Metatrader 5 / Metatrader 4 for MQL5 / MQL4 articles preview

A trader may want to arrange a mailing campaign to maintain business relationships with other traders, subscribers, clients or friends. Besides, there may be a necessity to send screenshots, logs or reports. These may not be the most frequently arising tasks but having such a feature is clearly an advantage. It would be definitely difficult or even outright impossible to use convenient MQL tools here. At the end of the article, we will get back to the issue of using exclusively MQL tools to solve this task. Until then, we will use the combination of MQL and C#. This will allow us to write the necessary code relatively easily and connect it to the terminal. Besides, it will also set a very interesting challenge related to this connection.

The article is intended for beginner and mid-level developers who want to deepen their knowledge of writing libraries and integrating them with the terminal, as well as become more familiar with Google services.

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The alternative method for launching the MetaTrader terminal was already discussed in an article by Vladimir Karputov. Also, the terminal launch steps and an additional alternative method are described in the relevant documentation. Data from these two sources was used within this article, but none of the sources contains a description of how one could create a convenient GUI for the simultaneous operation of multiple terminals. This topic will be covered in the given my article.


Based on the related research, I created an extension for the terminal which enables the launch of the optimization process of Expert Advisors on multiple terminals within one computer. Further article versions will expand the possibilities of this extension through the addition of new functionality.

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Contents

• Introduction
• Definition and application
• Developing the testing tool
• Conclusion

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Table of contents

• Introduction
• Task statement and application prototype
• Implementing a strategy builder for testing
• Demonstration and example of strategy builder operation
• Conclusion

In the previous article, we considered application of Merill patterns to various data, such as to a price value on a currency symbol chart and values of standard MetaTrader 5 indicators: ATR, WPR, CCI, RSI, among others. A graphical interface was developed to explore this idea. The main purpose of the interface is to test and find efficient pattern use methods. Further, the found successful configurations should be built into trading strategies and tested. In this article, we will develop a basic toolkit for building trading strategies and testing them.

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When developing indicators, Expert Advisors and scripts, developers often need to create various pieces of code, which are not directly related to the trading strategy. For example, such code may concern Expert Advisor operation schedule: daily, weekly or monthly. As a result, we will create an independent project, which can interact with the trading logic and other components using a simple interface. With the least effort, such a schedule can be further used in custom developed Expert Advisors and indicators without serious modifications. In this article we will try to apply a system approach to Expert Advisor creation using blocks. We will also consider an interesting possibility, which will emerge as a result of our work. The article is intended for beginners.

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In the previous articles (Optimization Management (Part I) and Optimization Management (Part 2)) we considered a mechanism for launching the optimization in the terminal through a third-party process. This allows creating a certain Optimization Manager which can implement the process similarly to a trading algorithm implementing a specific trading process, i.e. in a fully automated mode without user interference. The idea is to create an algorithm which manages the sliding optimization process, in which forward and historical periods are shifted by a preset interval and overlap each other.


This approach to algorithm optimization can serve as strategy robustness testing rather than pure optimization, although it performs both roles. As a result, we can find out whether a trading system is stable and can determine optimal combinations of indicators for the system. Since the described process can involve different robot coefficient filtration and optimal combination selection methods, which we need to check in each of the time intervals (which can be multiple) the process can hardly be implemented manually. Moreover, thus we can encounter errors connected with data transfer or other errors related to the human factor. Therefore, some tools are needed that would manage the optimization process from the outside without our intervention. The created program meets the set goals. For a more structured presentation, the program creation process has been split into several articles, each of which covers a specific area of the program creation process.


This part is devoted to the creation of a toolkit for working with optimization reports, for importing them from the terminal, as well as for filtering and sorting the obtained data. To provide a better presentation structure, we will use the *xml file format. Data from the file can be read by both humans and programs. Moreover, data can be grouped in blocks inside the file and thus the required information can be accessed faster and easier.


Our program is a third-party process written in C# and it needs to create and read created *xml documents similarly to MQL5 programs. Therefore, the report creation block will be implemented as a DLL which can be used both in MQL5 and C# code. Thus, in order to develop an MQL5 code, we will need a library. We will first describe the library creation process, while the next article will provide description of the MQL5 code that works with the created library and generates optimization parameters. We will consider these parameters in the current article.

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Table of Contents

• Introduction
• Overview of Additions
• New Feature Implementation Stages
• Strategy Builder Testing and Demonstration
• Conclusion

Introduction
In the first part of the article series, we analyzed Merrill patterns and applied them to different data arrays, such as the price and price-based oscillators ATR, CCI and WPR, among others. The purpose of the article was to explore and evaluate the prospects of using the specified patterns on forex and other markets. The second part was devoted to the creation of a Strategy Builder to assemble simple strategies using the previously discussed patterns. In the third part, we will expand the strategy creation and testing functionality. We will add the possibility to work with lots in addition to points, as well the functionality for viewing testing results.


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This article features a description of the proposed statistical method for detecting seasonal patterns in financial time series. The market may have monthly season cycles, as well as intraday cycles depending on the month. The hourly analysis has shown that with a certain smoothing period (for example, a moving average), you can find certain cycles both inside sessions and when moving from one trading session to another.

One of the advantages of the approach is the possibility to work with specific market patterns and the absence of over optimization (parameter overfitting), and thus the trading system can be highly stable.

As for the disadvantages, the seasonal pattern mining process is not easy and involves operations with various combinations and cycles.

The analysis was performed for the EURUSD currency pair, with the 10-year time interval. Python source codes are attached at the end of the article in .ipynb format (Jupyter notebook). You can perform the same study for any desired financial instrument, using the attached library, and apply the obtained results to create your own trading system or to improve an existing one.

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This is the next article within a series devoted to the creation of an automated optimizer, which can perform walk-through optimization of trading strategies. The previous article described the creation of a DLL to be used in our auto optimizer and in Expert Advisors. This new part is entirely devoted to the MQL5 language. We will consider optimization report generation methods and the application of this functionality within your algorithms.

The strategy tester does not allow access to its data from an Expert Advisor while the provided results lack details, therefor,e we will use the optimization report downloading functionality implemented in my previous articles. Since separate parts of this functionality have been modified, while others were not fully covered in earlier articles, let's consider these features once again as they constitute the key parts of our program. Let's start with one of the new features: addition of custom commission. All classes and functions described in this article are located under the Include/History manager directory.

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Contents

• Modern algorithmic trading in MetaTrader 5
• Functions for working with databases
• Simple query
• Debugging SQL queries in MetaEditor
• Auto reading of query results into the structure using DatabaseReadBind()
• Accelerating transactions by wrapping them into DatabaseTransactionBegin()/DatabaseTransactionCommit()
• Handling trading history deals
• Portfolio analysis by strategies
• Analyzing deals by symbols
• Analyzing deals by entry hours
• Convenient data output to the EA log in DatabasePrint()
• Data import/export
• Saving optimization results to the database
• Optimizing query execution using indices
• Integrating database handling into MetaEditor

MQL5 is a perfect solution for algorithmic trading since it is as close to C++ as possible in terms of both syntax and computation speed. The MetaTrader 5 platform offers its users the modern specialized language for developing trading robots and custom indicators allowing them to go beyond simple trading tasks and create analytical systems of any complexity.

In addition to asynchronous trading functions and math libraries, traders also have access to the network functions, importing data to Python, parallel computing in OpenCL, native support for .NET libraries with "smart" function import, integration with MS Visual Studio and data visualization using DirectX. These indispensable tools in the arsenal of modern algorithmic trading currently allow users to solve a variety of tasks without leaving the MetaTrader 5 trading platform.

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