Articles on the MQL5 programming and use of trading robots

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 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 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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

In this article, we will develop a grider EA for trading in a trend direction within a range. Thus, the EA is to be suited mostly for Forex and commodity markets. According to the tests, our grider showed profit since 2018. Unfortunately, this is not true for the period of 2014-2018.

The article provides an overview of the terminal's capabilities for creating and working with custom symbols, offers options for simulating a trading history using custom symbols, trend and various chart patterns.

A proverbial wisdom often attributed to various famous people says: "He who makes no mistakes never makes anything." Unless you consider idleness itself a mistake, this statement is hard to argue with. But you can always analyze the past mistakes (your own and of others) to minimize the number of your future mistakes. We are going to attempt to review possible situations arising when executing jobs in the same-name service.

I continue filling the algorithm with the minimum necessary functionality and testing the results. The profitability is quite low but the articles demonstrate the model of the fully automated profitable trading on completely different instruments traded on fundamentally different markets.

In this article, I will rework the concept of building graphical objects from the previous article and prepare the base class of all graphical objects of the library powered by the Standard Library CCanvas class.

This article deals primarily with the classes CExpertAdvisor and CExpertAdvisors, which serve as the container for all the other components described in this article-series regarding cross-platform expert advisors.