Articles with examples of trading robots developed in MQL5
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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Neural Networks in Trading: A Hybrid Trading Framework with Predictive Coding (StockFormer)
In this article, we will discuss the hybrid trading system StockFormer, which combines predictive coding and reinforcement learning (RL) algorithms. The framework uses 3 Transformer branches with an integrated Diversified Multi-Head Attention (DMH-Attn) mechanism that improves on the vanilla attention module with a multi-headed Feed-Forward block, allowing it to capture diverse time series patterns across different subspaces.
Developing a trading Expert Advisor from scratch (Part 22): New order system (V)
Today we will continue to develop the new order system. It is not that easy to implement a new system as we often encounter problems which greatly complicate the process. When these problems appear, we have to stop and re-analyze the direction in which we are moving.
Introduction to MQL5 (Part 26): Building an EA Using Support and Resistance Zones
This article teaches you how to build an MQL5 Expert Advisor that automatically detects support and resistance zones and executes trades based on them. You’ll learn how to program your EA to identify these key market levels, monitor price reactions, and make trading decisions without manual intervention.
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.
Marvel Your MQL5 Customers with a Usable Cocktail of Technologies!
MQL5 provides programmers with a very complete set of functions and object-oriented API thanks to which they can do everything they want within the MetaTrader environment. However, Web Technology is an extremely versatile tool nowadays that may come to the rescue in some situations when you need to do something very specific, want to marvel your customers with something different or simply you do not have enough time to master a specific part of MT5 Standard Library. Today's exercise walks you through a practical example about how you can manage your development time at the same time as you also create an amazing tech cocktail.
Experiments with neural networks (Part 5): Normalizing inputs for passing to a neural network
Neural networks are an ultimate tool in traders' toolkit. Let's check if this assumption is true. MetaTrader 5 is approached as a self-sufficient medium for using neural networks in trading. A simple explanation is provided.
Automating Trading Strategies in MQL5 (Part 30): Creating a Price Action AB-CD Harmonic Pattern with Visual Feedback
In this article, we develop an AB=CD Pattern EA in MQL5 that identifies bullish and bearish AB=CD harmonic patterns using pivot points and Fibonacci ratios, executing trades with precise entry, stop loss, and take-profit levels. We enhance trader insight with visual feedback through chart objects.
Neural networks made easy (Part 16): Practical use of clustering
In the previous article, we have created a class for data clustering. In this article, I want to share variants of the possible application of obtained results in solving practical trading tasks.
Neural networks made easy (Part 67): Using past experience to solve new tasks
In this article, we continue discussing methods for collecting data into a training set. Obviously, the learning process requires constant interaction with the environment. However, situations can be different.
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.
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.
Neural networks made easy (Part 21): Variational autoencoders (VAE)
In the last article, we got acquainted with the Autoencoder algorithm. Like any other algorithm, it has its advantages and disadvantages. In its original implementation, the autoenctoder is used to separate the objects from the training sample as much as possible. This time we will talk about how to deal with some of its disadvantages.
Experiments with neural networks (Part 2): Smart neural network optimization
In this article, I will use experimentation and non-standard approaches to develop a profitable trading system and check whether neural networks can be of any help for traders. MetaTrader 5 as a self-sufficient tool for using neural networks in trading.
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
Neural networks made easy (Part 15): Data clustering using MQL5
We continue to consider the clustering method. In this article, we will create a new CKmeans class to implement one of the most common k-means clustering methods. During tests, the model managed to identify about 500 patterns.
Neural networks made easy (Part 32): Distributed Q-Learning
We got acquainted with the Q-learning method in one of the earlier articles within this series. This method averages rewards for each action. Two works were presented in 2017, which show greater success when studying the reward distribution function. Let's consider the possibility of using such technology to solve our problems.
Timeseries in DoEasy library (part 54): Descendant classes of abstract base indicator
The article considers creation of classes of descendant objects of base abstract indicator. Such objects will provide access to features of creating indicator EAs, collecting and getting data value statistics of various indicators and prices. Also, create indicator object collection from which getting access to properties and data of each indicator created in the program will be possible.
Understanding Programming Paradigms (Part 2): An Object-Oriented Approach to Developing a Price Action Expert Advisor
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.
Automating The Market Sentiment Indicator
In this article, we automate a custom market sentiment indicator that classifies market conditions into bullish, bearish, risk-on, risk-off, and neutral. The Expert Advisor delivers real-time insights into prevailing sentiment while streamlining the analysis process for current market trends or direction.
Build Self Optimizing Expert Advisors in MQL5 (Part 5): Self Adapting Trading Rules
The best practices, defining how to safely us an indicator, are not always easy to follow. Quiet market conditions may surprisingly produce readings on the indicator that do not qualify as a trading signal, leading to missed opportunities for algorithmic traders. This article will suggest a potential solution to this problem, as we discuss how to build trading applications capable of adapting their trading rules to the available market data.
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.
How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 3): Added symbols prefixes and/or suffixes and Trading Time Session
Several fellow traders sent emails or commented about how to use this Multi-Currency EA on brokers with symbol names that have prefixes and/or suffixes, and also how to implement trading time zones or trading time sessions on this Multi-Currency EA.
