DoEasy. Controls (Part 3): Creating bound controls
In this article, I will create subordinate controls bound to the base element. The development will be performed using the base control functionality. In addition, I will tinker with the graphical element shadow object a bit since it still suffers from some logic errors when applied to any of the objects capable of having a shadow.
Introduction to MQL5 (Part 16): Building Expert Advisors Using Technical Chart Patterns
This article introduces beginners to building an MQL5 Expert Advisor that identifies and trades a classic technical chart pattern — the Head and Shoulders. It covers how to detect the pattern using price action, draw it on the chart, set entry, stop loss, and take profit levels, and automate trade execution based on the pattern.
New Opportunities with MetaTrader 5
MetaTrader 4 gained its popularity with traders from all over the world, and it seemed like nothing more could be wished for. With its high processing speed, stability, wide array of possibilities for writing indicators, Expert Advisors, and informatory-trading systems, and the ability to chose from over a hundred different brokers, - the terminal greatly distinguished itself from the rest. But time doesn’t stand still, and we find ourselves facing a choice of MetaTrade 4 or MetaTrade 5. In this article, we will describe the main differences of the 5th generation terminal from our current favor.
Econometric tools for forecasting volatility: GARCH model
The article describes the properties of the non-linear model of conditional heteroscedasticity (GARCH). The iGARCH indicator has been built on its basis for predicting volatility one step ahead. The ALGLIB numerical analysis library is used to estimate the model parameters.
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.
Larry Williams Market Secrets (Part 5): Automating the Volatility Breakout Strategy in MQL5
This article demonstrates how to automate Larry Williams’ volatility breakout strategy in MQL5 using a practical, step-by-step approach. You will learn how to calculate daily range expansions, derive buy and sell levels, manage risk with range-based stops and reward-based targets, and structure a professional Expert Advisor for MetaTrader 5. Designed for traders and developers looking to transform Larry Williams’ market concepts into a fully testable and deployable automated trading system.
Technical Analysis: What Do We Analyze?
This article tries to analyze several peculiarities of representation of quotes available in the MetaTrader client terminal. The article is general, it doesn't concern programming.
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.
Modified Grid-Hedge EA in MQL5 (Part IV): Optimizing Simple Grid Strategy (I)
In this fourth part, we revisit the Simple Hedge and Simple Grid Expert Advisors (EAs) developed earlier. Our focus shifts to refining the Simple Grid EA through mathematical analysis and a brute force approach, aiming for optimal strategy usage. This article delves deep into the mathematical optimization of the strategy, setting the stage for future exploration of coding-based optimization in later installments.
Developing a Trading Strategy: Using a Volume-Bound Approach
In the world of technical analysis, price often takes center stage. Traders meticulously map out support, resistance, and patterns, yet frequently ignore the critical force that drives these movements: volume. This article delves into a novel approach to volume analysis: the Volume Boundary indicator. This transformation, utilizing sophisticated smoothing functions like the butterfly and triple sine curves, allows for clearer interpretation and the development of systematic trading strategies.
Neural networks made easy (Part 25): Practicing Transfer Learning
In the last two articles, we developed a tool for creating and editing neural network models. Now it is time to evaluate the potential use of Transfer Learning technology using practical examples.
Larry Williams Market Secrets (Part 11): Detecting Smash Day Reversals with a Custom Indicator
We convert Larry Williams’ Smash Day reversal rules into a practical MQL5 indicator that flags confirmed setups with arrows. Step by step, the text shows buffer binding, plot properties, historical mapping, and real‑time updates inside OnCalculate. Adjustable lookback parameters and clean chart rendering help you detect valid reversals quickly while keeping final trade decisions discretionary and context‑driven.
Graphics in DoEasy library (Part 93): Preparing functionality for creating composite graphical objects
In this article, I will start developing the functionality for creating composite graphical objects. The library will support creating composite graphical objects allowing those objects have any hierarchy of connections. I will prepare all the necessary classes for subsequent implementation of such objects.
How we developed the MetaTrader Signals service and Social Trading
We continue to enhance the Signals service, improve the mechanisms, add new functions and fix flaws. The MetaTrader Signals Service of 2012 and the current MetaTrader Signals Service are like two completely different services. Currently, we are implementing A Virtual Hosting Cloud service which consists of a network of servers to support specific versions of the MetaTrader client terminal.
William Gann methods (Part I): Creating Gann Angles indicator
What is the essence of Gann Theory? How are Gann angles constructed? We will create Gann Angles indicator for MetaTrader 5.
