Inspiration
Investing is hard enough for beginners, but new investors are worried about putting their money to companies whose values they don't agree with. They want to be socially conscious about their investing. We want a tool that makes it easy to specify what issues a user is passionate about, and to get back a list of recommended stocks that agree with their values. It allows you to find and invest in companies that truly meet your expectations environmentally and socially.
What it does
Our tool uses a chatbot to guide a user through listing out the issues their passionate about (and how much so) and what sectors they are interested in investing in. We track over 25 different metrics to calculate a companies environmental impact, so that you can find which companies work for you. As social issues are coming to light in out society, it is important to know which companies are doing their best to promote equality in the workplace. We track 20 different metrics to give you the best overview of how a company is challenging social issues. We then use ESG information (Environmental Social Governance) to filter down stocks and create a list of companies that match those values and preferences and display their metrics to the user.
How we built it
Our website is created using a React frontend and a Flask backend. The website talks to the Finnhub API for stock information which is presented as suggestions for the user. Finally, we used Figma to create high-fidelity prototypes for the project.
Challenges we ran into
The Finnhub API endpoint that we had access to as trial users was limited and didn't provide full data for all stocks, along with it being difficult to find well performing stocks that also had ESG information. So we tested on a dataset of stocks from the S&P 500 and filtered down companies from there that had ESG ratings. We would then dynamically filter out companies that didn't have values for the metrics the user was interested in.
Accomplishments that we're proud of
We were proud of being able to take in data from both the Finnhub API and CSVs and then filtering it down using Pandas. We were also proud of making a website that followed our Figma design rather closely.
What we learned
For the backend, we learned more about investing, metrics to compare stocks, and building flask APIs (which was a lot of fun). For the front end, we were able to strengthen our wire framing skills with Figma and being better able to take those wire frames and create a functional prototype that followed the design.
What's next for Vested Interest
Adding more issues, comparing stocks based on more metrics and long term projections, and taking in free-form text input that we extract keywords from.
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