get_news_sentiment_positions.py

def sentimentPositions(val, thresh=0.1, short=True):
    """
    Returns position as 1, -1, or 0 for Buy, Sell, 
    and Do Nothing respectively based on the given 
    sentiment value and threshold.
    """
    if val > thresh:
        return 1
    elif val < -thresh and short:
        return -1
    else:
        return 0
      

# Getting sentiment values for the news headlines/titles
news['sentiment'] = news['title'].apply(
    lambda x: TextBlob(x.lower()).sentiment[0]
)

# Grouping together dates and aggregating sentiment scores from the same day
sent_df = news.groupby('date')[['sentiment']].median()

# Applying the position function
sent_df['sentiment_positions'] = sent_df['sentiment'].apply(
    lambda x: sentimentPositions(x, thresh=0.1, short=False)
)

# Filling in missing days with the most recent position value
date_index = [str(i)[:10] for i in pd.date_range(sent_df.index[0], sent_df.index[-1])]

sent_df = sent_df.reindex(date_index).fillna(method='ffill')