One of our team members worked in the hedge fund industry and saw first-hand the value of easily accessible financial data in making effective investment decisions. We built DoDiligence to take data from often hard to access sources and make it easily accessible for investors of all experience levels. In building DoDiligence we built the investing data resource we wanted to use ourselves.

What it does

DoDiligence brings together data from a number of sources that are often overlooked by retail investors, whether because they are unfamiliar with the sources or simply don't have the time to parse through hundreds of pages of financials and company updates. DoDiligence parses through the SEC EGGAR database, Nasdaq and other financial and organizes financial data in a format that's easy to understand and act upon.

How we built it

We built DoDiligence using the SEC's EDGAR service, the Nasdaq news service, and the tools we built to parse financial data and output it in accessible graphs and company updates-in one place.

Challenges we ran into

Coming into PennApps with many of our team first time hackers, we had to revise our idea and learn a number of new frameworks to turn our idea into reality. We had to make difficult decisions on what features we wanted to keep and cut, and how we would optimize the time we have to build the best product we can.

Accomplishments that we're proud of

We're also proud of the way that even as our idea evolved over the weekend at Penn, we managed to bring our skillsets together and build a working product.

What we learned

Our entire team learned more about Chart.js and how to address challenges when it comes to parsing data from many different formats and sources into a readable and accessible format. Coming in with different skillsets and little knowledge of the frameworks and APIs we used, we all learned new things about building products and about how to work as a team to build a project.

What's next for DoDiligence

In the immediate future we will be working on adding indicators to our charts to show how news events on certain dates affect the stock price and help people project how future events will affect the company. Further along, we are interested in adding new data sources and integrating our team members' knowledge of machine learning to analyze the positive or negative market implications of SEC filings, news reports and other events.

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