Inspiration
As a couple of college students, we wondered what the most intriguing and useful tool for other college students might be, especially in areas where beginner resources or gateway applications are limited. Many college students struggle with needing extra money and often feel scared to enter the stock market out of fear of losing what they have.
What it does
Our application helps users take their first steps into the investing world by allowing them to gain financial knowledge before deciding to invest real money.
How we built it
To create a user-friendly interface, our frontend technologies include React, Vite, TypeScript, and HTML/CSS. For functionality, our backend uses RAG to implement the chatbot, machine learning models and datasets to classify a user’s risk personality, MongoDB to keep track of users, and Python and Node.js to support the overall system.
Challenges we ran into
We faced many challenges throughout this project, but the main ones were properly implementing RAG, dealing with merge conflicts, and deploying the application on Render.
Accomplishments that we're proud of
We are proud to successfully implement the frontend the backend connections along with data retrieval using the databases to be user-specific.
What we learned
All of us gained more experience and knowledge about how the databases work and connection between frontend and the backend using the databases.
What's next for MyRisk Assistant
Graph simulations and ability to select specific funds/investment are up for future implementations.
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