I will use a rough heuristic to filter for companies that have an upwards growth slope in their revenues in PostgreSQL to further visualize in Tableau and look for patterns in the data.
Previous video link: https://youtu.be/LDW86FfSoNA
In this video I will dive into finding US stocks that are highly values vs ones that are cheap, based on market valuations, revenues, and net income. I will use a scatter plot built in Tableau desktop, and drill down into select companies to understand further why some might be valued higher than others.
In this video I will show how to visually analyze clusters within the stock market by correlating 3000 different symbols over the last 15 years, and showing the correlations using Neo4j Bloom. Firstly, I import the data into my PostgreSQL database, scaffold the symbols and dates, and impute missing values. I think upload the data to my local python Jupyter Notebook, and create a correlation matrix of all the symbols. Once I do that, I load the data into PostgreSQL where I create proper data referential integrity checks. I then upload the data into Neo4j, and after writing some basic Cypher queries, visualize it in Bloom application where I can play around with different clusters and symbols.