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How you are greeted with the start page.
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An example of the language in practice.
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The results yielded from a hypothesis constructed comparing Amazon to a Gold ETF.
General track*
Inspiration
As members of CU Quants (a club for all things quantitative finance at CU) we can see how cumbersome it can be to develop and test even simple trading strategies. So, we sought to develop a streamlined method for doing so by designing a novel block-based programming language.
What it does
Provided two user defined events, the web app allows users to easily compose a hypothesis about how two stocks are related and quickly back test it on large data sets to determine its truthiness (as well as provide other interesting insights).
How we built it
Node app with EJS, Express, and Python for server-side data processing. The UI was built using the CSS framework UIKit.
Challenges we ran into
Integrating the front-end with the back-end after working on them entirely separately as partners. Additionally, we would've hoped to get more commands built into the language.
Accomplishments that we're proud of
Creating an intuitive AI which can be parsed by the browser processed by the server as an end-to-end data pipeline. Pretty sweet to make in 24 hours!
What we learned
The importance of carefully selecting front-end development tools for ease of use as your project scales.
What's next for Tulip
Starting a quantitative hedge fund 😎
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