This article covers the main principles set fourth in evolutionary algorithms, their variety and features. We will conduct an experiment with a simple Expert Advisor used as an example to show how our trading system benefits from optimization. We will consider software programs that implement genetic, evolutionary and other types of optimization, and provide examples of application when optimizing a predictor set and parameters of the trading system.
Cloud technologies are becoming more popular. Nowadays, we can choose between paid and free storage services. Is it possible to use them in trading? This article proposes a technology for exchanging data between terminals using cloud storage services.
This article discusses the implementation of money management method for a cross-platform expert advisor. The money management classes are responsible for the calculation of the lot size to be used for the next trade to be entered by the expert advisor.
This series of articles continues exploring deep neural networks (DNN), which are used in many application areas including trading. Here new dimensions of this theme will be explored along with testing of new methods and ideas using practical experiments. The first article of the series is dedicated to preparing data for DNN.
This article discusses the creation of an order manager for a cross-platform expert advisor. The order manager is responsible for the entry and exit of orders or positions entered by the expert, as well as for keeping an independent record of such trades that is usable for both versions.
This article focuses on specifics of choice, preconditioning and evaluation of the input variables (predictors) for use in machine learning models. New approaches and opportunities of deep predictor analysis and their influence on possible overfitting of models will be considered. The overall result of using models largely depends on the result of this stage. We will analyze two packages offering new and original approaches to the selection of predictors.
This article presents an alternative method of GUI creation based on layouts and containers, using one layout manager — the CBox class. The CBox class is an auxiliary control that acts as a container for essential controls in a GUI panel. It can make designing graphical panels easier, and in some cases, reduce coding time.
I am not a professional programmer. And thus, the principle of "going from the simple to the complex" is of primary importance to me when I am working on trading system development. What exactly is simple for me? First of all, it is the visualization of the process of creating the system, and the logic of its work. Also, it is a minimum of handwritten code. In this article, I will attempt to create and test the trading system, based on a Matlab package, and then write an Expert Advisor for MetaTrader 5. The historical data from MetaTrader 5 will be used for the testing process.
The entire complex of problems of creating a structure of an executed code and its tracing can be solved without serious difficulties. This possibility has appeared in MetaTrader 5 due to the new feature of the MQL5 language - automatic creation of variables of complex type of data (structures and classes) and their elimination when going out of local scope. The article contains the description of the methodology and the ready-made tool.
Is it possible to trade on a real MetaTrader 5 account today? How to organize such trading? The article contains the theory of these questions and the working codes used for copying trades from the MetaTrader 5 terminal to MetaTrader 4. The article will be useful both for the developers of Expert Advisors and for practicing traders.
If specific neural network programs for trading seem expensive and complex or, on the contrary, too simple, try NeuroPro. It is free and contains the optimal set of functionalities for amateurs. This article will tell you how to use it in conjunction with MetaTrader 5.
The ring buffer is the simplest and the most efficient way to arrange data when performing calculations in a sliding window. The article describes the algorithm and shows how it simplifies calculations in a sliding window and makes them more efficient.
This article discusses the CSignal and CSignals classes which will be used in cross-platform expert advisors. It examines the differences between MQL4 and MQL5 on how particular data needed for evaluation of trade signals are accessed to ensure that the code written will be compatible with both compilers.
MetaTrader 4 and MetaTrader 5 uses different conventions in processing trade requests. This article discusses the possibility of using a class object that can be used to represent the trades processed by the server, in order for a cross-platform expert advisor to further work on them, regardless of the version of the trading platform and mode being used.
The sixth part of the article about the universal Expert Advisor describes the use of the trailing stop feature. The article will guide you through how to create a custom trailing stop module using unified rules, as well as how to add it to the trading engine so that it would automatically manage positions.
There exists some components in the MQL5 Standard Library that may prove to be useful in the MQL4 version of cross-platform expert advisors. This article deals with a method of making certain components of the MQL5 Standard Library compatible with the MQL4 compiler.
This article details a method by which cross-platform expert advisors can be developed faster and easier. The proposed method consolidates the features shared by both versions into a single class, and splits the implementation on derived classes for incompatible features.
In the last part of the series of articles about the CStrategy trading engine, we will consider simultaneous operation of multiple trading algorithms, will learn to load strategies from XML files, and will present a simple panel for selecting Expert Advisors from a single executable module, and managing their trading modes.
This article provides further description of the CStrategy trading engine. By popular demand of users, we have added pending order support functions to the trading engine. Also, the latest version of the MetaTrader 5 now supports accounts with the hedging option. The same support has been added to CStrategy. The article provides a detailed description of algorithms for the use of pending orders, as well as of CStrategy operation principles on accounts with the hedging option enabled.
Any Expert Advisor developer, regardless of programming skills, is daily confronted with the same trading tasks and algorithmic problems, which should be solved to organize a reliable trading process. The article describes the possibilities of the CStrategy trading engine that can undertake the solution of these tasks and provide a user with convenient mechanism for describing a custom trading idea.
This article continues the series of publications on a universal Expert Advisor model. This part describes in detail the original event model based on centralized data processing, and considers the structure of the CStrategy base class of the engine.
