Today's financial programmers have a diverse choice of programming languages in implementing robust software solutions, ranging from C, Java, R, and MATLAB. However, each programming language was designed differently to accomplish specific tasks. Their inner workings, behavior, syntax, and performance affect the results of every user differently.
In this book, we will focus exclusively on the use of Python for analytical and quantitative finance. Originally intended for scientific computations, the Python programming language saw an increasingly widespread use in financial operations. In particular, pandas, a software library written for the Python programming language, was open sourced by an employee of AQR Capital Management to offer high-performance financial data management and quantitative analysis.
Even big financial corporations embrace Python to architect their infrastructure. Bank of America's Quartz platform uses Python for position management, pricing, and risk management. JP Morgan's Athena platform, a cross-market risk management and trading system, uses Python for flexibility in combination with C++ and Java.
The application of Python in finance is vast, and in this book, we will cover the fundamental topics in creating financial applications, such as portfolio optimization, numerical pricing, interactive analytics, big data with Hadoop, and more.
Here are some considerations on why you might use Python for your next financial application.
Python is free in terms of license. Documentation is widely available, and many Python online community groups are available, where one can turn in times of doubt. Because it is free and open source, anyone can easily view or modify the algorithms in order to adapt to customized solutions.
Being accessible to the public opens a whole new level of opportunities. Anyone can contribute existing enhancements or create new modules. For advanced users, interoperability between different programming languages is supported. A Python interpreter may be embedded in C and C++ programs. Likewise, with the appropriate libraries, Python may be integrated with other languages not limited to Fortran, Lisp, PHP, Lua, and more.
Python is available on all major operating systems, such as Windows, Unix, OS/2, Mac, among others.
Python as a general-purpose, high-level programming language allows the user to focus on problem solving and leave low-level mechanical constructs such as memory management out of the picture.
The expressiveness of the Python programming language syntax helps quantitative developers in implementing prototypes quickly.
Python allows the use of object-oriented, procedural, as well as functional programming styles. Because of this flexibility, it is especially useful in implementing complex mathematical models containing multiple changeable parameters.
By now, you should be familiar with the NumPy, SciPy, matplotlib, statsmodels, and pandas modules, as indispensable tools in quantitative analysis and data management.
Other libraries extend the functionalities of Python. For example, one may turn Python into a data visualization tool with the gnuplot package in visualizing mathematical functions and data interactively. With Tk-based GUI tools such as Tkinter, it is possible to turn Python scripts into GUI programs.
A widely popular shell for Python is IPython, which provides interactive computing and high-performance tools for parallel and distributed computing. With IPython Notebook, the rich text web interface of IPython, you can share code, text, mathematical expressions, plots, and other rich media with your target audience. IPython was originally intended for scientists to work with Python and data.