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main.py
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main.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer as SIA
import praw
import matplotlib.pyplot as plt
import math
import datetime as dt
import pandas as pd
import numpy as np
# In[2]:
nltk.download('vader_lexicon')
nltk.download('stopwords')
# In[3]:
reddit = praw.Reddit(client_id='*********',
client_secret='******************',
user_agent='*********') ## to use this, make a Reddit app. Client ID is in top left corner, client secret is given, and user agent is the username that the app is under
# In[4]:
sub_reddits = reddit.subreddit('wallstreetbets')
stocks = ["GME", "AMC"]
# For example purposes. To use this as a live trading tool, you'd want to populate this with tickers that have been mentioned on the pertinent community (WSB in our case) in a specified period.
# In[5]:
def commentSentiment(ticker, urlT):
subComments = []
bodyComment = []
try:
check = reddit.submission(url=urlT)
subComments = check.comments
except:
return 0
for comment in subComments:
try:
bodyComment.append(comment.body)
except:
return 0
sia = SIA()
results = []
for line in bodyComment:
scores = sia.polarity_scores(line)
scores['headline'] = line
results.append(scores)
df =pd.DataFrame.from_records(results)
df.head()
df['label'] = 0
try:
df.loc[df['compound'] > 0.1, 'label'] = 1
df.loc[df['compound'] < -0.1, 'label'] = -1
except:
return 0
averageScore = 0
position = 0
while position < len(df.label)-1:
averageScore = averageScore + df.label[position]
position += 1
averageScore = averageScore/len(df.label)
return(averageScore)
# In[6]:
def latestComment(ticker, urlT):
subComments = []
updateDates = []
try:
check = reddit.submission(url=urlT)
subComments = check.comments
except:
return 0
for comment in subComments:
try:
updateDates.append(comment.created_utc)
except:
return 0
updateDates.sort()
return(updateDates[-1])
# In[7]:
def get_date(date):
return dt.datetime.fromtimestamp(date)
# In[8]:
submission_statistics = []
d = {}
for ticker in stocks:
for submission in reddit.subreddit('wallstreetbets').search(ticker, limit=130):
if submission.domain != "self.wallstreetbets":
continue
d = {}
d['ticker'] = ticker
d['num_comments'] = submission.num_comments
d['comment_sentiment_average'] = commentSentiment(ticker, submission.url)
if d['comment_sentiment_average'] == 0.000000:
continue
d['latest_comment_date'] = latestComment(ticker, submission.url)
d['score'] = submission.score
d['upvote_ratio'] = submission.upvote_ratio
d['date'] = submission.created_utc
d['domain'] = submission.domain
d['num_crossposts'] = submission.num_crossposts
d['author'] = submission.author
submission_statistics.append(d)
dfSentimentStocks = pd.DataFrame(submission_statistics)
_timestampcreated = dfSentimentStocks["date"].apply(get_date)
dfSentimentStocks = dfSentimentStocks.assign(timestamp = _timestampcreated)
_timestampcomment = dfSentimentStocks["latest_comment_date"].apply(get_date)
dfSentimentStocks = dfSentimentStocks.assign(commentdate = _timestampcomment)
dfSentimentStocks.sort_values("latest_comment_date", axis = 0, ascending = True,inplace = True, na_position ='last')
dfSentimentStocks
# In[9]:
dfSentimentStocks.author.value_counts()
# In[10]:
dfSentimentStocks.to_csv('Reddit_Sentiment_Equity.csv', index=False)
# In[ ]: