Baby Quant

All nighter!

Inspiration

Our inspiration came from a shared interest in quantitative finance and a common frustration: powerful financial tools and insights are often locked behind complex math, jargon, and professional platforms. We wanted to change that. Our goal was to build an educational platform that makes finance, especially quantitative and data-driven decision-making, accessible to everyday users. By combining intuitive design with modern technologies, we aimed to bridge the gap between advanced financial concepts and practical understanding, empowering users to learn, experiment, and grow their financial literacy in an engaging and approachable way. Our product aims to explain the complex world of trading algorithms and data-driven trading, in a simple method where even a baby could use our platform to trade stocks (on paper, of course.)

What it does

Baby Quant is a immersive quantitative financial platform where users can create financial algorithms for stock trading using a variety of data sources and parameters. These data sources include, reddit databases and Finhub news sources, using a custom sentiment analysis algorithm to drive decisions on different stocks. The algorithms are also based on market timing metrics and investment indicators supporting data driven algorithm deployment.

How we built it

Baby Quant was built using Python for the backend and SQL for database management. The backend utilized Reddit API and Finhub API for data collection. The frontend was built using Blockly, JS, CSS, and HTML.

Challenges we ran into

We had challenges building an accurate sentiment analysis model for Reddit and Financial news data. We also faced problems properly developing the database to handle all sentiment and financial metric datapoints. Implementing Blockly using limited documentation for the application was another hurdle to overcome, though with determination and some caffeine we were able to develop our ideas fully.

Accomplishments that we're proud of

Taking showers....not really ;) Building a working prototype of Baby Quant that incorporates various important data sources and investing parameters. Learning more about API implementation, large-scale database management, and creating complex algorithms.

What we learned

API implementation and best practices Using Blockly to create a functioning UI that maps code to Python How to develop simple quantitative trading algorithms based on sentiment analysis and investment metrics Investment terminology and calculation of investment indicators Software development best practices

What's next for Baby Quant

Making the processing faster, integrating insider trading, adding more types of code blocks, improving UI experience.

Built With

Submitted to

SwampHacks XI

Created by

I worked on the backend. I worked on reddit api and news api. I also helped produce the sentimental analysis algorithm.

Jaykumar Patel
I worked on the Backend, implementing a Finhub news API for sentiment analysis and financial data for the algorithm. I also worked on getting financial indicators from price data, creating the back-end code for calculating various investment indicators. Worked on creating the DevPost descriptions of the project as well.

Sai R
I worked on the core backtesting logic, the frontend, and integrating the backend code with the frontend code. I implemented the blockly interface and defined the custom block types and schemas.

Saaketh Kesireddy
I worked on the backend processes of the data collection of our application. I developed the tools for data collection of Indicators and Sentiments for financial analysis.

Janav Saluja

Updates

Jaykumar Patel started this project — Jan 25, 2026 04:35 AM EST

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