Automated Stock Screener
Project Overview
For the 2022 Wharton Global High School Investment Competition, our team needed to pick stocks for a client portfolio that fit our investment strategy. I wrote a Python script to screen candidate stocks automatically, which taught me a lot about finance and strengthened my programming skills.
The script took a list of companies and pulled each one's financial data through a free API. For any data the API didn't provide, I used web scraping with the Beautiful Soup library, so that together the two methods covered everything we needed. The script then wrote the data to a CSV file and applied our strategy's filters, highlighting in green any stock that met our team's criteria. The script screened over 1,000 stocks, replacing hours of manual research and letting our team focus on evaluating the strongest candidates for the client. We finished in the top 100 of about 5,000 teams in portfolio returns.
After the competition ended, I turned the screener into a web app that tracked each day's top stock gainers and losers. Company data was stored in MongoDB and refreshed daily with AWS Lambda. I also added a mailing list that emailed the day's screened results to subscribers at market open.