Inspiration
We wanted people to be able to make informed decisions on financial investments, so we created Ascertain to analyze investment risks for them.
What it does
The app helps users make and manage investments with risk analysis.
How we built it
We used Python and FastAPI for the backend and PyTorch for the ML. The front end was made with React, Next.JS, HTML, CSS, and JavaScript.
Challenges we ran into
It was difficult to gain a good understanding of the domain of the Goldman Sachs challenge prompt, as none of us were very knowledgeable on finance and economics. Another challenge we faced was the fact that none of us were familiar with backend work.
Accomplishments that we're proud of
One of the things that we are proud of is the price density function that we implemented for predicting expected stock returns.
What we learned
We gained a better understanding of the field of investments.
What's next for Ascertain
We would like to add more investment options, live updates (with sentiment analysis), and fine-tooth ML models to Ascertain in the future.
Built With
- css
- fastapi
- html
- javascript
- next.js
- python
- react
- tensorflow
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