Market Buddy

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  homepage

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  Top Picks

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  Text Analyze

Inspiration

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

Built With

• flask
• react
• semantic
• ui