Complex indicators made easy using objects
This article provides a method to create complex indicators while also avoiding the problems that arise when dealing with multiple plots, buffers and/or combining data from multiple sources.
Implementing a Rapid-Fire Trading Strategy Algorithm with Parabolic SAR and Simple Moving Average (SMA) in MQL5
In this article, we develop a Rapid-Fire Trading Expert Advisor in MQL5, leveraging the Parabolic SAR and Simple Moving Average (SMA) indicators to create a responsive trading strategy. We detail the strategy’s implementation, including indicator usage, signal generation, and the testing and optimization process.
EA Status SMS Notifications
Developing a system of SMS notifications that informs you of the status of your EA so that you are always aware of any critical situation, wherever you may be.
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
Graphical Interfaces VIII: The Tree View Control (Chapter 2)
The previous chapter of part VIII on graphical interfaces has focused on the elements of static and drop-down calendar. The second chapter will be dedicated to an equally complex element — a tree view, that is included in every complete library used for creating graphical interfaces. A tree view implemented in this article contains multiple flexible settings and modes, thus allowing to adjust this element of control to your needs.
Library for easy and quick development of MetaTrader programs (part VI): Netting account events
In the previous articles, we started creating a large cross-platform library simplifying the development of programs for MetaTrader 5 and MetaTrader 4 platforms. In the fifth part of the article series, we created trading event classes and the event collection, from which the events are sent to the base object of the Engine library and the control program chart. In this part, we will let the library to work on netting accounts.
Graphics in DoEasy library (Part 89): Programming standard graphical objects. Basic functionality
Currently, the library is able to track standard graphical objects on the client terminal chart, including their removal and modification of some of their parameters. At the moment, it lacks the ability to create standard graphical objects from custom programs.
Statistical Estimations
Estimation of statistical parameters of a sequence is very important, since most of mathematical models and methods are based on different assumptions. For example, normality of distribution law or dispersion value, or other parameters. Thus, when analyzing and forecasting of time series we need a simple and convenient tool that allows quickly and clearly estimating the main statistical parameters. The article shortly describes the simplest statistical parameters of a random sequence and several methods of its visual analysis. It offers the implementation of these methods in MQL5 and the methods of visualization of the result of calculations using the Gnuplot application.
Transferring an Indicator Code into an Expert Advisor Code. Conclusion
This is the final article, devoted to transferring an indicator code into an Expert Advisor code. Here the author transforms on a certain example a code of an Expert Advisor so, that this EA is presented in a single file without calling custom indicators.
Price Action Analysis Toolkit Development (Part 6): Mean Reversion Signal Reaper
While some concepts may seem straightforward at first glance, bringing them to life in practice can be quite challenging. In the article below, we'll take you on a journey through our innovative approach to automating an Expert Advisor (EA) that skillfully analyzes the market using a mean reversion strategy. Join us as we unravel the intricacies of this exciting automation process.
Introduction to Connexus (Part 1): How to Use the WebRequest Function?
This article is the beginning of a series of developments for a library called “Connexus” to facilitate HTTP requests with MQL5. The goal of this project is to provide the end user with this opportunity and show how to use this helper library. I intended to make it as simple as possible to facilitate study and to provide the possibility for future developments.
Automating Trading Strategies in MQL5 (Part 22): Creating a Zone Recovery System for Envelopes Trend Trading
In this article, we develop a Zone Recovery System integrated with an Envelopes trend-trading strategy in MQL5. We outline the architecture for using RSI and Envelopes indicators to trigger trades and manage recovery zones to mitigate losses. Through implementation and backtesting, we show how to build an effective automated trading system for dynamic markets
Develop a Proof-of-Concept DLL with C++ multi-threading support for MetaTrader 5 on Linux
We will begin the journey to explore the steps and workflow on how to base development for MetaTrader 5 platform solely on Linux system in which the final product works seamlessly on both Windows and Linux system. We will get to know Wine, and Mingw; both are the essential tools to make cross-platform development works. Especially Mingw for its threading implementations (POSIX, and Win32) that we need to consider in choosing which one to go with. We then build a proof-of-concept DLL and consume it in MQL5 code, finally compare the performance of both threading implementations. All for your foundation to expand further on your own. You should be comfortable building MT related tools on Linux after reading this article.
Formulating Dynamic Multi-Pair EA (Part 5): Scalping vs Swing Trading Approaches
This part explores how to design a Dynamic Multi-Pair Expert Advisor capable of adapting between Scalping and Swing Trading modes. It covers the structural and algorithmic differences in signal generation, trade execution, and risk management, allowing the EA to intelligently switch strategies based on market behavior and user input.
Trading with the MQL5 Economic Calendar (Part 1): Mastering the Functions of the MQL5 Economic Calendar
In this article, we explore how to use the MQL5 Economic Calendar for trading by first understanding its core functionalities. We then implement key functions of the Economic Calendar in MQL5 to extract relevant news data for trading decisions. Finally, we conclude by showcasing how to utilize this information to enhance trading strategies effectively.
Developing a trading Expert Advisor from scratch (Part 31): Towards the future (IV)
We continue to remove separate parts from our EA. This is the last article within this series. And the last thing to be removed is the sound system. This can be a bit confusing if you haven't followed these article series.
Price Action Analysis Toolkit Development (Part 36): Unlocking Direct Python Access to MetaTrader 5 Market Streams
Harness the full potential of your MetaTrader 5 terminal by leveraging Python’s data-science ecosystem and the official MetaTrader 5 client library. This article demonstrates how to authenticate and stream live tick and minute-bar data directly into Parquet storage, apply sophisticated feature engineering with Ta and Prophet, and train a time-aware Gradient Boosting model. We then deploy a lightweight Flask service to serve trade signals in real time. Whether you’re building a hybrid quant framework or enhancing your EA with machine learning, you’ll walk away with a robust, end-to-end pipeline for data-driven algorithmic trading.
Creating an EA that works automatically (Part 06): Account types (I)
Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode. Our EA in its current state can work in any situation but it is not yet ready for automation. We still have to work on a few points.
Building a Trading System (Part 2): The Science of Position Sizing
Even with a positive-expectancy system, position sizing determines whether you thrive or collapse. It’s the pivot of risk management—translating statistical edges into real-world results while safeguarding your capital.
Learn how to design a trading system by Force Index
Welcome to a new article in our series about how to design a trading system by the most popular technical indicators. In this article, we will learn about a new technical indicator and how to create a trading system using the Force Index indicator.
Developing a Replay System — Market simulation (Part 01): First experiments (I)
How about creating a system that would allow us to study the market when it is closed or even to simulate market situations? Here we are going to start a new series of articles in which we will deal with this topic.
LibMatrix: Library of Matrix Algebra (Part One)
The author familiarizes the readers with a simple library of matrix algebra and provides descriptions and peculiarities of the main functions.
Neural networks made easy (Part 30): Genetic algorithms
Today I want to introduce you to a slightly different learning method. We can say that it is borrowed from Darwin's theory of evolution. It is probably less controllable than the previously discussed methods but it allows training non-differentiable models.
Price Action Analysis Toolkit Development (Part 38): Tick Buffer VWAP and Short-Window Imbalance Engine
In Part 38, we build a production-grade MT5 monitoring panel that converts raw ticks into actionable signals. The EA buffers tick data to compute tick-level VWAP, a short-window imbalance (flow) metric, and ATR-based position sizing. It then visualizes spread, ATR, and flow with low-flicker bars. The system calculates a suggested lot size and a 1R stop, and issues configurable alerts for tight spreads, strong flow, and edge conditions. Auto-trading is intentionally disabled; the focus remains on robust signal generation and a clean user experience.
Neural networks made easy (Part 13): Batch Normalization
In the previous article, we started considering methods aimed at improving neural network training quality. In this article, we will continue this topic and will consider another approach — batch data normalization.
Graphics in DoEasy library (Part 94): Moving and deleting composite graphical objects
In this article, I will start the development of various composite graphical object events. We will also partially consider moving and deleting a composite graphical object. In fact, here I am going to fine-tune the things I implemented in the previous article.
Data Science and Machine Learning (Part 04): Predicting Current Stock Market Crash
In this article I am going to attempt to use our logistic model to predict the stock market crash based upon the fundamentals of the US economy, the NETFLIX and APPLE are the stocks we are going to focus on, Using the previous market crashes of 2019 and 2020 let's see how our model will perform in the current dooms and glooms.
