This program goes thru reddit, finds the most mentioned tickers and uses Vader SentimentIntensityAnalyzer to calculate the ticker compound value.
subs = [] sub-reddit to search
post_flairs = {} posts flairs to search || None flair is automatically considered
goodAuth = {} authors whom comments are allowed more than once
uniqueCmt = True allow one comment per author per symbol
ignoreAuthP = {} authors to ignore for posts
ignoreAuthC = {} authors to ignore for comment
upvoteRatio = float upvote ratio for post to be considered, 0.70 = 70%
ups = int define # of upvotes, post is considered if upvotes exceed this #
limit = int define the limit, comments 'replace more' limit
upvotes = int define # of upvotes, comment is considered if upvotes exceed this #
picks = int define # of picks here, prints as "Top ## picks are:"
picks_ayz = int define # of picks for sentiment analysis
It took 216.65 seconds to analyze 5862 comments in 80 posts in 4 subreddits.
Posts analyzed saved in titles
10 most mentioned picks:
GME: 197
BB: 72
FB: 56
PLTR: 36
TSLA: 25
PLUG: 17
RC: 15
NIO: 14
SPCE: 10
TLRY: 10
Bearish Neutral Bullish Total/Compound
GME 0.087 0.763 0.150 0.161
BB 0.058 0.768 0.175 0.261
FB 0.119 0.708 0.173 0.127
PLTR 0.062 0.804 0.134 0.235
TSLA 0.124 0.690 0.187 0.195
Includes US stocks with market cap > 100 Million, and price above $3. It doesn't include penny stocks.
You can download data from here:
Source (US stocks): https://www.nasdaq.com/market-activity/stocks/screener?exchange=nasdaq&letter=0&render=download\
Implementation: I am using sets for 'x in s' comparison, sets time complexity for "x in s" is O(1) compare to list: O(n).
Limitations: It depends mainly on the defined parameters for current implementation: It completely ignores the heavily downvoted comments, and there can be a time when the most mentioned ticker is heavily downvoted, but you can change that in upvotes variable.