Integration with Python

Python is a high-level programming language with dynamic typing and automatic memory management. It is oriented towards improving developer productivity and code readability, and it belongs to fully object-oriented programming languages.

Python belongs to interpreted programming languages. It is often used to create scripts.

The syntax of the language is minimalistic, which increases the productivity of the programmer. In conjunction with the language interpretability, this allows for quick coding and immediate testing of individual program components. This helps reduce the time spent on finding and fixing errors during debugging of software products, and in some cases, it enables the evaluation of solution effectiveness at the design stage without the need to create a complete product.

At the same time, interpreted programming languages are noticeably inferior to compiled ones in terms of program execution speed. The solution to this problem lies within the Python architecture itself. It is designed so that its small core can be easily extended with a set of libraries, including those written in compiled programming languages.

Thus, Python can be compared to a constructor in which programs are assembled from ready-made blocks that are already written and defined in libraries. This explains the large number of standard libraries. Moreover, in your program, you utilize only the functionality that is necessary to solve a specific task.

An unusual feature of the language is the use of whitespace indentation to denote code blocks. If you're accustomed to the clear delineation of code blocks with curly braces in C-like languages, this might seem inconvenient. On the other hand, structuring the program code makes it visually understandable. One glance at the code is enough to determine the presence of nested blocks and their boundaries.

At the same time, this places a certain responsibility on the programmer. While in languages where the compiler checks for the presence of opening and closing braces and issues an error message if they don't match, in the case of structuring code with indentation, the responsibility lies entirely on the programmer. In this case, an incorrect structure can change the course of program execution.

Dynamic typing allows the programmer to be less concerned about data compatibility when storing them in variables, as the variables will automatically acquire the type of data being assigned.

The standard library contains a large set of useful functions. There are tools for working with text, and for writing network applications.

Additional functionality can be implemented using a wide range of third-party libraries. Among them, you can find tools for mathematical modeling, and for writing web applications, and for developing games. In addition, there is the possibility of integrating libraries written in C or C++ and other languages.

A specialized software repository has been created for software written in Python, which provides tools for easy installation of packages into the operating system. Among the repository libraries, you can find functions to suit any preference, including those for currency markets and machine learning.

Considering all the above, Python has become one of the most popular programming languages. It is used in data analysis and machine learning. As of July 2021, Python is ranked third in the TIOBE Programming Language Popularity Rankings with a score of 10.95%.

Starting the with Build 2085 version, released in June 2019, MetaTrader 5 received API for requesting data from the terminal to Python applications. Since then, this functionality has been constantly developed. Currently, you can run Python scripts directly on the terminal chart along with MQL5 applications.

At the same time, the functionality of Python applications is also expanding. You can fetch quotes from the terminal for analysis and based on the analysis results, open and close positions, and set pending orders. There's also the capability to retrieve information about the current account status, open positions, and orders. For a complete list of features, see the Python integration documentation page.

To set up a Python connection to MetaTrader 5, you first need to download and install the latest version of the interpreter from https://www.python.org/downloads/windows/.

When installing Python, be sure to check the "Add Python 3.9 to PATH%" checkbox (version may vary) to be able to run Python scripts from the command line.

After that, launch and update the MetaTrader5 module. In this case, we are talking about the Python library, not the terminal. To do this, enter the following commands at the command prompt.

After these iterations, Python scripts will be able to access operations with the MetaTrader 5 terminal.

MetaEditor also has Python support. In the editor settings on the "Compilers" tab, all you need to do is specify the location of the interpreter.

After that, you can create multilingual projects in the MetaEditor integrated environment. Such projects will include programs written in MQL and Python. Similarly, you can add support for the C/C++ language.

Python integration in MetaEditor