Using TensorFlow machine learning and Twitter sentiment analysis to predict stock trends

"stock-predict.py" runs through these steps:

1. Requests an input of a NASDAQ stock quote.
2. Downloads the last year's historical data for that stock using Google's resources.
3. Trains a neural network that the historical data to predict the stock's closing price for tomorrow.
4. Uses Tweepy to find a certain number of tweets about that stock and uses TextBlob to determine if there is a positive or negative trends, using sentiment analysis.

• numpy
• scipy
• pyyaml
• tensorflow
• tweepy
• keras
• requests
• textblob

• Build neural network to classify past year's historical data
• Perform sentiment analysis on collected Tweets
• Create portfolio-builder that allows for multiple stocks to be analyzed
• Build interactive webpage to display data and predictions