Articles with examples of trading robots developed in MQL5

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An Expert Advisor is the 'pinnacle' of programming and the desired goal of every automated trading developer. Read the articles in this section to create your own trading robot. By following the described steps you will learn how to create, debug and test automated trading systems.

The articles not only teach MQL5 programming, but also show how to implement trading ideas and techniques. You will learn how to program a trailing stop, how to apply money management, how to get the indicator values, and much more.

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Filtering and feature extraction in the frequency domain

Filtering and feature extraction in the frequency domain

In this article we explore the application of digital filters on time series represented in the frequency domain so as to extract unique features that may be useful to prediction models.
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Neural networks made easy (Part 37): Sparse Attention

Neural networks made easy (Part 37): Sparse Attention

In the previous article, we discussed relational models which use attention mechanisms in their architecture. One of the specific features of these models is the intensive utilization of computing resources. In this article, we will consider one of the mechanisms for reducing the number of computational operations inside the Self-Attention block. This will increase the general performance of the model.
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Automating Trading Strategies in MQL5 (Part 2): The Kumo Breakout System with Ichimoku and Awesome Oscillator

Automating Trading Strategies in MQL5 (Part 2): The Kumo Breakout System with Ichimoku and Awesome Oscillator

In this article, we create an Expert Advisor (EA) that automates the Kumo Breakout strategy using the Ichimoku Kinko Hyo indicator and the Awesome Oscillator. We walk through the process of initializing indicator handles, detecting breakout conditions, and coding automated trade entries and exits. Additionally, we implement trailing stops and position management logic to enhance the EA's performance and adaptability to market conditions.
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Neural networks made easy (Part 18): Association rules

Neural networks made easy (Part 18): Association rules

As a continuation of this series of articles, let's consider another type of problems within unsupervised learning methods: mining association rules. This problem type was first used in retail, namely supermarkets, to analyze market baskets. In this article, we will talk about the applicability of such algorithms in trading.
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Creating an MQL5-Telegram Integrated Expert Advisor (Part 5): Sending Commands from Telegram to MQL5 and Receiving Real-Time Responses

Creating an MQL5-Telegram Integrated Expert Advisor (Part 5): Sending Commands from Telegram to MQL5 and Receiving Real-Time Responses

In this article, we create several classes to facilitate real-time communication between MQL5 and Telegram. We focus on retrieving commands from Telegram, decoding and interpreting them, and sending appropriate responses back. By the end, we ensure that these interactions are effectively tested and operational within the trading environment
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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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Neural networks made easy (Part 54): Using random encoder for efficient research (RE3)

Neural networks made easy (Part 54): Using random encoder for efficient research (RE3)

Whenever we consider reinforcement learning methods, we are faced with the issue of efficiently exploring the environment. Solving this issue often leads to complication of the algorithm and training of additional models. In this article, we will look at an alternative approach to solving this problem.
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Automating Trading Strategies in MQL5 (Part 20): Multi-Symbol Strategy Using CCI and AO

Automating Trading Strategies in MQL5 (Part 20): Multi-Symbol Strategy Using CCI and AO

In this article, we create a multi-symbol trading strategy using CCI and AO indicators to detect trend reversals. We cover its design, MQL5 implementation, and backtesting process. The article concludes with tips for performance improvement.
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Build Self Optimizing Expert Advisors in MQL5 (Part 6): Stop Out Prevention

Build Self Optimizing Expert Advisors in MQL5 (Part 6): Stop Out Prevention

Join us in our discussion today as we look for an algorithmic procedure to minimize the total number of times we get stopped out of winning trades. The problem we faced is significantly challenging, and most solutions given in community discussions lack set and fixed rules. Our algorithmic approach to solving the problem increased the profitability of our trades and reduced our average loss per trade. However, there are further advancements to be made to completely filter out all trades that will be stopped out, our solution is a good first step for anyone to try.
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MQL5 Trading Tools (Part 3): Building a Multi-Timeframe Scanner Dashboard for Strategic Trading

MQL5 Trading Tools (Part 3): Building a Multi-Timeframe Scanner Dashboard for Strategic Trading

In this article, we build a multi-timeframe scanner dashboard in MQL5 to display real-time trading signals. We plan an interactive grid interface, implement signal calculations with multiple indicators, and add a close button. The article concludes with backtesting and strategic trading benefits
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Testing and optimization of binary options strategies in MetaTrader 5

