Stock Fighter

Inspiration

As high schoolers and freshmen who have made money through part-time jobs in the last 1-2 years, we found the process of investing our earned money difficult and complicated. Sometimes it can be so complicated that we make poor financial decisions because we were never taught what to do. This is why we built Stock Fighter. Stock Fighter aims to bridge the gap between students and investing by outlining the powerful performance of "robo-advisor" investing models such as RBC InvestEase.

What it does

Stock Fighter puts a user directly against an autonomous investing model in a simulated stock market environment. Users have to handle the pile of news, events, and world situations while self-directing their portfolio under the pressure of time. On the other hand, the autonomous models are able to instantly make decisions, while RBC InvestEase displays clear profits in the simulated timeline. After the matchup of user vs computer, what the user will soon find out is that RBC InvestEase is a much stronger and self-sustainable option for students new to investing.

How we built it

Frontend: TypeScript (Vite)
Backend: Node.js
Database: MongoDB for storing data and creating a live leaderboard
Styling: TailwindCSS for frontend CSS framework
Authentication: Auth0 for user authentication

Challenges we ran into

Simulating the stock environment

Gathering realistic performances of stocks, news events, and portfolio performance
Time simulating: hard to simulate months in a couple minutes with humans making decisions → pivoted to using this as an "overwhelming" factor for the user

Accomplishments that we're proud of

Implementing an autonomous model that outperforms humans in the stock market

What we learned

We learned how to use the MongoDB Atlas Database effectively and make the most out of it
The usage of Auth0 to authenticate users with ease and have integration with our database

What's next for Stock Fighter

As the API capabilities of InvestEase improves, we can look at applying its decision-making at specific moments during a day such as would a human or an autonomous model. This would provide a stronger representation of InvestEase's capabilities than just a simulation → nudges more students towards InvestEase
Continue to iterate upon the stock market environment, making it more realistic to real-world data
Use investors' investing ideals (volatility, growth, risk) into hand when creating an autonomous model