Python programmers are in high demand in banks and hedge funds. Fortunately, this programming language is easy to learn. However, before you get started with Python, you need to know some things, especially if you want to use it in a financial context.
Python 3 or Python 2?
When new versions of languages appear, they usually involve incremental updates and are backward compatible with earlier versions. This means that all your existing code will still work. This is not the case with Python version 3 which has some important modifications and does not work at all with Python 2. Strangely, Python 2 continues to be supported, with more than 30 updates released since Python 3, almost 10 years ago. The new versions of Python 2 are now stopped, but they are still widely used in the financial sector.
If you have the choice, Python 3 is without a doubt the best one to use for all new projects, the most recent one being often the best: all the interesting new features of later versions are worth using. However, you could end up working with Python 2 code so it’s important to master both variants if you want to use Python in a finance job.
Packages not included, but easy to get
You should also know that the Python Software Library is quite light. You will need to import pre-packaged libraries if you want to do something interesting. These libraries include features to perform most math operations, manage calendars, import and manage data, and perform common system tasks.
However, the real power of Python comes when you start downloading some of the many freely available third-party libraries. For serious financial work, you will need numpy (to handle operations on large tables), scipy (statistical and advanced math functions) and matplotlib (data visualization). Data scientists interested in machine learning will probably want to look into ensorflow . As for Panda , it’s a must for data manipulation that has a solid financial pedigree – it was originally developed by the giant hedge fund of AQR capital management.
Python is slow. But it’s easy to mix with C
Programmers accustomed to the blazing speed of C or C ++, or the relatively fast pace of Julia or Java, will find Python a bit slow (although it’s still a little faster than R and Matlab, two popular languages of the quantitative finance).
Programmers love the speed and efficiency of their code, but most codes do not have to run as fast. However, Python will certainly be too slow when it comes to functions executed repeatedly on large data sets or latency-sensitive trading algorithms.
Fortunately, it is extremely easy to write C or C ++ functions quickly and then integrate them into your Python modules. Learn to do that too.
Python loves big data
Financial institutions seeking an advantage in today’s markets are focusing on new sources of data. These alternative data sources have one thing in common: they are bulky. Using Twitter feed data to predict market opinion is a good idea, but there are about 500 million new tweets a day. It’s a huge amount of data to store, process and analyze.
Fortunately, Python integrates perfectly with the Big Data ecosystem with packages that you can use to interact with Spark and Hadoop. Python also has APIs for NoSQL databases such as MongoDB, as well as for all major cloud storage providers.
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