## Inspiration

Civic authorities are constantly working to improve transport for their cities. Their efforts, however, often lack an important piece: access to accurate data about how current transport systems are used. A better understanding of how people commute, share rides, and take public transport today will inform how these systems can be better tomorrow.

## What it does

In essence, Argonaut is a marketplace: it helps users part with any personal data that they're willing to share, in return for monetary incentives in the form of Algorand coins (Algos). Authorities can buy data packs - which are anonymised and aggregated clusters of such data submitted by multiple users, for a price, which is then distributed amongst each of the contributors equally. Here's the key: the blockchain keeps a track record of what organisation is requesting and accessing what data, even while maintaining complete anonymity on the part of the sellers.

## How I built it

We used React.js to build the frontend, a Node.js/Express powered backend; Python to scrape through personal data dumps accessed via Google Takeout, and the Algorand JavaScript SDK to implement the blockchain-based transaction management system.

## Challenges I ran into

The principal challenge we faced was how to implement Blockchain for managing access to personal data. Another important challenge was to do with making sure that the anonymity of users sharing their data is maintained, whilst also keeping any shared data private.

## Accomplishments that I'm proud of

This was our first deep dive into Blockchain technology, and we were glad to be able to use Algorand's APIs and Dev Tools to be able to build a platform aimed at enhancing urban decision making, while simultaneously helping people like you and us take back control of their personal data.

## What I learned

We learned about the power of the blockchain, and how decentralised ledgers have applications far and beyond the traditional financial markets that we've currently seen them in.

## What's next for Argonaut

Argonaut can be scaled up to include the ability for users to connect Google Maps/Uber/Lyft and a lot of other apps directly to the platform so that their periodic data dumps can be seamlessly and automatically streamed into the platform, versus the current requirement of having to upload personal data dumps.
## Inspiration

Course selection is an exciting but frustrating time to be a Princeton student. While you can look at all the cool classes that the university has to offer, it is challenging to aggregate a full list of prerequisites and borderline impossible to find what courses each of them leads to in the future. We recently encountered this problem when building our schedules for next fall. The amount of searching and cross-referencing that we had to do was overwhelming, and to this day, we are not exactly sure whether our schedules are valid or if there will be hidden conflicts moving forward. So we built TigerMap to address this common issue among students.

## What it does

TigerMap compiles scraped course data from the Princeton Registrar into a traversable graph where every class comes with a clear set of prerequisites and unlocked classes. A user can search for a specific class code using a search bar and then browse through its prereqs and unlocks, going down different course paths and efficiently exploring the options available to them.

## How we built it

We used React (frontend), Python (middle tier), and a MongoDB database (backend). Prior to creating the application itself, we spent several hours scraping the Registrar's website, extracting information, and building the course graph. We then implemented the graph in Python and had it connect to a MongoDB database that stores course data like names and descriptions. The prereqs and unlocks that are found through various graph traversal algorithms, and the results are sent to the frontend to be displayed in a clear and accessible manner.

## Challenges we ran into

Data collection and processing was by far the biggest challenge for TigerMap. It was difficult to scrape the Registrar pages given that they are rendered by JavaScript, and once we had the pages downloaded, we had to go through a tedious process of extracting the necessary information and creating our course graph. The prerequisites for courses is not written in a consistent manner across the Registrar's pages, so we had to develop robust methods of extracting data. Our main concern was ensuring that we would get a graph that completely covered all of Princeton's courses and was not missing any references between classes. To accomplish this, we used classes from both the Fall and Spring 21-22 semesters, and we can proudly say that, apart from a handful of rare occurrences, we achieved full course coverage and consistency within our graph.

## Accomplishments that we're proud of

We are extremely proud of how fast and elegant our solution turned out to be. TigerMap definitely satisifes all of our objectives for the project, is user-friendly, and gives accurate results for nearly all Princeton courses. The amount of time and stress that TigerMap can save is immeasurable.

## What we learned

* Graph algorithms
* The full stack development process
* Databases
* Web-scraping
* Data cleaning and processing techniques

## What's next for TigerMap

We would like to improve our data collection pipeline, tie up some loose ends, and release TigerMap for the Princeton community to enjoy!

## Track

Education

## Discord

Leo Stepanewk - nwker#3994
Aaliyah Sayed - aaligator#1793
## Inspiration

Over the past year, the field of investing has seen meteoritic growth. Individual retail investors now have serious influence over the stock market--as we all saw with gamestop. That being said, institutional investor’s have sophisticated technologies and approaches that the average retail investor could only dream of. We created alGOAT to help bridge that gap, empowering the everyday investor to have access to cutting edge technologies.

## What it does

Creators can publish their trading algorithms to alGOAT. Then, alGOAT back tests the algorithm over the past 5 years of market data. This is published along with some other statistical metrics, so investors can educate themselves on the performance of algorithms, while keeping the underlying code a secret. When someone invests in an algorithm, we charge a flat investment fee, to cover server costs.
Once someone invests, the algorithm runs on our servers to determine their investments, which are automatically made on their account. They can terminate their investment at any time. Upon withdrawing funds from an algorithm, the “Creator’s Cut” of the profits, which can be determined by the algorithm creator goes to them, 3% of the profits goes to the charity associated with the algorithm, and also determined by its creator, and the rest of the profits (expected to typically be in the 75%-90% range) goes to the investor.
One key element of this model is that we do not act as a fund and the creator does not act as a financial advisor. The investor makes their own purchases, which are determined by the algorithms they chose.

## How we built it

The frontend is a flask web app, designed in figma and implemented using python, html, and css.
We created sample data and created algorithm metrics and charts in python, using numpy and matplotlib, respectively.
We hosted the app using a free Heroku server.

## Challenges we ran into

One challenge that we ran into is the $25k account balance minimum requirement for day trading. We decided to incorporate algorithms that do not use day trading and algorithms that do use day trading in their implementations to allow for any investor to enter the alGOAT marketplace and start investing in algorithms.
We also ran into a challenge that our alGOAT, to become fully working, will require some network and server setup that was way beyond the scope of this weekend (see the what’s next for more info). We decided it was best to focus on fleshing out the idea and creating the UI instead of trying (and almost certainly failing) to hack together a stock exchange connection.

## Accomplishments that we're proud of

We are particularly proud of creating a pleasant-looking, easy-to-use front end. Every member of our team had very little front-end experience, so this was something we put a particular emphasis on.
We also are very proud of creating a business plan to go with our tech. We genuinely believe that our model, although complicated, would actually work, and will continue to work on it after this weekend.

## What we learned

Design, design, design. We learned how to mock up a nice UI and implement it. We learned how to create not only an interesting idea but a business plan that works with governmental regulations.

## What's next for alGOAT

First, we need to work on the servers alGOAT needs to be able to interact with the stock exchanges. We need to set up a partnership with a brokerage, so the algorithms have an endpoint to connect to. Beyond that, it’s a matter of recruiting young technologists to start making algorithms (maybe the HackPrinceton devpost will help) and then getting investors onboard (which a post on r/WallStreetBets will surely help with).