I scraped and cleaned data about the S&P 500 from Wikipedia usind Pandas. Then, using SQLAlchemy, I connected to MySQL and wrote the data to table. I also wrote a Python program to fetch, clean, and store data like the beta and a recommendation score from Yahoo Finance about each individual stock to MySQL. The recommendation score is based on analyst buy/sell recommendations and is designed to measure the confidence of the prediction based on the number of analysts.
Using HTML and JavaScript, I built a front-end that allows users to filter by sector and sort by the ticker, beta, or recommendation score. Then, I connected my database to the frontend using FastAPI and a Python script. I also wrote methods to create the urls for filtering and sorting. And I defined the table structures through SQLAlchemy.
Future updates will include deploying Easy S&P online via AWS tools and creating a news sentiment analyzer with AWS Sagemaker. Stay tuned!
Github Repo
