Total Comments: 2,979,131

Total Comments Mentioning Valid Securities: 281,550

First Comment: Wednesday, April 11, 2012 4:46:43 PM

Stock Ticker: TSLA

Total Comments Mentioning Ticker: 12,079

Average Sentiment Towards Ticker: 0.042

Stock Mention Ranking (SMR): 3

Ticker First Mentioned on WSB: 2,105 Days Ago

Note: Trades based on WallStreetBets comments are in BLUE, trades based on holding long-term are in RED

Stock Ticker: FB

Total Comments Mentioning Ticker: 10,827

Average Sentiment Towards Ticker: 0.0459

Stock Mention Ranking (SMR): 7

Ticker First Mentioned on WSB: 2,468 Days Ago

Note: Trades based on WallStreetBets comments are in BLUE, trades based on holding long-term are in RED

WallStreetBets is a discussion forum about day trading, stocks, options, futures, and anything market related, so it would be innacurate to assume that any comment containing a stock ticker indicated a long position on the security.

From my understanding this is an NLP problem that's relatively difficult to solve. A slighly more accurate way of extracting the type of position from a comment would be to assume a long position unless the word "short" is present in the comment.

Unfortunately, this strategy would fail in comments discussing options, and in our model the purchase of a put option would imply the same sentiment as a short position.

Lastly, the discussion of multiple securities in a single comment can cause confusion as to the implied position relative to each stock ticker.

Rather than using NLTK or RAKE, I created an algorithm present in main.extract_buy_or_sell() that attempts to extract the indicated position towards each stock ticker in a comment. Here is the algorithm in Psuedocode:

comment_info = {'puts': [], 'calls': [], 'buy': [], 'sell': []}
for sentence in comment:
    while sentence:
        word = sentence.pop()
        if word == 'buy' or 'buying':
            tempList = []
            while sentence:
                newWord = sentence.pop()
                if newWord is StockTicker:
                    tempList.append(newWord)
                elif newWord == 'puts' and len(tempList) > 0:
                    comment_info['puts'] += tempList
                    tempList.clear()
                    break
                elif newWord == 'calls' and len(tempList) > 0:
                    comment_info['calls'] += tempList
                    tempList.clear()
                    break
            comment_info['buy'] += tempList
        elif word == 'short' or 'shorting':
            while sentence:
                newWord = sentence.pop()
                if newWord is StockTicker:
                    comment_info['sell'] += newWord
                else:
                    break
        elif word == 'sell' or 'sold' or 'close' or 'closing' or 'shorts':
            tempList = []
            while sentence:
                newWord = sentence.pop()
                if newWord is StockTicker:
                    tempList.append(newWord)
               elif newWord == 'puts' and len(tempList) > 0:
                    comment_info['puts'] += tempList
                    tempList.clear()
                    break
                elif newWord == 'calls' and len(tempList) > 0:
                    comment_info['calls'] += tempList
                    tempList.clear()
                    break
                elif newWord == 'shorts' and len(tempList) > 0:
                    comment_info['buy'] += tempList
                    tempList.clear()
                    break
            comment_info['sell'] += tempList
        elif word is StockTicker:
            tempList = [word]
            while sentence:
                newWord = sentence.pop()
                if newWord is StockTicker:
                    tempList.append(newWord)
                elif newWord == 'puts' and len(tempList) > 0:
                    comment_info['puts'] += tempList
                    tempList.clear()
                    break
                elif newWord == 'calls' and len(tempList) > 0:
                    comment_info['calls'] += tempList
                    tempList.clear()
                    break
            comment_info['buy'] += tempList

"Short GPRO"

{"sell": ["GPRO"], "buy": [], "calls": [], "puts": []}

"HIMX, EOG, WPRT, VALE. Some high div paying stocks NLY, PMT, HTS."

{"sell": [], "buy": ["HIMX", "WPRT"], "calls": [], "puts": []}

"AAMRQ and ONCS did great for me (and EPZM yesterday)"

{"sell": [], "buy": ["ONCS", "EPZM"], "calls": [], "puts": []}

"holding ZGNX now. Thanks man!"

{"sell": [], "buy": ["ZGNX"], "calls": [], "puts": []}

"I threw a couple of hundreds on VVUS earnings. It both beat and is up! Yay!"

{"sell": [], "buy": ["VVUS"], "calls": [], "puts": []}

"No. OP is hyping his GPRO bet."

{"sell": [], "buy": ["GPRO"], "calls": [], "puts": []}

"I closed my SRPT position today as well. 258% gain."

{"sell": ["SRPT"], "buy": [], "calls": [], "puts": []}

"I think SFM has much better growth opportunty"

{"sell": [], "buy": ["SFM"], "calls": [], "puts": []}

"Though he's doing great in AAPL."

{"sell": [], "buy": ["AAPL"], "calls": [], "puts": []}

"I have some money to spare after my 300% gain on TSLA puts."

{"sell": [], "buy": [], "calls": [], "puts": ["TSLA"]}

"Disclaimer, I am short Aug 100 covered calls. I am also long AAPL (though that's implied)."

{"sell": [], "buy": ["AAPL"], "calls": [], "puts": []}

"looks like CSIQ is about to turn around tmr...."

{"sell": [], "buy": ["CSIQ"], "calls": [], "puts": []}

"GPRO October $60 Puts"

{"sell": [], "buy": [], "calls": [], "puts": ["GPRO"]}

Prior to December 29th, sentiment analysis was done on comments without taking the sentiment of the ticker itself into consideration. This overlook returned biased results in favor of companies with ticker names that doubled as valid words in the english dictionary.

To fix this overlook, I modified the string prior to calculating sentiment so that all tickers are replaced with "TSLA" (a sentiment neutral ticker).

An example of the bias caused by this overlook can be seen in the original Best Securities by Sentiment Polarity table below (initially published in commit 89ddf9d).