Python For Finance - Top 5 Libraries To Learn - JournalDev

We know how crucial finance is in one’s life. Today, with the help of technology we have many advancements in this industry. It may be banking, Fintech, Payments, and much more. Almost we can make any transaction in seconds at our fingertips.

Top Libraries to use Python For Finance

We as Pythonista, love to work on as many domains as possible. Today, let’s discuss what python offers to work in the domain of finance. Shortly, Python for finance. We will be discussing different libraries that python offers to work with financial data.

1. Scipy

One of the first libraries which come to my mind is the SciPy. It is scientific Python. Using this library you can do all the scientific complex calculations using python.

The SciPy module in some way an extension of the numpy module. Which is also used for numerical computation using python.

It is an open-source library.

It is used for both mathematical and scientific problems. One fascinating thing about the SciPy library is that you can even visualize the data using some advanced commands.

Therefore, modules supported by the SciPy are linear algebra, differential calculus. The others include Fourier transforms, signal, and image processing.

Official documentation of SciPy – SciPy.org

More read: Python SciPy Tutorial

2. Scikit-Learn

Scikit-learn is the go-to python library for machine learning and data science. It has a huge collection of modules that will assist you in data science projects.

It offers modules that are beyond finance and a lot more. You can work with all kinds of machine learning models. It may be a classification, regression, and even time series analysis as well.

The ARIMA and SARIMA models are the go-to options. They will help you with stock analysis and price forecasting. One of the most valuable additions to the list of libraries for python for finance.

You can process the data, manipulate it and eliminate the anomalies in it. This library also has official documentation and the GitHub repository. It includes plenty of tutorials on using this.

The financial data is huge in nature. with the help of many algorithms offered by scikit, you can process that gain useful insights by visualizations as well.

Official documentation of Scikit-learn – Scikit-learn.org

More read: Python SciKit Learn Tutorial

3. Pyfolio

The pyfolio library in python is mainly used to analyze financial portfolios. The risk associated with it. You can also use this library as a risk analyzer.

The basic principle with it is the Bayesian analysis. This library is developed by Quantopian Inc. In the year 2015 as an open source project.

In other words, It consists of many specialized plots to visualize the risks of your portfolio in a precise way.

The pyfolio library offers many statistical and mathematical functions. You can use them to interpret the data. You can work on the time series analysis for forecasting as well.

Official documentation of Pyfolio – Pyfolio

More read: Pywedge for EDA

4. PyAlgoTrade

This is the very first module that incorporates the financial data assessment for data science in python. It is also one of the top python algorithmic trading libraries.

The main focus of this library is backtesting and paper trading. It supports some of the functionalities such as stop loss and works on multiple markets.

Similarly, the major features of this library are its technical indicators. SMA, WMA, and EMA. It also offers the performance metrics such as drawdown analysis and Sharpe ratio.

Therefore, Official documentation of PyAlgoTrade – Pyalgotrade 2.0

More read: Trading bots

5. FinmarketPy

This is our final library for python for finance. The finmarketPy is an excellent library that you can use for market analysis and strategy analysis.

The best thing is, it has its database and templates to assist in your work. You can directly import the templates for quick analysis.

You can use various parameters. There are many available in the FinmarketPy to observe the market data.

For instance, you can copy all your financial data and analyze the strategies.

However, FinmarketPy is build on many other libraries such as SciPy and Numpy.

Official documentation of FinmarketPy – FinmarketPy.org

More read: GitHub Repo for more Tutorials

Python For Finance – The End

In conclusion, For people working in Finance domain, nothing can be impressive as libraries for python for finance. Python is universal, that to for finance, it offers many top notch libraries as discussed above. Similarly, if you are data professional work in the finance domain or a trader / investor. Who is interested in python for finance, these are the best libraries for you to work on finance data.

Above all, I hope you love these libraries as much as I do.

That’s all for now. Happy Python!!!

More read: Financial data with python