DRAKON visual programming language — communication tool for MQL developers and customers
DRAKON is a visual programming language designed to simplify interaction between specialists from different fields (biologists, physicists, engineers...) with programmers in Russian space projects (for example, in the Buran reusable spacecraft project). In this article, I will talk about how DRAKON makes the creation of algorithms accessible and intuitive, even if you have never encountered code, and also how it is easier for customers to explain their thoughts when ordering trading robots, and for programmers to make fewer mistakes in complex functions.
Optimizing Long-Term Trades: Engulfing Candles and Liquidity Strategies
This is a high-timeframe-based EA that makes long-term analyses, trading decisions, and executions based on higher-timeframe analyses of W1, D1, and MN. This article will explore in detail an EA that is specifically designed for long-term traders who are patient enough to withstand and hold their positions during tumultuous lower time frame price action without changing their bias frequently until take-profit targets are hit.
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.
MQL5 Cookbook — Macroeconomic events database
The article discusses the possibilities of handling databases based on the SQLite engine. The CDatabase class has been formed for convenience and efficient use of OOP principles. It is subsequently involved in the creation and management of the database of macroeconomic events. The article provides the examples of using multiple methods of the CDatabase class.
Category Theory in MQL5 (Part 14): Functors with Linear-Orders
This article which is part of a broader series on Category Theory implementation in MQL5, delves into Functors. We examine how a Linear Order can be mapped to a set, thanks to Functors; by considering two sets of data that one would typically dismiss as having any connection.
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.
Population optimization algorithms: Harmony Search (HS)
In the current article, I will study and test the most powerful optimization algorithm - harmonic search (HS) inspired by the process of finding the perfect sound harmony. So what algorithm is now the leader in our rating?
Neural Networks in Trading: An Agent with Layered Memory
Layered memory approaches that mimic human cognitive processes enable the processing of complex financial data and adaptation to new signals, thereby improving the effectiveness of investment decisions in dynamic markets.
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.
Introduction to MQL5 (Part 25): Building an EA that Trades with Chart Objects (II)
This article explains how to build an Expert Advisor (EA) that interacts with chart objects, particularly trend lines, to identify and trade breakout and reversal opportunities. You will learn how the EA confirms valid signals, manages trade frequency, and maintains consistency with user-selected strategies.
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.
Larry Williams Market Secrets (Part 14): Detecting Hidden Smash Day Reversals with a Custom Indicator
This article develops a practical MQL5 indicator that identifies Hidden Smash Day bars by strict numeric criteria and optional confirmation on the following session. We cover detection routines, buffer registration, and plot configuration to place arrows at valid bars. The approach delivers stable, non-repainting signals for historical testing and real-time monitoring.
Nikolay Ivanov (Techno): "What is important for programs is the accuracy of their algorithms"
A programmer from Krasnoyarsk Nikolay Ivanov (Techno) is a leader among the developers in terms of the number of completed orders - he has implemented already more than 200 applications in the Jobs service. In this interview, he is talking about the Jobs service, its specific features and challengers faced by programmers.
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.
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.
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.
DoEasy. Controls (Part 6): Panel control, auto resizing the container to fit inner content
In the article, I will continue my work on the Panel WinForms object and implement its auto resizing to fit the general size of Dock objects located inside the panel. Besides, I will add the new properties to the Symbol library object.
Swing Extremes and Pullbacks in MQL5 (Part 1): Developing a Multi-Timeframe Indicator
In this discussion we will Automate Swing Extremes and the Pullback Indicator, which transforms raw lower-timeframe (LTF) price action into a structured map of market intent, precisely identifying swing highs, swing lows, and corrective phases in real time. By programmatically tracking microstructure shifts, it anticipates potential reversals before they fully unfold—turning noise into actionable insight.
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.
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.
Population optimization algorithms
This is an introductory article on optimization algorithm (OA) classification. The article attempts to create a test stand (a set of functions), which is to be used for comparing OAs and, perhaps, identifying the most universal algorithm out of all widely known ones.
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!
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.
Data Science and ML (Part 37): Using Candlestick patterns and AI to beat the market
Candlestick patterns help traders understand market psychology and identify trends in financial markets, they enable more informed trading decisions that can lead to better outcomes. In this article, we will explore how to use candlestick patterns with AI models to achieve optimal trading performance.
Python, ONNX and MetaTrader 5: Creating a RandomForest model with RobustScaler and PolynomialFeatures data preprocessing
In this article, we will create a random forest model in Python, train the model, and save it as an ONNX pipeline with data preprocessing. After that we will use the model in the MetaTrader 5 terminal.