Programming is an essential skill, increasingly required in the labour market, and the finance industry is no exception. In the finance industry, great attention is paid to the analysis of the extreme amount of data that banks, insurance companies and other entities collect. Maybe we are not all aware of this, but banks collect unimaginable amounts of data on a daily basis. From data about new clients, data about all transactions, location data from which ATMs clients withdraw their money to “soft” data from meetings with clients.
Such a huge amount of data not only needs to be stored, but subsequently worked with. Much of this data is used to analyze client behavios, look for opportunities for cross-sell of related products, improve customer support and improve the products themselves.
New data analysis industry has emerged from the above-mentioned activities, which requires a specific need for software.
Of course, even today we see very often, that Microsoft Excel is used for data analysis. It is of course sufficient only for basic data analysis. Along with the use of Visual Basic for Applications (VBA) programming language, Excel can partially be used for more demanding data analysis. In this article, however, I would like to describe programming languages, that handle data analysis much more elegantly and can do incomparably more, than Excel.
Let’s start with well known Python. The Python programming language was created in 1989 and today is basically mostly associated with data analysis in finance, technology, more modern AI (artificial intelligence) and machine learning. Python is mainly used for statistical analysis, creating mathematical models, financial algorithms, etc. Its advantage is a syntax very similar to mathematical notation, but its disadvantage is its relatively low computational speed. There are many libraries in Python for charting, financial analysis, machine learning and much more.
The R programming language has recently seen a huge increase in its popularity. For use in statistics and data analysis, R is one of the most sought-after programming language. With R, you can analyze and process data to determine the relatioships between multiple variables. This is, of course, hugely used in finance industry for predicting asset price movements (such as movements in Apples stock price in the next few seconds).
Very similar programming language to R is Matlab created by MathWorks, used mainly for matrix programming. The advantage of Matlab is its pleasant graphical user interface with the possibility of extension with additional libraries (Simulink, MuPad, etc.). The disadvantage is that it’s a software developed by private company, which logically entails considerable licensing costs. It is also used for modeling, creating algorithms, computer simulations and much more.
Creating standalone applications
In addition to the above-mentioned programming languages for data analysis, there are number of other programming languages that are used to create complex standalone applications. Whether it is an application such as Portfolio Management System or applications for reporting.
The high-performance C++ programming language is popular due to its computational speed. Compared to Java (a compiled programming language), C++ is a low-level programming language, which means that it can acces computer architecture more easily. In the finance industry, thanks to its speed, C++ is mainly used by quants – programmers/mathematicians developing high frequency trading strategies for stocks, currency pairs and derivatives. In these strategies, where we are talking about microseconds, speed is extremely important.