Best Python Libraries For Financial Modeling: The rise in the fintech industry amid coronavirus has increased globally. According to reports, over a billion dollar investment will be done in Fintech companies in the next 3–5 years. Python programming language is an excellent tool for developing new financial technologies. A wide range of software packages exists to help users build their own financial models, from crunching raw numbers to creating aesthetically pleasing, intuitive graphical user interfaces. This article provides a list of the best python packages and libraries used by finance professionals.
All financial models rely on crunching numbers. NumPy is the fundamental package for scientific computing with Python. It is a first-rate library for numerical programming and is widely used in academia, finance, and industry. NumPy specializes in basic array operations.
The panda’s library provides high-performance, easy-to-use data structures, and data analysis tools for the Python programming language. Pandas’ focus is on the fundamental data types and their methods, leaving other packages to add more sophisticated statistical functionality.
SciPy supplements the popular Numeric module, Numpy. It is a Python-based ecosystem of open-source software for mathematics, science, and engineering. It is also used intensively for scientific and financial computation based on Python. This package provides functions and algorithms critical to the advanced scientific computations needed to build any statistical model.
Pyfolio is a Python library for performance and risk analysis of financial portfolios. It works well with the Zipline open-source backtesting library. the pyfolio package provides an easy way to generate a tearsheet containing performance statistics. These statistics include annual/monthly returns, return quantiles, rolling beta/Sharpe ratios, portfolio turnover, and a few more.
The statsmodels package builds on these packages by implementing more advanced testing of different statistical models. An extensive list of result statistics and diagnostics for each estimator is available for any given model, with the goal of providing the user with a full picture of model performance. The results are tested against existing statistical packages to ensure that they are correct.
Zipline is a Pythonic algorithmic trading library. It is an event-driven system that supports both backtesting and live trading. It is a formidable algorithmic trading library for Python, evident by the fact that it powers Quantopian, a free platform for building and executing trading strategies.
It is an open-source python package that retrieves, analyses, and visualizes the data from stock market derivatives. With this library in hand, you can generate labels and features for machine learning models. To make this library work, it is advised to install numpy, pandas, and matplotlib or have any of these installed beforehand.
Financial data sources, optimal data structures, and statistical models and evaluation mechanisms for financial data are established by the aforementioned Python packages for finance. A crucial Python tool for financial modeling is data visualization, but none of them provides it.