Why Python?

Python is the most widely used programming language in the world (one-third of new software development uses this language):

This language is very simple to learn. Python is an interpreted, high-level programming language with type inference. Unlike C/C++, where you need to focus on memory management and the hardware features of the machine you are using to code, Python takes care of the internal implementation, such as memory management. As a result, this type of language will ease the focus on coding trading algorithms. Python is versatile; it can be used in any domain for any application development. Since Python has been widely used for years, the community of programmers is large enough to get many critical libraries for your trading strategy, ranging from data analytics, machine learning, data extraction, and runtime to communication; the list of open source libraries is gigantic. Additionally, on the software engineering side, Python includes paradigms used in other languages, such as object-oriented, functional, and dynamic types. The online resources for Python are unlimited, and tons of book will drive you through any domains where you can use Python. Python is not the only language using in trading. We will preferably use Python (or eventually R) to do data analysis and to create trading models. We will use C, C++, or Java in trading for production code. These language will compile source code into executable or byte codes. Consequently, the software will be one hundred times faster than Python or R. Even if these three last languages are faster than Python, we will use all of them to create libraries. We will wrap these libraries to be used with Python (or R).

When choosing Python, we also need to choose the version of the language. While Python 2 is the most commonly used Python standard, Python 3 should take over in a few years. The Python community develops Python 3 libraries. Tech firms have started their migration toward this version. After 2020, Python 2.X will no longer be maintained. Therefore, if you are a new programmer, it is recommended to learn Python 3 over Python 2. 

Both Python and R are among the most popular languages for assisting quantitative researchers (or quantitative developers) in creating trading algorithms. It provides a ton of support libraries for data analysis or machine learning. Choosing between these two languages will depend on which side of the community you are on. We always associate Python with a general-purpose language with an understandable syntax and simplicity, while R was developed with statisticians as an end user by giving emphasis to data visualization. Even if Python can also give you the same visualization experience, R was designed for this purpose.

R is not significantly more recent than Python. It was released in 1995 by the two founders, Ross Ihaka and Robert Gentleman, while Python was released in 1991 by Guido Van Rossum. Today, R is mainly used by the academic and research world.

Unlike many other languages, Python and R allows us to write a statistical model with a few lines of code. Because it is impossible to choose one over the other, since they both have their own advantages, they can easily be used in a complementary manner. Developers created a multitude of libraries capable of easily using one language in conjunction with the other without any difficulties.

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