Testing and optimization of binary options strategies in MetaTrader 5

In this article, I will check and optimize binary options strategies in MetaTrader 5.
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Introduction to MQL5 (Part 6): A Beginner's Guide to Array Functions in MQL5 (II)

Introduction to MQL5 (Part 6): A Beginner's Guide to Array Functions in MQL5 (II)

Embark on the next phase of our MQL5 journey. In this insightful and beginner-friendly article, we'll look into the remaining array functions, demystifying complex concepts to empower you to craft efficient trading strategies. We’ll be discussing ArrayPrint, ArrayInsert, ArraySize, ArrayRange, ArrarRemove, ArraySwap, ArrayReverse, and ArraySort. Elevate your algorithmic trading expertise with these essential array functions. Join us on the path to MQL5 mastery!
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Introduction to MQL5 (Part 3): Mastering the Core Elements of MQL5

Introduction to MQL5 (Part 3): Mastering the Core Elements of MQL5

Explore the fundamentals of MQL5 programming in this beginner-friendly article, where we demystify arrays, custom functions, preprocessors, and event handling, all explained with clarity making every line of code accessible. Join us in unlocking the power of MQL5 with a unique approach that ensures understanding at every step. This article sets the foundation for mastering MQL5, emphasizing the explanation of each line of code, and providing a distinct and enriching learning experience.
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Implementing the Generalized Hurst Exponent and the Variance Ratio test in MQL5

Implementing the Generalized Hurst Exponent and the Variance Ratio test in MQL5

In this article, we investigate how the Generalized Hurst Exponent and the Variance Ratio test can be utilized to analyze the behaviour of price series in MQL5.
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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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Automating Trading Strategies in MQL5 (Part 15): Price Action Harmonic Cypher Pattern with Visualization

Automating Trading Strategies in MQL5 (Part 15): Price Action Harmonic Cypher Pattern with Visualization

In this article, we explore the automation of the Cypher harmonic pattern in MQL5, detailing its detection and visualization on MetaTrader 5 charts. We implement an Expert Advisor that identifies swing points, validates Fibonacci-based patterns, and executes trades with clear graphical annotations. The article concludes with guidance on backtesting and optimizing the program for effective trading.
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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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Neural networks made easy (Part 17): Dimensionality reduction

Neural networks made easy (Part 17): Dimensionality reduction

In this part we continue discussing Artificial Intelligence models. Namely, we study unsupervised learning algorithms. We have already discussed one of the clustering algorithms. In this article, I am sharing a variant of solving problems related to dimensionality reduction.
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Neural networks made easy (Part 75): Improving the performance of trajectory prediction models

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.
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Build Self Optimizing Expert Advisors in MQL5  (Part 3): Dynamic Trend Following and Mean Reversion Strategies

Build Self Optimizing Expert Advisors in MQL5 (Part 3): Dynamic Trend Following and Mean Reversion Strategies

Financial markets are typically classified as either in a range mode or a trending mode. This static view of the market may make it easier for us to trade in the short run. However, it is disconnected from the reality of the market. In this article, we look to better understand how exactly financial markets move between these 2 possible modes and how we can use our new understanding of market behavior to gain confidence in our algorithmic trading strategies.
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Neural networks made easy (Part 22): Unsupervised learning of recurrent models

Neural networks made easy (Part 22): Unsupervised learning of recurrent models

We continue to study unsupervised learning algorithms. This time I suggest that we discuss the features of autoencoders when applied to recurrent model training.
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Trading with the MQL5 Economic Calendar (Part 2): Creating a News Dashboard Panel

Trading with the MQL5 Economic Calendar (Part 2): Creating a News Dashboard Panel

In this article, we create a practical news dashboard panel using the MQL5 Economic Calendar to enhance our trading strategy. We begin by designing the layout, focusing on key elements like event names, importance, and timing, before moving into the setup within MQL5. Finally, we implement a filtering system to display only the most relevant news, giving traders quick access to impactful economic events.
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Building a Candlestick Trend Constraint Model (Part 9): Multiple Strategies Expert Advisor (III)

Building a Candlestick Trend Constraint Model (Part 9): Multiple Strategies Expert Advisor (III)

