Python for finance is one of the best tools that we can use to support our financial analysis of companies and financial markets. The main advantage of Python is the multiple libraries that we can leverage to make our coding more efficient. In this post, I will like to highlight my top 5 libraries on Python for Finance:
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What are Python libraries?
Before going into my top 5 libraries for Python for Finance, lets quickly understand what is a Python library. A library is a piece of reusable code that someone has written and made available for everyone to use. There are over 137,000 available Python libraries. Lets have a look at the best Python libraries for financial analysis:
Regarding the application of Pandas in Python for Finance, we have already seen some of the great capabilities of pandas in some of my previous posts. Below are a couple of examples that will show you how powerful Pandas is for data analysis:
My favourite Python library is Pandas. Pandas is one of the best libraries in Python. If offers amazing capabilities to clean, handle and analyse data. In the official documentation, we can find the key aspects of the library.
By using Pandas we can transform and analyse stock prices with only a few lines of codes. For instance, we can search for superperformer stocks with Pandas.Or resampling daily stock prices to monthly stock prices to aggregate stock prices data.
Best Python Libraries – Pandas
Plotly is my second favourite library in Python. It is a graphic library that makes super easy to plot financial data into very nice looking graphs. And not only that, with Plotly we can also build very sophisticated financial dashboards using Python.
Have a look at the post showing how to build a financial dashboard in Python using Plotly. This post will showcase the simplicity and potential of Plotly.
Python libraries for Finance – Plotly
Although my favourite plotting library in Python is Plotly. Matplotlib is also great due to the great complement with Pandas. With a simple line of code, we are able to plot multiple Pandas columns into a chart.
In my post How to plot a Pandas DataFrame using Matplotlib, you will see how easy is to plot financial data using Python and Matplotlib.
Another top library for financial analysis is Numpy. Numpy is a great library to work with matrixes and arrays. Its numerical computing capabilities are a great fit to compute portfolio risks and returns and to find optimised portfolios. Have a look at my previous posts on Portfolio optimisation with Python to see the true power of Numpy.
Statsmodels is a great Python library to perform statistical analysis and tests in Python. It has nothing to envy to other statistical softwares like SPSS. Although I do not have many posts in my blog on statistical analysis, you can still have a look how statsmodels can help us to test how different stocks are correlated among them.
How to calculate Stock Returns
And these are my top libraries in Python. With this five python libraries for financial analysis you will be able to gain very valuable insights about particular stocks and market trends. I look forward to hear which Python libraries are your favourites.
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