Introduction to MQL5 (Part 4): Mastering Structures, Classes, and Time Functions
Unlock the secrets of MQL5 programming in our latest article! Delve into the essentials of structures, classes, and time functions, empowering your coding journey. Whether you're a beginner or an experienced developer, our guide simplifies complex concepts, providing valuable insights for mastering MQL5. Elevate your programming skills and stay ahead in the world of algorithmic trading!
Developing a trading Expert Advisor from scratch (Part 9): A conceptual leap (II)
In this article, we will place Chart Trade in a floating window. In the previous part, we created a basic system which enables the use of templates within a floating window.
Ready-made templates for including indicators to Expert Advisors (Part 2): Volume and Bill Williams indicators
In this article, we will look at standard indicators of the Volume and Bill Williams' indicators category. We will create ready-to-use templates for indicator use in EAs - declaring and setting parameters, indicator initialization and deinitialization, as well as receiving data and signals from indicator buffers in EAs.
Neural Networks in Trading: A Multi-Agent System with Conceptual Reinforcement (FinCon)
We invite you to explore the FinCon framework, which is a a Large Language Model (LLM)-based multi-agent system. The framework uses conceptual verbal reinforcement to improve decision making and risk management, enabling effective performance on a variety of financial tasks.
Developing a trading Expert Advisor from scratch (Part 13): Time and Trade (II)
Today we will construct the second part of the Times & Trade system for market analysis. In the previous article "Times & Trade (I)" we discussed an alternative chart organization system, which would allow having an indicator for the quickest possible interpretation of deals executed in the market.
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 in Trading: Parameter-Efficient Transformer with Segmented Attention (Final Part)
In the previous work, we discussed the theoretical aspects of the PSformer framework, which includes two major innovations in the classical Transformer architecture: the Parameter Shared (PS) mechanism and attention to spatio-temporal segments (SegAtt). In this article, we continue the work we started on implementing the proposed approaches using MQL5.
News Trading Made Easy (Part 1): Creating a Database
News trading can be complicated and overwhelming, in this article we will go through steps to obtain news data. Additionally we will learn about the MQL5 Economic Calendar and what it has to offer.
Introduction to MQL5 (Part 19): Automating Wolfe Wave Detection
This article shows how to programmatically identify bullish and bearish Wolfe Wave patterns and trade them using MQL5. We’ll explore how to identify Wolfe Wave structures programmatically and execute trades based on them using MQL5. This includes detecting key swing points, validating pattern rules, and preparing the EA to act on the signals it finds.
Building AI-Powered Trading Systems in MQL5 (Part 2): Developing a ChatGPT-Integrated Program with User Interface
In this article, we develop a ChatGPT-integrated program in MQL5 with a user interface, leveraging the JSON parsing framework from Part 1 to send prompts to OpenAI’s API and display responses on a MetaTrader 5 chart. We implement a dashboard with an input field, submit button, and response display, handling API communication and text wrapping for user interaction.
How to build and optimize a volatility-based trading system (Chaikin Volatility - CHV)
In this article, we will provide another volatility-based indicator named Chaikin Volatility. We will understand how to build a custom indicator after identifying how it can be used and constructed. We will share some simple strategies that can be used and then test them to understand which one can be better.
Graph Theory: Dijkstra's Algorithm Applied in Trading
Dijkstra's algorithm, a classic shortest-path solution in graph theory, can optimize trading strategies by modeling market networks. Traders can use it to find the most efficient routes in the candlestick chart data.
Building AI-Powered Trading Systems in MQL5 (Part 2): Developing a ChatGPT-Integrated Program with User Interface
In this article, we develop a ChatGPT-integrated program in MQL5 with a user interface, leveraging the JSON parsing framework from Part 1 to send prompts to OpenAI’s API and display responses on a MetaTrader 5 chart. We implement a dashboard with an input field, submit button, and response display, handling API communication and text wrapping for user interaction.
Developing a multi-currency Expert Advisor (Part 1): Collaboration of several trading strategies
There are quite a lot of different trading strategies. So, it might be useful to apply several strategies working in parallel to diversify risks and increase the stability of trading results. But if each strategy is implemented as a separate Expert Advisor (EA), then managing their work on one trading account becomes much more difficult. To solve this problem, it would be reasonable to implement the operation of different trading strategies within a single EA.
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.
Introduction to MQL5 (Part 14): A Beginner's Guide to Building Custom Indicators (III)
Learn to build a Harmonic Pattern indicator in MQL5 using chart objects. Discover how to detect swing points, apply Fibonacci retracements, and automate pattern recognition.
Neural networks made easy (Part 31): Evolutionary algorithms
In the previous article, we started exploring non-gradient optimization methods. We got acquainted with the genetic algorithm. Today, we will continue this topic and will consider another class of evolutionary algorithms.
Continuous walk-forward optimization (Part 8): Program improvements and fixes
The program has been modified based on comments and requests from users and readers of this article series. This article contains a new version of the auto optimizer. This version implements requested features and provides other improvements, which I found when working with the program.