Is python useful in finance?

Python programmers are in high demand among banks and hedge funds. Fortunately, the language is easy to learn - it is often used in British primary schools to teach the basics of programming. However, before you encounter Python for the first time, there are a few things you should know - especially if you want to use it in a financial context.

Python is a programming language that has a huge reputation in the financial industry. The largest investment banks and hedge funds are using it to build a wide range of financial applications, including core trading projects and risk management systems. (Recommended learning: Python video tutorial)

The functions are not included, but there are libraries

You also need to know the core Python The library is very lightweight. If you want to do anything interesting, you need to import prepackaged libraries. These libraries contain functions to perform most mathematical operations, import and process data, and perform common system tasks.

However, the true power of Python comes when you start downloading the many third-party libraries that are freely available. For finance work, you'll need numpy (handles operations on large arrays), scipy (advanced statistical and mathematical functions), and matplotlib (data visualization). Data scientists interested in machine learning may want to look into tensorflow. Pandas is a necessity for data manipulation - it was originally developed at the management of giant hedge fund AQR Capital.

Users may wish to view the Anaconda distribution in a neat pre-packaged environment, which includes all the above packages and more.

Python is slow. But it's easy to mix it with C

Programmers who are used to the lightning speed of C or C++, or those who are relatively fast in Julia or Java, will find Python a bit sluggish (although it's still faster than R and Matlab are faster, both are popular languages ​​in quantitative finance).

Programmers like to brag about how quick and fast their code is, but most code doesn't have to be fast. However, Python will definitely be too slow for functions that are run repeatedly on large data sets or latency-sensitive trading algorithms.

Luckily, it's very easy to write fast C or C++ functions and then embed them into Python modules. Learn how to do this.

Python loves big data

Financial companies looking to gain an edge in today’s market are looking to new sources of data. These alternative data sources have one thing in common - they're big. Using Twitter feed data to predict market sentiment is a cool idea, but there are about 500 million new tweets every day. This requires large amounts of data to be stored, processed and analyzed.

Fortunately, Python fits well into the big data ecosystem, with packages available for interacting with Spark and Hadoop. Python also provides APIs for NoSQL databases such as MongoDB, and provides APIs for all major cloud storage providers.

Don’t be afraid of the GIL

The GI is notoriously Python’s Achilles’ heel. The interpreter can only execute one thread at any time, creating a bottleneck that slows down execution and does not take advantage of modern multi-core CPUs. However, GIL rarely causes problems in practice. Most real-world programs spend more time waiting for input or output.

The GIL affects large, computationally intensive operations, but only a masochist would try to run them on a desktop or laptop. It makes more sense to parallelize the code and then distribute it to a local cluster or cloud computing provider.

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