In recent years, the popularity of Python for development has exploded. In fact, earlier this year, it was Python that was used by scientists to develop a new algorithm for taking the first ever photo of a black hole.
The potential use cases for this powerful and easy-to-work-with programming language seem endless. Certainly, in the financial sector we have seen incredible uptake of Python, particularly in the area of data analysis, ranging from fraud detection, and market and risk analysis, to investment decision-making and smart beta.
At FINCAD, Python has been a topic of high interest with our clients. Many are curious about how they can get started leveraging Python and about the various benefits developing with this tool can offer. As such, below I’ve compiled five of our top blog posts on Python, which give an idea of the advantages, risks and best practices for Python development.
Python has become vastly appealing to quant developers because it very easy to learn and use. As a scripting language, Python is also versatile. Its usage runs the gamut from simplistic to very complicated tasks, and it requires no compiling of data. In fact, Python is used for a myriad of different purposes including web programming, scientific computation, data analysis and artificial intelligence. Additionally, key features of Python such as exception handling, object-oriented usability and extensive support libraries have made it an excellent choice for developers. Read more about why quant developers prefer working with Python.
While it’s clear that the benefits of using Python are numerous (it is highly flexible, multi-purpose and easy to use—just to name a few), there are also potential risks to consider. For example, the immense popularity of Python has led to a vast ecosystem of tools and free libraries built up around it. NumPy and SciPy are two libraries that are very well-suited to financial analytics. But while there is obvious benefit in these libraries being free, firms must be careful to invest the time in creating a plan and controls for the use of Python, so their project does not fall off course. Read more.
Indeed, Python is becoming so popular in finance because it offers a vast ecosystem of powerful math and science libraries. Many of these libraries are free to use and are well-suited to the modeling, analysis and computation functions that are essential to derivatives finance. In this blog post, FINCAD’s Orlando Calvo Arias discusses five exceptionally useful Python libraries that are becoming increasingly popular with quants and other financial technologists in the industry. Read more.
Asyncio was introduced to the Python ecosystem in Python 3.4, and since then has seen rapid adoption, because it mitigates many of the limitations of classically multithreaded programs. The great thing about Asyncio is that it gives users the power to quickly and easily write single-threaded concurrent programs using “Coroutines.” Coroutines are like lightweight threads which allow tasks to run concurrently, but within the context of a single thread. Benefits of single threading can include improved performance without having to write (and debug) multi-threaded code. Read more.
Over the last few decades, having good Excel skills has been of paramount importance within the financial industry, almost regardless of your role. What we are now observing is that this paradigm is shifting at a rapid pace with Python becoming the tool of choice for financial industry participants. Is it possible that Python is becoming the new Excel?
Python is of course a very different paradigm when compared to Excel. Even if there is some overlap, a direct comparison cannot be made. But what we are seeing is exponential growth in financial institutions adopting the use of Python, which could make the Python skillset of equal importance to Excel for many organizations. Read more.
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