Articles on MetaTrader 5 integration using MQL5

This article describes the process of creating an extension for the MetaTrader terminal. The solution discussed helps to automate the optimization process by running optimizations in other terminals. A few more articles will be written concerning this topic. The extension has been developed using the C# language and design patterns, which additionally demonstrates the ability to expand the terminal capabilities by developing custom modules, as well as the ability to create custom graphical user interfaces using the functionality of a preferred programming language.

Studies related to search for the fractal behavior of financial data suggest that behind the seemingly chaotic behavior of economic time series there are hidden stable mechanisms of participants' collective behavior. These mechanisms can lead to the emergence of price dynamics on the exchange, which can define and describe specific properties of price series. When applied to trading, one could benefit from the indicators which can efficiently and reliably estimate the fractal parameters in the scale and time frame, which are relevant in practice.

The article is a follow-up of the previous publication "Developing graphical interfaces for Expert Advisors and indicators based on .Net Framework and C#". It introduces new graphical elements for creating graphical interfaces.

The article presents a new version of the Pattern Analyzer application. This version provides bug fixes and new features, as well as the revised user interface. Comments and suggestions from previous article were taken into account when developing the new version. The resulting application is described in this article.

The original basic article has not lost its relevance and thus if you are interested in this topic, be sure to read the first article. However much time has passed since then, so the current Visual Studio 2017 features an updated interface. The MetaTrader 5 platform has also acquired new features. The article provides a description of dll project development stages, as well as DLL setup and interaction with MetaTrader 5 tools.

After the upgrade of the MATLAB package in 2015, it is necessary to consider a modern way of creating DLL libraries. The article uses a sample predictive indicator to illustrate the peculiarities of linking MetaTrader 5 and MATLAB using modern 64-bit versions of the platforms, which are utilized nowadays. With the entire sequence of connecting MATLAB considered, MQL5 developers will be able to create applications with advanced computational capabilities much faster, avoiding «pitfalls».

The article provides a description of a universal method for analyzing and converting data from HTML documents based on CSS selectors. Trading reports, tester reports, your favorite economic calendars, public signals, account monitoring and additional online quote sources will become available straight from MQL.

The article presents a simple and fast method of creating graphical windows using Visual Studio with subsequent integration into the Expert Advisor's MQL code. The article is meant for non-specialist audiences and does not require any knowledge of C# and .Net technology.

Comprehensive data processing requires extensive tools and is often beyond the sandbox of one single application. Specialized programming languages are used for processing and analyzing data, statistics and machine learning. One of the leading programming languages for data processing is Python. The article provides a description of how to connect MetaTrader 5 and Python using sockets, as well as how to receive quotes via the terminal API.

The article describes a preprocessor, a scanner, and a parser to be used in parsing the MQL-based source codes. MQL implementation is attached.

The article describes the creation of a custom exchange symbol using the MQL5 language. In particular, it considers the use of exchange quotes from the popular Finam website. Another option considered in this article is the possibility to work with an arbitrary format of text files used in the creation of the custom symbol. This allows working with any financial symbols and data sources. After creating a custom symbol, we can use all the capabilities of the MetaTrader 5 Strategy Tester to test trading algorithms for exchange instruments.

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.

The article considers three methods which can be used to increase the classification quality of bagging ensembles, and their efficiency is estimated. The effects of optimization of the ELM neural network hyperparameters and postprocessing parameters are evaluated.

The widget provides websites with a detailed release schedule of 500 indicators and indices, of the world's largest economies. Thus, traders quickly receive up-to-date information on all important events with explanations and graphs in addition to the main website content.

The article deals with the algorithm of developing stock indicators based on real volumes using the CopyTicks() and CopyTicksRange() functions. Some subtle aspects of developing such indicators, as well as their operation in real time and in the strategy tester are also described.

The article describes how to add the ability to work with Microsoft SQL Server database server to MQL5-based Expert Advisors. Import of functions from a DLL is used. The DLL is created using the Microsoft .NET platform and the C# language. The methods used in the article are also suitable for experts written in MQL4, with minor adjustments.

We continue to build ensembles. This time, the bagging ensemble created earlier will be supplemented with a trainable combiner — a deep neural network. One neural network combines the 7 best ensemble outputs after pruning. The second one takes all 500 outputs of the ensemble as input, prunes and combines them. The neural networks will be built using the keras/TensorFlow package for Python. The features of the package will be briefly considered. Testing will be performed and the classification quality of bagging and stacking ensembles will be compared.

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 ZUP indicator platform allows searching for multiple known patterns, parameters for which have already been set. These parameters can be edited to suit your requirements. You can also create new patterns using the ZUP graphical interfaces and save their parameters to a file. After that you can quickly check, whether these new patterns can be found on charts.

The article considers the possibility to apply Bayesian optimization to hyperparameters of deep neural networks, obtained by various training variants. The classification quality of a DNN with the optimal hyperparameters in different training variants is compared. Depth of effectiveness of the DNN optimal hyperparameters has been checked in forward tests. The possible directions for improving the classification quality have been determined.

The article compares the classic MQL5 access to indicators with alternative MQL4-style methods. Several varieties of MQL4-style access to indicators are considered: with and without the indicator handles caching. Considering the indicator handles inside the MQL5 core is analyzed as well.

The article is an intermediate step for those who still writes in MQL4 and has no desire to switch to MQL5. We continue to search for opportunities to write code in MQL4 style. This time, we will look into the macro substitution of the #define preprocessor.

The Strategy Tester in the MetaTrader 5 trading platform provides only two optimization options: complete search of parameters and genetic algorithm. This article proposes a new method for optimizing trading strategies — Simulated annealing. The method's algorithm, its implementation and integration into any Expert Advisor are considered. The developed algorithm is tested on the Moving Average EA.

If you have newly switched to MQL5, then this article will be useful. First, the access to the indicator data and series is done in the usual MQL4 style. Second, this entire simplicity is implemented in MQL5. All functions are as clear as possible and perfectly suited for step-by-step debugging.

The article is devoted to the analysis of trading signals for the MetaTrader 5 platform, which enable the automated execution of trading operations on subscribers' accounts. Also, the article considers the development of tools, which help search for potentially promising trading signals straight from the terminal.

In this article we look at the possibility of creating a flexible news feed that offers more options in terms of the type of news and also its source. The article will show how a web API can be integrated with the MetaTrader 5 terminal.

This article describes the construction of the custom optimization criterion R-squared. This criterion can be used to estimate the quality of a strategy's balance curve and to select the most smoothly growing and stable strategies. The work discusses the principles of its construction and statistical methods used in estimation of properties and quality of this metric.

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.

This article considers new capabilities of the darch package (v.0.12.0). It contains a description of training of a deep neural networks with different data types, different structure and training sequence. Training results are included.

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.

This article is a continuation of the series of articles about deep neural networks. Here we will consider selecting samples (removing noise), reducing the dimensionality of input data and dividing the data set into the train/val/test sets during data preparation for training the neural network.

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.

Creating custom symbols pushes the boundaries in the development of trading systems and financial market analysis. Now traders are able to plot charts and test trading strategies on an unlimited number of financial instruments.

The second article of the series about deep neural networks will consider the transformation and choice of predictors during the process of preparing data for training a model.

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 an implementation of stop levels in an expert advisor in order to make it compatible with the two platforms MetaTrader 4 and MetaTrader 5.

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.

This article considers creation of documentation for MQL5 code starting with the automated markup of required tags. It also provides the description of how to use the Doxygen software, how to properly configure it and how to receive results in different formats, including html, HtmlHelp and PDF.