Welcome to this project! In it, I deal with exploratory data analysis of the stock prices of four banks between 2006 (before the 2008 crisis) and the beginning of 2020 (before the covid19 crisis). This kind of information could be useful to financial professionals studying the behaviour of these four stocks between the last two major financial crisis in recent times. In this analysis, I create the distribution plots of the daily returns of each asset, calculate their historical risk (standard deviation) and their correlation to each other.
Important note: This is conceived as a training project and should not be considered as financial advice. Take care of your money.
I built this project to practice my data cleaning and EDA techniques. It should be useful to anyone seeking training with basic visualisation libraries (Seaborn, Matplotlib and Cufflinks). If that is you, I recommend that you code along and try to understand the code in each cell of the Jupyter Notebook.
In this project, I import some data from a free API (Alpha Vantage). Note that if you want to run the code in your local computer, you should first register for a free API key on Alpha Vantage and add that key to the code. There will be a comment in the code where you should include your API key. Of course, you will not be able to use my API key since it is not included in this code.
The data corresponds to the stock values of 4 US banks in the period between 01/01/2006 and 01/01/2020.
Visualisation libraries: Seaborn, Matplotlib and Cufflinks (Plotly). Data manipulation: Numpy, Pandas and Pandas_datareader.
Thanks for reading and I hope you find this training project interesting. Alex.