8 Popular Python Libraries In Finance Industry

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. Fintech has its roots in domains like banking, insurance, lending, trading and other payment services. All these sectors are adopting python for solving challenging problems such as risk management, trading, pricing, compliance and analytics by using its libraries and frameworks. The Python language ability to solve complex problems at a faster rate with easier syntax has made it the ideal programming language for the financial industry.

Python is gaining popularity as the best language in data analysis other than R, Java. The great libraries Python offers make it easier to perform analysis on any dataset. With the help of Python libraries the financial professionals are getting more clear insight in their analytics and financial reports.

This list contains the most widely used Python libraries in the finance industry that every aspiring financial data scientist must know.

1.Pandas

Pandas is the open-source python library that is widely used for data analysis and data science and built on the top of other libraries such as Numpy. Its main purpose is to perform data analysis on the structured data and focuses on the fundamental data processing.

This high performance data analysis and manipulation tool offers an extension known as pandas data-reader that gathers the most updated financial data from the web like Yahoo finance, Google finance, Bing finance.

How to install:

pip install pandas-datareader

#READING FRED DATA

import pandas_datareader as pdr
pdr.get_data_fred('GS10')

2.NumPy

The numerical library like Pandas that mainly focuses on scientific computing and specializes in array operations. NumPy package comes with a wide collection of numerical functions that makes it an important library in academia and finance industry.

Note:

With the new release of numpy-financial package ( A library that includes all the financial functions) use of financial functions in NumPy is deprecated.

3. SciPy

After NumPy, there is another mathematical functions and computing library offered by Python known as Scipy. An extension of NumPy that is used for financial computation and other numerical integrations in the finance industry. If you are looking for high-level data visualization and parallel programming, then SciPy is the best option.

How to install:

pip install numpy scipy matplotlib

4. Pyfolio

One can easily evaluate the trading performance with the help of Pyfolio. It is an open-source library that provides risk analysis reports and performance results of financial portfolios based on the returns. This was developed by Quantopian and works well with Zipline which is a backtesting library (will discuss later). Pyfolio specializes in creating tear sheet models and bayesian analysis. There are various plotting features to get an overview of your portfolio.

How to install:

pip install pyfolio

#FETCHING APPLE STOCKS

stock_rets = pf.utils.get_symbol_rets('AAPL')

5. Statsmodel

Statsmodel is gaining growth and a powerful Python tool for finance and statistical analysis . You can build different statistical models with the classes and functions that Statsmodel has to offer. Other features include statistical tests and statistical data exploration. Some best models of statsmodel includes linear regression model, discrete model, time series analysis, bayesian analysis.

How to install:

pip install statsmodels

6. Pynance

Pynance will work wonders for a stock market trader. 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 this installed beforehand. As Pynance depended on these powerful libraries.

How to install:

pip install numpy
pip install pandas
pip install pandas-datareader>=0.1.1
pip install matplotlib
pip install lxml
pip install pynance

#RETRIEVING YAHOO AND GOOGLE STOCKS

import pynance as pn
ge = pn.data.get('ge', '1962', '2015')

7. Zipline

As said earlier, Zipline is the most used open-source python tool for the purpose of backtesting and live trading. This is mainly needed for the purpose of algorithmic trading. It is also maintained and developed by Quantopian. This algorithmic simulator library simulates different cost cuttings, transactions and slippages. This library allows ease of use and supports other python libraries for numerical analysis.

How to install:

pip install zipline

8. Quandl

Any python library list is incomplete without mentioning Quandl. This is the biggest and powerful marketplace where lives the financial, economical, and alternative data in modern formats for financial analysts. It is a platform developed by NASDAQ to help analysts from hedge funds, banks stay up to date with the market. The Quandl Python library will get your financial data directly into the Python.

How to install:

pip install quandl 
import quandl

Or:

easy_install quandl

Read more about Quandl here: https://www.quandl.com/

Github source: https://github.com/wilsonfreitas/awesome-quant

To sum it up, python is changing the face of the financial industry with its powerful libraries and useful tools. There are many more libraries used in Finance but most of them are built on the popular libraries Pandas and Numpy. The use of Python in FinTech has been the reason for most successful startups. Performing prediction market prices, forecasting returns, risk analysis, trading is a tedious task for financial data scientists that is simplified with the python libraries and tools.