Inspired due to the recent events of a lot retail investors joining into the stock market, social media/discussion groups like subreddits are a place in which market demand and supply can be calculated and the need to understand this type of data in relation with the stock market
What it does
Given a stock based subreddit, Market Buddy scrapes hot posts off there, filters out the top stocks by giving visualizaitons, and analyzes post texts to state whether a subreddit is bullish or bearish.
How we built it
The frontend was built in React using Semantic UI while the backend was just a Flask API connected with the Reddit Python library to help get posts and comments.
Challenges we ran into
Building a valid model to classify text as bullish or bearish, there were lots of complications in finding out the intent and training the model.
Using random forests with attribute selection, I wasn't fully able to implement the NLP model as it needed more time to be trained properly on labeled data
Also changing backends along the way from django to flask -> huge hassle moving over .py files and API endpoints
Accomplishments that we're proud of
Having a working product across three different subreddits being applicable widely
What we learned
Learned about a new framework flask and the reddit praw api!
What's next for Market Buddy
Build out a proper text-based model to classify post submissions