Articles on the MQL5 programming and use of trading robots

In this new article from our series for learning how to design trading systems, I will present the Commodities Channel Index (CCI), explain its specifics, and share with you how to create a trading system based on this indicator.

The article describes the algorithm for implementing the OpenCL candlestick patterns tester in the "1 minute OHLC" mode. We will also compare its speed with the built-in strategy tester launched in the fast and slow optimization modes.

In this article, we will fiddle around ChatGPT from OpenAI in order to understand its capabilities in terms of reducing the time and labor intensity of developing Expert Advisors, indicators and scripts. I will quickly navigate you through this technology and try to show you how to use it correctly for programming in MQL4 and MQL5.

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 expand the capabilities of the toolkit: we will add the ability to close trade positions upon specific conditions and will create tables for controlling market and pending orders, with the ability to edit these 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.

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.

In this article, we will consider the class of displaying text messages. Currently, we have a sufficient number of different text messages. It is time to re-arrange the methods of their storage, display and translation of Russian or English messages to other languages. Besides, it would be good to introduce convenient ways of adding new languages to the library and quickly switching between them.

Surfing the Internet, it is easy to find many strategies, which will give you a number of various recommendations. Let’s take an insider’s approach and look into the process of strategy creation, based on the differences in timezones on different continents.

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.

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.

In this article, we continue to dwell on reversing techniques. We will try to reduce the maximum balance drawdown till an acceptable level for the instruments considered earlier. We will see if the measures will reduce the profit. We will also check how the reversing method performs on other markets, including stock, commodity, index, ETF and agricultural markets. Attention, the article contains a lot of images!

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.

The article describes how to create a framework for the online analysis of multidimensional data (OLAP), as well as how to implement this in MQL and to apply such analysis in the MetaTrader environment using the example of trading account history processing.

The article suggests a technology helping everyone to create custom trading strategies by assembling an individual indicator set, as well as to develop custom market entry signals.

In this article, I will continue the development of the basic graphical element class of all library graphical objects powered by the CCanvas Standard Library class. I will create the methods for drawing graphical primitives and for displaying a text on a graphical element object.

The article describes the library allowing you to increase the efficiency of working with HTTP requests in MQL5. Execution of WebRequest in non-blocking mode is implemented in additional threads that use auxiliary charts and Expert Advisors, exchanging custom events and reading shared resources. The source codes are applied as well.

This is a continuation of the idea of processing and analysis of optimization results. This time, our purpose is to select the 100 best optimization results and display them in a GUI table. The user will be able to select a row in the optimization results table and receive a multi-symbol balance and drawdown graph on separate charts.

It is impossible to get a truly stable algorithm if we use optimization based on historical data to select parameters. A stable algorithm should be aware of what parameters are needed when working on any trading instrument at any time. It should not forecast or guess, it should know for sure.

In my previous article, I mentioned the importance of identifying the trend which is the direction of prices. In this article I will share one of the most important concepts and indicators which is the Momentum indicator. I will share how to design a trading system based on this Momentum indicator.

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.

Learn how to automate Larry Williams market structure concepts in MQL5 by building a complete Expert Advisor that reads swing points, generates trade signals, manages risk, and applies a dynamic trailing stop strategy.

This is the last article within the series devoted to the Reversing trading strategy. Here we will try to solve the problem, which caused the testing results instability in previous articles. We will also develop and test our own algorithm for manual trading in any market using the reversing strategy.

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.

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.

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.

The article offers a simple emulator of the MetaTrader 5 trading environment for MetaTrader 4. The emulator implements migration and adjustment of trade classes of the Standard Library. As a result, Expert Advisors generated in the MetaTrader 5 Wizard can be compiled and executed in MetaTrader 4 without changes.

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.

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.

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.

We continue the series of articles on MQL5 programming. This time we will see how to get results of each optimization pass right during the Expert Advisor parameter optimization. The implementation will be done so as to ensure that if the conditions specified in the external parameters are met, the corresponding pass values will be written to a file. In addition to test values, we will also save the parameters that brought about such results.

This article describes a new approach to hedging of positions and draws the line in the debates between users of MetaTrader 4 and MetaTrader 5 about this matter. It is a continuation of the first part: "Bi-Directional Trading and Hedging of Positions in MetaTrader 5 Using the HedgeTerminal Panel, Part 1". In the second part, we discuss integration of custom Expert Advisors with HedgeTerminalAPI, which is a special visualization library designed for bi-directional trading in a comfortable software environment providing tools for convenient position management.

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.