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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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.
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.
Price Action Analysis Toolkit Development (Part 41): Building a Statistical Price-Level EA in MQL5
Statistics has always been at the heart of financial analysis. By definition, statistics is the discipline that collects, analyzes, interprets, and presents data in meaningful ways. Now imagine applying that same framework to candlesticks—compressing raw price action into measurable insights. How helpful would it be to know, for a specific period of time, the central tendency, spread, and distribution of market behavior? In this article, we introduce exactly that approach, showing how statistical methods can transform candlestick data into clear, actionable signals.
Creating an MQL5-Telegram Integrated Expert Advisor (Part 3): Sending Chart Screenshots with Captions from MQL5 to Telegram
In this article, we create an MQL5 Expert Advisor that encodes chart screenshots as image data and sends them to a Telegram chat via HTTP requests. By integrating photo encoding and transmission, we enhance the existing MQL5-Telegram system with visual trading insights directly within Telegram.
Automating Trading Strategies in MQL5 (Part 38): Hidden RSI Divergence Trading with Slope Angle Filters
In this article, we build an MQL5 EA that detects hidden RSI divergences via swing points with strength, bar ranges, tolerance, and slope angle filters for price and RSI lines. It executes buy/sell trades on validated signals with fixed lots, SL/TP in pips, and optional trailing stops for risk control.
Reimagining Classic Strategies (Part 16): Double Bollinger Band Breakouts
This article walks the reader through a reimagined version of the classical Bollinger Band breakout strategy. It identifies key weaknesses in the original approach, such as its well-known susceptibility to false breakouts. The article aims to introduce a possible solution: the Double Bollinger Band trading strategy. This relatively lesser known approach supplements the weaknesses of the classical version and offers a more dynamic perspective on financial markets. It helps us overcome the old limitations defined by the original rules, providing traders with a stronger and more adaptive framework.
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.
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.
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.
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.
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.
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.
Automated exchange grid trading using stop pending orders on Moscow Exchange (MOEX)
The article considers the grid trading approach based on stop pending orders and implemented in an MQL5 Expert Advisor on the Moscow Exchange (MOEX). When trading in the market, one of the simplest strategies is a grid of orders designed to "catch" the market price.
Bill Williams Strategy with and without other indicators and predictions
In this article, we will take a look to one the famous strategies of Bill Williams, and discuss it, and try to improve the strategy with other indicators and with predictions.
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 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.
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.
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.
From Python to MQL5: A Journey into Quantum-Inspired Trading Systems
The article explores the development of a quantum-inspired trading system, transitioning from a Python prototype to an MQL5 implementation for real-world trading. The system uses quantum computing principles like superposition and entanglement to analyze market states, though it runs on classical computers using quantum simulators. Key features include a three-qubit system for analyzing eight market states simultaneously, 24-hour lookback periods, and seven technical indicators for market analysis. While the accuracy rates might seem modest, they provide a significant edge when combined with proper risk management strategies.
Reimagining Classic Strategies (Part XI): Moving Average Cross Over (II)
The moving averages and the stochastic oscillator could be used to generate trend following trading signals. However, these signals will only be observed after the price action has occurred. We can effectively overcome this inherent lag in technical indicators using AI. This article will teach you how to create a fully autonomous AI-powered Expert Advisor in a manner that can improve any of your existing trading strategies. Even the oldest trading strategy possible can be improved.
Price Action Analysis Toolkit Development (Part 7): Signal Pulse EA
Unlock the potential of multi-timeframe analysis with 'Signal Pulse,' an MQL5 Expert Advisor that integrates Bollinger Bands and the Stochastic Oscillator to deliver accurate, high-probability trading signals. Discover how to implement this strategy and effectively visualize buy and sell opportunities using custom arrows. Ideal for traders seeking to enhance their judgment through automated analysis across multiple timeframes.
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.
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!
Creating an MQL5-Telegram Integrated Expert Advisor (Part 2): Sending Signals from MQL5 to Telegram
In this article, we create an MQL5-Telegram integrated Expert Advisor that sends moving average crossover signals to Telegram. We detail the process of generating trading signals from moving average crossovers, implementing the necessary code in MQL5, and ensuring the integration works seamlessly. The result is a system that provides real-time trading alerts directly to your Telegram group chat.
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.
