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.
Month: April 2023
In this video tutorial we will see how we can simulate a simple equally weighted portfolio of our basket of stocks backwards in time.
In this video tutorial we will look at stocks that have grown the most since 2020. We can further filter stocks by current market capitalization, and sort them in descending order.
In this video we will create a top down chart of all publicly available stocks in the Tesseract Analytics database and plot them against each other by their last stock price, and earnings per share (EPS) to find a relationship between how company stocks are prices, and their profitability.
In this video tutorial we will briefly analyze META, previously known as Facebook. We will create different charts looking at stock price, market capitalization, net income, revenue, cash flows, assets and liabilities.
In this video we will learn how to create a list of all the companies available in the Tesseract Analytics Apache SuperSet dataset, how to filter and search for the company that we are interested in. Then how to find if there is more than one stock symbol associated with that company, how to plot the stock prices of those symbols, and EPS (Earnings Per Share). Then we will validate that data against SEC Edgar reports to data quality validation.