Welcome to the third installment of our trend series! Today, we’ll delve into the use of divergence as a strategy for identifying optimal entry points within the prevailing daily trend. We’ll also introduce a custom profit-locking mechanism, similar to a trailing stop-loss, but with unique enhancements. In addition, we’ll upgrade the Trend Constraint Expert to a more advanced version, incorporating a new trade execution condition to complement the existing ones. As we move forward, we’ll continue to explore the practical application of MQL5 in algorithmic development, providing you with more in-depth insights and actionable techniques.
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Creating an EA that works automatically (Part 07): Account types (II)

Creating an EA that works automatically (Part 07): Account types (II)

Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode. The trader should always be aware of what the automatic EA is doing, so that if it "goes off the rails", the trader could remove it from the chart as soon as possible and take control of the situation.
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Data Science and Machine Learning (Part 22): Leveraging Autoencoders Neural Networks for Smarter Trades by Moving from Noise to Signal

Data Science and Machine Learning (Part 22): Leveraging Autoencoders Neural Networks for Smarter Trades by Moving from Noise to Signal

In the fast-paced world of financial markets, separating meaningful signals from the noise is crucial for successful trading. By employing sophisticated neural network architectures, autoencoders excel at uncovering hidden patterns within market data, transforming noisy input into actionable insights. In this article, we explore how autoencoders are revolutionizing trading practices, offering traders a powerful tool to enhance decision-making and gain a competitive edge in today's dynamic markets.
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Experiments with neural networks (Part 7): Passing indicators

Experiments with neural networks (Part 7): Passing indicators

Examples of passing indicators to a perceptron. The article describes general concepts and showcases the simplest ready-made Expert Advisor followed by the results of its optimization and forward test.
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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.
Considering Orders in a Large Program
Considering Orders in a Large Program

Considering Orders in a Large Program

General principles of considering orders in a large and complex program are discussed.
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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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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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Automating Trading Strategies in MQL5 (Part 18): Envelopes Trend Bounce Scalping - Core Infrastructure and Signal Generation (Part I)

Automating Trading Strategies in MQL5 (Part 18): Envelopes Trend Bounce Scalping - Core Infrastructure and Signal Generation (Part I)

In this article, we build the core infrastructure for the Envelopes Trend Bounce Scalping Expert Advisor in MQL5. We initialize envelopes and other indicators for signal generation. We set up backtesting to prepare for trade execution in the next part.
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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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Neural networks made easy (Part 58): Decision Transformer (DT)

Neural networks made easy (Part 58): Decision Transformer (DT)

We continue to explore reinforcement learning methods. In this article, I will focus on a slightly different algorithm that considers the Agent’s policy in the paradigm of constructing a sequence of actions.
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Neural networks made easy (Part 23): Building a tool for Transfer Learning

Neural networks made easy (Part 23): Building a tool for Transfer Learning

In this series of articles, we have already mentioned Transfer Learning more than once. However, this was only mentioning. in this article, I suggest filling this gap and taking a closer look at Transfer Learning.
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Example of new Indicator and Conditional LSTM

Example of new Indicator and Conditional LSTM

This article explores the development of an Expert Advisor (EA) for automated trading that combines technical analysis with deep learning predictions.
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Price Action Analysis Toolkit Development (Part 16): Introducing Quarters Theory (II) — Intrusion Detector EA

Price Action Analysis Toolkit Development (Part 16): Introducing Quarters Theory (II) — Intrusion Detector EA

In our previous article, we introduced a simple script called "The Quarters Drawer." Building on that foundation, we are now taking the next step by creating a monitor Expert Advisor (EA) to track these quarters and provide oversight regarding potential market reactions at these levels. Join us as we explore the process of developing a zone detection tool in this article.
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Neural networks made easy (Part 73): AutoBots for predicting price movements

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".
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News Trading Made Easy (Part 2): Risk Management

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.
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Integration of Broker APIs with Expert Advisors using MQL5 and Python

Integration of Broker APIs with Expert Advisors using MQL5 and Python

In this article, we will discuss the implementation of MQL5 in partnership with Python to perform broker-related operations. Imagine having a continuously running Expert Advisor (EA) hosted on a VPS, executing trades on your behalf. At some point, the ability of the EA to manage funds becomes paramount. This includes operations such as topping up your trading account and initiating withdrawals. In this discussion, we will shed light on the advantages and practical implementation of these features, ensuring seamless integration of fund management into your trading strategy. Stay tuned!
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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.