From Novice to Expert: Auto-Geometric Analysis System
Geometric patterns offer traders a concise way to interpret price action. Many analysts draw trend lines, rectangles, and other shapes by hand, and then base trading decisions on the formations they see. In this article, we explore an automated alternative: harnessing MQL5 to detect and analyze the most popular geometric patterns. We’ll break down the methodology, discuss implementation details, and highlight how automated pattern recognition can sharpen a trader's market insights.
Automating Trading Strategies in MQL5 (Part 27): Creating a Price Action Crab Harmonic Pattern with Visual Feedback
In this article, we develop a Crab Harmonic Pattern system in MQL5 that identifies bullish and bearish Crab harmonic patterns using pivot points and Fibonacci ratios, triggering trades with precise entry, stop loss, and take-profit levels. We incorporate visual feedback through chart objects like triangles and trendlines to display the XABCD pattern structure and trade levels.
Algorithmic Trading Without the Routine: Quick Trade Analysis in MetaTrader 5 with SQLite
The article presents a minimal working set for maintaining a trading journal in MQL5 using SQLite: a table structure for trades, signals, and events, indices, prepared statements and trades, as well as standard analytical SQL queries. Integration with the statistics dashboard in MetaTrader 5 and working with the database via MetaEditor are demonstrated. The approach allows automating the journal, accelerating calculations, and performing analysis without complicating the EA code.
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.
Using the MQL5 Economic Calendar for News Filtering (Part 1): Implementing Pre- and Post-News Windows in MQL5
We build a calendar‑driven news filter entirely in MQL5, avoiding web requests and external DLLs. Part 1 covers loading and caching events, mapping them to symbols by currency, filtering by impact level, defining pre/post windows, and blocking new trades during active news, with optional pre‑news position closure. The result is a configurable, prop‑firm‑friendly control that reduces false pauses and protects entries during volatility.
MQL5 Trading Toolkit (Part 3): Developing a Pending Orders Management EX5 Library
Learn how to develop and implement a comprehensive pending orders EX5 library in your MQL5 code or projects. This article will show you how to create an extensive pending orders management EX5 library and guide you through importing and implementing it by building a trading panel or graphical user interface (GUI). The expert advisor orders panel will allow users to open, monitor, and delete pending orders associated with a specified magic number directly from the graphical interface on the chart window.
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.
Price Action Analysis Toolkit Development (Part 74): Building an MQL5 Expert Advisor from Indicator Buffers
This article implements an MQL5 Expert Advisor that connects to a weekend gap indicator via iCustom and CopyBuffer, reading six buffers for buy/sell signals and SL/TP. It validates broker stop-distance rules, handles closed-bar confirmation and duplicate-signal control, and executes orders with a configurable magic number. The EA also includes midpoint stop-loss management and a backtesting procedure so you can verify behavior and adapt parameters to your setup.
MQL5 Trading Tools (Part 1): Building an Interactive Visual Pending Orders Trade Assistant Tool
In this article, we introduce the development of an interactive Trade Assistant Tool in MQL5, designed to simplify placing pending orders in Forex trading. We outline the conceptual design, focusing on a user-friendly GUI for setting entry, stop-loss, and take-profit levels visually on the chart. Additionally, we detail the MQL5 implementation and backtesting process to ensure the tool’s reliability, setting the stage for advanced features in the preceding parts.
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.
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.
Building AI-Powered Trading Systems in MQL5 (Part 4): Overcoming Multiline Input, Ensuring Chat Persistence, and Generating Signals
In this article, we enhance the ChatGPT-integrated program in MQL5 overcoming multiline input limitations with improved text rendering, introducing a sidebar for navigating persistent chat storage using AES256 encryption and ZIP compression, and generating initial trade signals through chart data integration.
Larry Williams Market Secrets (Part 13): Automating Hidden Smash Day Reversal Patterns
The article builds a transparent MQL5 Expert Advisor for Larry Williams’ hidden smash day reversals. Signals are generated only on new bars: a setup bar is validated, then confirmed when the next session trades beyond its extreme. Risk is managed via ATR or structural stops with a defined risk-to-reward, position sizing can be fixed or balance-based, and direction filters plus a one-position policy ensure reproducible tests.
Building AI-Powered Trading Systems in MQL5 (Part 9): Creating an AI Signal Dispatcher
We turn the MQL5 AI trading assistant into a dispatch-driven system that routes seven trading actions through a single central dispatcher. A line-based key-value protocol constrains AI output, while each action maps to market or pending orders and instrument-aware stop levels. A canvas-based UI with a custom prompt editor and pixel-accurate text fitting makes signals consistent, auditable, and ready to render on the chart
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.