There are many reasons why Python has had such recent success and why it seems it will continue to do so in the future. Among the reasons are its syntax, the ecosystem of scientific and data analytics libraries available to developers using Python, its ease of integration with almost any other technology, and its status as open source. – From the book ‘Python for Finance: Analyze Big Financial Data‘ by Yves Hilpisch
Since its 1991 advent in the programming scene, Python has attained a status rare for programming languages. Object-oriented, easy to learn and apply syntax to, the resultant low maintenance costs are part of Python’s enduring reputation. Open source is an obvious advantage, effectiveness across platforms, multipurpose, garbage collection (automated), briefness in code and neat indentation are other striking features.
Python in Finance
Technology innovations have contributed significantly to greater efficiency in the derivatives market….These strong improvements have only been possible due to the constant, high IT investments by derivatives exchanges and clearing houses. – Deutsche Borse Group, 2008.
Over the past decade, with the advent of automation, technology is finally playing a part in prominent financial institutions, banks, insurance and investment companies, share trading firms, hedge funds, brokerages, etc. According to Crosman 2013 reports, banks and financial firms have spent 4.2% more on technology in 2014 as compared to 2013. Expenditure on annual financial services tech costs will cross $500 million in 2020. Prominent banks hiring developers is commonplace now, even as systems need to be maintained and constantly updated. Now where does Python fit in?
The Python syntax gels well with financial algorithms and mathematical calculations. Every mathematical statement can be turned to a single line Python code, along with over 1 lakh calculations permitted in that single line.
There is no other language as aligned to mathematics as Python, which is at home with calculations, permutations and combinations that maths and science deal with. Representing numbers, sequences and algorithms comes as second nature in Python. Check the SciPy library out, best used for technical and scientific computing, used by engineers, scientists and analysts. NumPy, a Python extension, makes great company for complex mathematical functions, arrays and matrices. At the same time, Python can also pitch in with strict coding patterns too, thus making it a balanced option, either ways.
Obtaining the same results with lesser number of people and achieving things not possible with other programming languages, is a prime Python strength. The precise and brief nature of the Python syntax, its vast treasure of tools make it a sole solid option for handling the intricacies of the financial industry.
“Cross-market risk management and trading systems are using Python (sometimes mixed with C++) and many of the banks when choosing to build front-to-bank cross asset risk systems have used this language,” reveals Stephen Grant, Cititec, managing director of technology recruiters. Financial companies using Python include Abn Amro Bank, Deutsche Borse Group, Bellco Credit Union, JP Morgan Chase and Altis Investment Management.
Python in Analytics
Analytics, be it in the field of data, web, financial has gained prominence in recent years. The application of software-technology combo to collect, arrange and study data, arrive at conclusions with regard to decision-making, business requirements, etc. Analytics to study market impact of a product, bank decisions on lending money, are just tip of the iceberg. They also have a far-reaching impact in the field of big data, security, digital and software analysis. Here’s a run through Python’s stellar role in analytics:
In this world of excessive information, only those who can dissect data to their advantage and draw insights, will benefit. Python’s role has been significant in the interpretation and analysis of big data. A great number of tools have been developed by analytics firms based on Python to restrict large data chunks. Analysts will find that Python is not hard to pick up, it is a potent medium for data management and supporting your business.
Working on data in a single language has its benefits. If you already have worked with C++ or Java, Python should be easy for you. Data analytics and Python go in tandem, there are enough Python libraries to support data analytics in the language. Pandas, with its user-friendly tools and structures is a great tool for data analysis. It certainly makes an appropriate choice for big data. Even in the field of data science, Python is upstaging other languages due to its programmer-friendly nature. The probability of data scientists been familiar with Python as compared to other languages is considered more likely.
Apart from the obvious reasons quoted in support of using Python in analytics (easy to learn, rocking community online, etc), the common usage of the language for data science and most of the web-based analytics we see today, a major cause for its widespread use in this field.
Be it derivatives or big data analytics, Python is playing a major role in both fronts. With regard to the former, Python integrates better with other system & software tools and data streams, which also includes R. Big data graphs look better on Python, it is also reliable for range, speed and assistance. Several companies use the language for predictive and statistical analysis. According to a Forbes.com (December 29, 2014 article), demand for Python-related big-data job openings rose by 96.9% in the 2014 calendar year.
Python in Artificial Intelligence
“Python, like many good technologies, soon spreads virally throughout your development team and finds its way into all sorts of applications and tools. In other words, Python begins to feel like a big hammer and coding tasks look like nails.” – Mustafa Thamer, Firaxis Games
While artificial intelligence (AI) is the ‘in’ thing nowadays and Python has made a prominent mark in this field, the language has also proved its utility in business intelligence (BI) . Coming back to AI, Python has been part of several AI algorithms, from simple two-player gaming to complex data engineering tasks. Python AI libraries play a huge part in modern software, these include NLTK, PyBrain, OpenCV and AIMA. Short code blocks are adequate for several AI software functioning. From face recognition technology, conversational interfaces and much more, Python is constantly covering new territory.
Python is the modern alternative when it comes to AI. Why? Apart from the general reasons, the language has facilitate quicker prototyping and is equipped with robust frameworks. Take Scikit-learn, for instance, the machine-learning library.
Debugging is a fast process in the language. Python also provides an application programming interface (API) from other languages. The voluminous libraries of Python are of much help, though you have to be well versed in the language to make good use of it.
Python is BI-ready, it is also a force to reckon with in cyber intelligence. Automating forensic investigation, security checks, analysis of the web are all possible with the language. For BI, there is a whole host of Python-enabled tools to make your task easier. The language has a natural inclination to algorithms, mathematical equations, making it a multi-utility medium.
Python in Mathematics
Python vs Matlab: Python is also sizing up against the numerical computing specialist language, Matlab. Many are considering transition from the latter to the former. Matlab is expensive to use, it puts a check on code portability, you can’t run your code on another computer. It uses proprietary algorithms, which implies you can’t view majority of the used algorithms, and have to believe that they have been correctly implemented.
At the same time, Matlab is backed by the scientific community, is part of many universities, though there are a section that can’t afford it, due to the expenses. Matlab is great for beginners, its package includes everything that you will ever need. Python will require an integrated development environment (IDE) and extra packages.
Python as an open source program is designed to be easy and systematic to use. Arranging data is a great experience in Python, thanks to its libraries and datatypes. As it is not proprietary, Python is easily portable; with its classes and functions enabled to be defined, as per your needs, anywhere in the program. The graphical user interface (GUI) toolkit (Qt, for instance), contributes to creating an impressive front-end. Finally, Python offers an all-round programming package.
Python continues to impress programmers and software makers worldwide with its reliability and efficiency. It has invaded new fields and made inroads in vital day-to-day software functioning. Until a successor enters the fray, Python will maintain its popularity as an all-round programming language.