Articles with examples of trading robots developed in MQL5

The eighth part of the article features the description of the CSymbol class, which is a special object that provides access to any trading instrument. When used inside an Expert Advisor, the class provides a wide set of symbol properties, while allowing to simplify Expert Advisor programming and to expand its functionality.

A regular expression is a special language for handling texts by applying a specified rule, also called a regex or regexp for short. In this article, we are going to show how to handle a trade report with the RegularExpressions library for MQL5, and will also demonstrate the optimization results after using it.

In this chapter, we will extend the library implementation to the possibility of creating multi-window interfaces for the MQL applications. We will also develop a system of priorities for left mouse clicking on graphical objects. This is required to avoid problems when elements do not respond to the user's actions.

The generator of trading strategies of the MQL5 Wizard greatly simplifies testing of trading ideas. The article describes how to develop a custom risk and money management module and enable it in the MQL5 Wizard. As an example we've considered a money management algorithm, in which the size of the trade volume is determined by the results of the previous deal. The structure and format of description of the created class for the MQL5 Wizard are also discussed in the article.

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.

In this article, we create an Inverse Fair Value Gap (IFVG) detection system in MQL5 that identifies bullish/bearish FVGs on recent bars with minimum gap size filtering, tracks their states as normal/mitigated/inverted based on price interactions (mitigation on far-side breaks, retracement on re-entry, inversion on close beyond far side from inside), and ignores overlaps while limiting tracked FVGs.

In this article, we create a Breaker Block Trading System in MQL5 that identifies consolidation ranges, detects breakouts, and validates breaker blocks with swing points to trade retests with defined risk parameters. The system visualizes order and breaker blocks with dynamic labels and arrows, supporting automated trading and trailing stops.

In this article, we will continue to modify the Expert Advisor we have been working on throughout the preceding articles of the MQL5 Cookbook series. This time, the Expert Advisor will be enhanced with indicators whose values will be used to check position opening conditions. To spice it up, we will create a drop-down list in the external parameters to be able to select one out of three trading indicators.

In this article, we build an MQL5 Expert Advisor for Fibonacci retracement trading, using either daily candle ranges or lookback arrays to calculate custom levels like 50% and 61.8% for entries, determining bullish or bearish setups based on close vs. open. The system triggers buys or sells on price crossings of levels with max trades per level, optional closure on new Fib calcs, points-based trailing stops after a min profit threshold, and SL/TP buffers as percentages of the range.

The article describes a method of modifying the code of a trading signal module for the implementation of the functionality allowing you to set pending orders at any distance from the current price: it may be the Close or Open price of the previous bar or the value of the moving average. There are plenty of options. Important is that you can set any opening price for a pending order. This article will be useful to traders who trade with pending orders.

The article discusses the methods for building and training ensembles of neural networks with bagging structure. It also determines the peculiarities of hyperparameter optimization for individual neural network classifiers that make up the ensemble. The quality of the optimized neural network obtained in the previous article of the series is compared with the quality of the created ensemble of neural networks. Possibilities of further improving the quality of the ensemble's classification are considered.

The main difference of the trading system proposed in the article is the use of mathematical tools for analyzing stock quotes. The system applies digital filtering and spectral estimation of discrete time series. The theoretical aspects of the strategy are described and a test Expert Advisor is created.

In the previous article, we started developing a form class for controls. In this article, we are going to continue doing that by filling this class with methods for moving a form over the chart area. We will then integrate this interface component into the core of the library. Also, we will ensure that the color of a form control changes when the mouse cursor is hovering over it.

In the article, we continue to develop the MQL application for working with optimization results. This time, we will show how to form the table of the best results after optimizing the parameters by specifying another criterion via the graphical interface.

The problem of calculation of the total position volume of the specified symbol and magic number is considered in this article. The proposed method requests only the minimum necessary part of the history of deals, finds the closest time when the total position was equal to zero, and performs the calculations with the recent deals. Working with global variables of the client terminal is also considered.

In this article, we are going to continue developing the CWindow class by adding methods, which will allow managing the form by clicking on its controls. We will enable the program to be closed by a form button as well as implement a minimizing and maximizing feature for the form.

The first chapter of the series was about simple and multi-functional buttons. The second article will be dedicated to groups of interconnected buttons that will allow the creation of elements in an application when a user can select one of the option out of a set (group).

Since the publication of the previous article in the series, Easy And Fast library has received some new features. The library structure and code have been partially optimized slightly reducing CPU load. Some recurring methods in many control classes have been moved to the CElement base class.

The project involves using Python for deep learning-based forecasting in financial markets. We will explore the intricacies of testing the model's performance using key metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2) and we will learn how to wrap everything into an executable. We will also make a ONNX model file with its EA.

This article discusses how custom stop levels can be set up in a cross-platform expert advisor. It also discusses a closely-related method by which the evolution of a stop level over time can be defined.

In previous articles, we used stochastic gradient descent to train a neural network using the same learning rate for all neurons within the network. In this article, I propose to look towards adaptive learning methods which enable changing of the learning rate for each neuron. We will also consider the pros and cons of this approach.

Different situations happen in trader’s life. Often, the history of successful trades allows us to restore a strategy, while looking at a loss history we try to develop and improve it. In both cases, we compare trades with known indicators. This article suggests methods of batch comparison of trades with a number of indicators.

This article presents a method of programming the equidistant channel system. Certain details of building such channels are being considered here. Channel typification is provided, and a universal type of moving channels' method is suggested. Object-oriented programming (OOP) is used for code implementation.

This is the next article within the series, in which I show how I created a convenient library for manual application of chart graphics by utilizing keyboard shortcuts. The tools used include straight lines and their combinations. In this part, we will view how the drawing tools are applied using the functions described in the first part. The library can be connected to any Expert Advisor or indicator which will greatly simplify the charting tasks. This solution DOES NOT use external dlls, while all the commands are implemented using built-in MQL tools.

Many traders on Moscow Exchange would like to automate their trading algorithms, but they do not know where to start. The MQL5 language offers a huge range of trading functions, and it additionally provides ready classes that help users to make their first steps in algo trading.

In continuation of our work on the Expert Advisor from the previous article of the series called "MQL5 Cookbook: Analyzing Position Properties in the MetaTrader 5 Strategy Tester", we will enhance it with a whole lot of useful functions, as well as improve and optimize the existing ones. The Expert Advisor will this time have external parameters that can be optimized in the MetaTrader 5 Strategy Tester and will in some ways resemble a simple trading system.

This article will consider new controls: Text Edit box, Picture Slider, as well as additional simple controls: Text label and Picture. The library continues to grow, and in addition to introduction of new controls, the previously created ones are also being improved.

We continue studying the world of neural networks. In this article, we will consider another type of neural networks, recurrent networks. This type is proposed for use with time series, which are represented in the MetaTrader 5 trading platform by price charts.

Some traders execute all their trades automatically, and some mix automatic and manual trades based on the output of several indicators. Being a member of the latter group I needed an interactive tool to asses dynamically risk and reward price levels directly from the chart. This article will present a way to implement an interactive semi-automatic Expert Advisor with predefined equity risk and R/R ratio. The Expert Advisor risk, R/R and lot size parameters can be changed during runtime on the EA panel.