Winability Market Watch

Recently, the stock market has seen a sharp uptick in casual traders. Novice traders are often susceptible to being swayed by speculation instead of well-established metrics. We aim to correct that!

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Inspiration

We stumbled across the popular subreddit r/wallstreetbets and were shocked to see so many people losing money over random speculation. We realized many of these people were novices who had jumped into the stock market on promises of huge wealth increases.

What it does

It webscrapes historical stock data from two different websites, and calculates a price-earnings ratio. Then, it normalizes the data to find the probability of winning on a particular investment, and displays it to the user.

How we built it

We created a backend in Python to scrape data from Yahoo Finance, TDAMeritrade, and Macrotrends APIs and obtain their stock information. Some of this was saved to csv files which we could do calculations on. We used python scripts to synthesize this information into a probability using numpy calculations. This information was organized through the Django web framework with HTML and CSS, and we dynamically displayed the information in a real-time webpage. Users are presented with a list of automatically-selected stocks on the front page and through a search form are able to get information about any specific stock (including unlisted ones) by inputting a valid ticker.

Challenges we ran into

It was challenging to coordinate code across four different programmers. We had compatibility issues between OSs, as well as between different python versions. This meant that the same code would sometimes appear and function differently for each of us. Since we all were inexperienced with web development, we weren’t sure how to organize the project so some important files were all over the place. These all needed to be dealt with over online communication.

Accomplishments that we're proud of

We managed to accrue a significant amount of historical data, parse it into useful metrics, and even connect this to a front-end API and website that updated automatically, which none of us had ever done before. This was the first hackathon for many of us and we weren’t sure if we could handle such an ambitious project in such a short period of time.

What we learned

We gained a lot of experience with how the Django framework resolves URLs, uses dependencies, and combines templates to display websites. We also gained a lot of knowledge on building a web application with the built-in Django API and the framework. We learned a lot about Python packages as well such as pandas, scipy, numpy, yfinance, sys and os.

What's next for Winability Market Watch

At the moment Winability cannot display all the best NASDAQ tickers at once. We hope to expand this capability in the future so users can see information on any stock they may be interested in investing in. We also were not able to finish a feature that showed the distribution of the normalized data because of compatibility issues with Django. Given more time we think this feature could give a more complete picture to users about how to approach an investment. We also would like to host Winability online.

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