Inspiration

The Game-Stop frenzy inspired us to probe into how the sentiments of Redditors on the famous (infamous?) r/WallStreetBets subreddit could sway the direction of particular stocks. We wanted to see how combining this general sentiment of the Redditors about the top five most talked about stocks for each day, with fundamental information about the said stocks, could predict the general direction of the price in the near future.

What it does

It runs sentimental analysis and fundamental (financial) analysis into the top five most talked about stocks for each day and combines both the insight to determine whether those stocks are bullish or bearish and hence, advising whether or not they should be stocks one should look out for!

How we built it

We fetch current Reddit-comments and posts using the Reddit Api. For the purpose of training our model on past r/wallstreetbets comments and posts, we use the Pushshift Api. We then select the top five most talked about stocks for each day. Following that, we use the sentimentIntensityAnalyzer from the vaderSentiment library to gauge whether the comments for particular stocks carry a positive or negative connotation. We do so by computing a Total Compound Score for each stock. After that, we extract fundamental data about the stocks, such as P/E ratio, Market Cap etc, and combine the sentiment data and the fundamental data into a single data frame. Finally, we train a RandomForrest classifier, and use it to produce our final predictions.

Challenges we ran into

Cleaning the data we gathered was the most challenging aspect of our project.

What's next for Wall-Street-Sentiments

We want to expand our project by training our model with more data, such as top stocks and their associated comments from the past two years in order to better predict the bearishness or bullishness of stocks. Furthermore, we plan to expand our webapp by incorporating user account creation and signing up for newsletters, through which the result of each day will be automatically sent to users from our mailing list. From then on, we can build upon our project further and include more features to our data set to give more accurate predictions

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