QuantTest

Inspiration

The quantitative trading space is difficult to access and understand, but many students at target and non-target universities possess the skills and/ or ability to develop those skills to work in this highly competitive and highly compensated field. QuantTest takes what we see in the market serving retail traders and institutions and breaks down this access barrier by democratizing the tooling, allowing every student to build a verifiable, recruiter-ready quant track record.

What it does

QuantTest is an AI-native, showcase-first platform for student quant trading. It allows students to paper trade based on quantitative strategies common at quantitative investment and trading firms. It allows students to publish their trading performance, over time improvement and level of trade execution complexity to recruiters directly as well as simply serve as a learning platform for interested minds.

How we built it

We are utilizing lovable and other LLMs such as Gemini and ChatGPT to execute prompt engineering. We created specific example code for our backtest on a unique scenario.

Challenges we ran into

Delivering unique value beyond what competitors offer here can be difficult. Building a system that optimally presents quantitative trading to a largely unsophisticated audience presents many risks, as we need to guide users and encourage them not to assume direct risk to their own assets beyond what they possibly understand. We have had to think of how we can inform the uniformed users and prevent them from utilizing our system to take outsized personal financial risk(s).

Accomplishments that we're proud of

We successfully came together to think of something that could help people have opportunities they would never have otherwise. This is something we would use, we know others would use, and we know would bring value to others. Utilizing lovable and learning to build with AI and bring together a team was a unique and memorable experience.

What we learned

This space is crowded for retail and institutional serving firms, but students are completely untapped and underrepresented. We learned that firms often index on highly specific target firms and degrees, effectively limiting themselves to uniquely talented individuals that are likely at non-target universities or studying non-target degrees.

What's next for QuantTest

We want to create a larger menu of quantitative trading activities and strategies users can utilize to both learn and signal their ability to firms. We also want to construct community and leaderboard additions, so that users can more effectively discuss, share and display their ability.