This article presents an alternative method of GUI creation based on layouts and containers, using one layout manager — the CGrid class. The CGrid class is an auxiliary control that acts as a container for other containers and controls using a grid layout.
Hiding of the implementation details of classes/functions in an .ex5 file will enable you to share your know-how algorithms with other developers, set up common projects and promote them in the Web. And while the MetaQuotes team spares no effort to bring about the possibility of direct inheritance of ex5 library classes, we are going to implement it right now.
In addition to creation of neuronets, the NeuroSolutions software suite allows exporting them as DLLs. This article describes the process of creating a neuronet, generating a DLL and connecting it to an Expert Advisor for trading in MetaTrader 5.
MetaEditor 5 has the debugging feature. But when you write your MQL5 programs, you often want to display not the individual values, but all messages that appear during testing and online work. When the log file contents have large size, it is obvious to automate quick and easy retrieval of required message. In this article we will consider ways of finding errors in MQL5 programs and methods of logging. Also we will simplify logging into files and will get to know a simple program LogMon for comfortable viewing of logs.
This article describes the method of information exchange between the Expert Advisor and ICQ users, several examples are presented. The provided material will be interesting for those, who wish to receive trading information remotely from a client terminal, through an ICQ client in their mobile phone or PDA.
Want to organize export of quotes from MetaTrader 5 to your own application? The MQL5-DLL junction allows to create such solutions! This article will show you one of the ways to export quotes from MetaTrader 5 to applications written in .NET. For me it was more interesting, rational and easy to implement export of quotes using this very platform. Unfortunately, version 5 still does not support .NET, so like in old days we will use win32 dll with .NET support as an interlayer.
The article introduces the dialectical algorithm (DA), a new global optimization method inspired by the philosophical concept of dialectics. The algorithm exploits a unique division of the population into speculative and practical thinkers. Testing shows impressive performance of up to 98% on low-dimensional problems and overall efficiency of 57.95%. The article explains these metrics and presents a detailed description of the algorithm and the results of experiments on different types of functions.
The original Royal Flush Optimization algorithm offers a new approach to solving optimization problems, replacing the classic binary coding of genetic algorithms with a sector-based approach inspired by poker principles. RFO demonstrates how simplifying basic principles can lead to an efficient and practical optimization method. The article presents a detailed analysis of the algorithm and test results.
Today, we take an important step toward helping every developer understand how to read class structures and quickly build Expert Advisors using the MQL5 Standard Library. The library is rich and expandable, yet it can feel like being handed a complex toolkit without a manual. Here we share and discuss an alternative integration routine—a concise, repeatable workflow that shows how to connect classes reliably in real projects.
Gamma and Delta measure how an option’s value reacts to changes in the underlying asset’s price. Delta represents the rate of change of the option’s price relative to the underlying, while Gamma measures how Delta itself changes as price moves. Together, they describe an option’s directional sensitivity and convexity—critical for dynamic hedging and volatility-based trading strategies.
In this discussion, we will develop an Expert Advisor using the CTrade and CStdDevChannel classes, while applying several filters to enhance profitability. This stage puts our previous discussion into practical application. Additionally, I’ll introduce another simple approach to help you better understand the MQL5 Standard Library and its underlying codebase. Join the discussion to explore these concepts in action.
The article presents a new metaheuristic optimization Circle Search Algorithm (CSA) based on the geometric properties of a circle. The algorithm uses the principle of moving points along tangents to find the optimal solution, combining the phases of global exploration and local exploitation.
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.
If we are going to automate periodic optimization, we need to think about auto updates of the settings of the EAs already running on the trading account. This should also allow us to run the EA in the strategy tester and change its settings within a single run.
Every market period has a beginning and an end, each closing with a price that defines its sentiment—much like any candlestick session. Understanding these reference points allows us to gauge the prevailing market mood, revealing whether bullish or bearish forces are in control. In this discussion, we take an important step forward by developing a new feature within the Market Periods Synchronizer—one that visualizes Forex market sessions to support more informed trading decisions. This tool can be especially powerful for identifying, in real time, which side—bulls or bears—dominates the session. Let’s explore this concept and uncover the insights it offers.
Historical data is far from “trash”—it’s the foundation of any robust market analysis. In this article, we’ll take you step‑by‑step from collecting that history to using it to train a predictive model, and finally deploying that model for live price forecasts. Read on to learn how!
We will use a quantum computer from IBM to discover all price movement options. Sounds like science fiction? Welcome to the world of quantum computing for trading!
In Part 5 of our MQL5 AI trading system series, we enhance the ChatGPT-integrated Expert Advisor by introducing a collapsible sidebar, improving navigation with small and large history popups for seamless chat selection, while maintaining multiline input handling, persistent encrypted chat storage, and AI-driven trade signal generation from chart data.
Just because ticks are constantly flowing in doesn’t mean every moment is an opportunity to trade. Today, we take an in-depth study into the art of timing—focusing on developing a time isolation algorithm to help traders identify and trade within their most favorable market windows. Cultivating this discipline allows retail traders to synchronize more closely with institutional timing, where precision and patience often define success. Join this discussion as we explore the science of timing and selective trading through the analytical capabilities of MQL5.