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Options Flow: July 2017

A recent project of mine is tracking the tweets of 12 Twitter users who tweet heavily for stock options. I will increase sample size as I learn more about Python/SQL mining from Twitter.

A lot needs to be tweaked as curation of word choices to determine “bullish,” “mildly bullish, “neutral,” “mildly bearish,” and “bearish” sentiment is difficult from an Excel standpoint. Manual curation from self is necessary as of now but I will try to automate as much as possible. Also, some Twitterers seem to typo often and use buzz words that aren’t common like “bot,” “blood sweeper,” etc.

Here’s a legend for the ratings I give:

Below are the tables for data mined since 7/1/2017 (I have data dating further back, but more sorting/curation is needed).

We can see that “Financial”-related tweets have the highest count, but of the 283, most are ETFs (195), so true “Financial”-related stocks are only 88. More than half the tweets have a “Bullish” sentiment (53% = 2, 6% = 1).

Tech-related tweets pose the highest count, with “Internet Information Providers” being the top tweeted from a “Bullish” standpoint. Top names in that...


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