with MQL5: A Step-by-Step Guide

Implementing a Bollinger Bands Trading Strategy with MQL5: A Step-by-Step Guide

A step-by-step guide to implementing an automated trading algorithm in MQL5 based on the Bollinger Bands trading strategy. A detailed tutorial based on creating an Expert Advisor that can be useful for traders.
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Risk manager for manual trading

Risk manager for manual trading

In this article we will discuss in detail how to write a risk manager class for manual trading from scratch. This class can also be used as a base class for inheritance by algorithmic traders who use automated programs.
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Data Science and ML (Part 28): Predicting Multiple Futures for EURUSD, Using AI

Data Science and ML (Part 28): Predicting Multiple Futures for EURUSD, Using AI

It is a common practice for many Artificial Intelligence models to predict a single future value. However, in this article, we will delve into the powerful technique of using machine learning models to predict multiple future values. This approach, known as multistep forecasting, allows us to predict not only tomorrow's closing price but also the day after tomorrow's and beyond. By mastering multistep forecasting, traders and data scientists can gain deeper insights and make more informed decisions, significantly enhancing their predictive capabilities and strategic planning.
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From Novice to Expert: The Essential Journey Through MQL5 Trading

From Novice to Expert: The Essential Journey Through MQL5 Trading

Unlock your potential! You're surrounded by opportunities. Discover 3 top secrets to kickstart your MQL5 journey or take it to the next level. Let's dive into discussion of tips and tricks for beginners and pros alike.
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Practicing the development of trading strategies

Practicing the development of trading strategies

In this article, we will make an attempt to develop our own trading strategy. Any trading strategy must be based on some kind of statistical advantage. Moreover, this advantage should exist for a long time.
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Building A Candlestick Trend Constraint Model (Part 7): Refining our model for EA development

Building A Candlestick Trend Constraint Model (Part 7): Refining our model for EA development

In this article, we will delve into the detailed preparation of our indicator for Expert Advisor (EA) development. Our discussion will encompass further refinements to the current version of the indicator to enhance its accuracy and functionality. Additionally, we will introduce new features that mark exit points, addressing a limitation of the previous version, which only identified entry points.
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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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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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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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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 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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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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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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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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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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Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models

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