Mastering Fair Value Gaps: Formation, Logic, and Automated Trading with Breakers and Market Structure Shifts
This is an article that I have written aimed to expound and explain Fair Value Gaps, their formation logic for occurring, and automated trading with breakers and market structure shifts.
Using Discriminant Analysis to Develop Trading Systems
When developing a trading system, there usually arises a problem of selecting the best combination of indicators and their signals. Discriminant analysis is one of the methods to find such combinations. The article gives an example of developing an EA for market data collection and illustrates the use of the discriminant analysis for building prognostic models for the FOREX market in Statistica software.
Show Must Go On, or Once Again about ZigZag
About an obvious but still substandard method of ZigZag composition, and what it results in: the Multiframe Fractal ZigZag indicator that represents ZigZags built on three larger ons, on a single working timeframe (TF). In their turn, those larger TFs may be non-standard, too, and range from M5 to MN1.
Learn how to design a trading system by DeMarker
Here is a new article in our series about how to design a trading system by the most popular technical indicators. In this article, we will present how to create a trading system by the DeMarker indicator.
Data Science and Machine Learning (Part 02): Logistic Regression
Data Classification is a crucial thing for an algo trader and a programmer. In this article, we are going to focus on one of classification logistic algorithms that can probability help us identify the Yes's or No's, the Ups and Downs, Buys and Sells.
Growing Neural Gas: Implementation in MQL5
The article shows an example of how to develop an MQL5-program implementing the adaptive algorithm of clustering called Growing neural gas (GNG). The article is intended for the users who have studied the language documentation and have certain programming skills and basic knowledge in the area of neuroinformatics.
Price Action Analysis Toolkit Development (Part 49): Integrating Trend, Momentum, and Volatility Indicators into One MQL5 System
Simplify your MetaTrader 5 charts with the Multi Indicator Handler EA. This interactive dashboard merges trend, momentum, and volatility indicators into one real‑time panel. Switch instantly between profiles to focus on the analysis you need most. Declutter with one‑click Hide/Show controls and stay focused on price action. Read on to learn step‑by‑step how to build and customize it yourself in MQL5.
DIY technical indicator
In this article, I will consider the algorithms allowing you to create your own technical indicator. You will learn how to obtain pretty complex and interesting results with very simple initial assumptions.
Regression models of the Scikit-learn Library and their export to ONNX
In this article, we will explore the application of regression models from the Scikit-learn package, attempt to convert them into ONNX format, and use the resultant models within MQL5 programs. Additionally, we will compare the accuracy of the original models with their ONNX versions for both float and double precision. Furthermore, we will examine the ONNX representation of regression models, aiming to provide a better understanding of their internal structure and operational principles.
Creating multi-symbol, multi-period indicators
In this article, we will look at the principles of creating multi-symbol, multi-period indicators. We will also see how to access the data of such indicators from Expert Advisors and other indicators. We will consider the main features of using multi-indicators in Expert Advisors and indicators and will see how to plot them through custom indicator buffers.
MQL5 Wizard techniques you should know (Part 01): Regression Analysis
Todays trader is a philomath who is almost always (either consciously or not...) looking up new ideas, trying them out, choosing to modify them or discard them; an exploratory process that should cost a fair amount of diligence. This clearly places a premium on the trader's time and the need to avoid mistakes. These series of articles will proposition that the MQL5 wizard should be a mainstay for traders. Why? Because not only does the trader save time by assembling his new ideas with the MQL5 wizard, and greatly reduce mistakes from duplicate coding; he is ultimately set-up to channel his energy on the few critical areas of his trading philosophy.
Automating Trading Strategies in MQL5 (Part 19): Envelopes Trend Bounce Scalping — Trade Execution and Risk Management (Part II)
In this article, we implement trade execution and risk management for the Envelopes Trend Bounce Scalping Strategy in MQL5. We implement order placement and risk controls like stop-loss and position sizing. We conclude with backtesting and optimization, building on Part 18’s foundation.
Brute force approach to pattern search (Part II): Immersion
In this article we will continue discussing the brute force approach. I will try to provide a better explanation of the pattern using the new improved version of my application. I will also try to find the difference in stability using different time intervals and timeframes.