HedgeFun

Paper trade like a pro.

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Inspiration

As broke college students losing money on stocks, we dream to make it big like some of the world's most famous investors.

What it does

Gain valuable insights into how the world's most famous investors trade. With AI-driven insights, and detailed explanations behind trades. The platform should share insights into investing and trading.

How we built it

We used Next.js for the frontend and a bunch of frontend technologies like Tailwind, RadixUI, and Recharts. For the backend, we used a simple Python and Flask API powered by Google Gemini-2.0.

We used pre-collected data from the Yahoo Finance API to speed up data fetching. We extracted historical price data for the Mag7 stocks from Yahoo finance and balance sheet data from discountingcashflows.com

Challenges we ran into

We ran into many challenges throughout the project:

  • We encountered a token issue which forced us to reformat the data we used.
  • Deployment issue which forced us to use localhost

Accomplishments that we're proud of

We're proud of achieving an MVP from a fairly ambitious initial idea in less than 12 hours.

What we learned

We learned a lot about collaborative software development. We tried using Git efficiently, managing the scope of our project, and discussing between front and backend developers.

Technically, we also delved more into backend development as that was the most difficult. It was a first for some of us to start a Python virtual environment, run a Flask app, etc.

What's next for HedgeFun

For HedgeFun, we'd like to extend beyond the Mag7 stocks we currently support, utilize machine learning to provide evaluate trading strategies and provide accurate forecasts, and deploying our project to retail investors. globally.

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