This model uses stock data and twitter sentiment to generate a prediction of future market trends. It gathers sentiment from scraping the Twitter website. Unfortunately, one cannot use the Twitter API as tweets are only available in a window spanning back two weeks. This tool solves this problem.
General process is as follows:
-
Get stock data from Quandl (day-to-day data)
-
Get stock twitter sentiment for each day
-
Join data
-
Feed it into a Long-term Short-term (LSTM) Neural Network
-
Graph past/future predictions
Version1__base: Contains a basic implementation. No twitter sentiment analysis.
Version2__twitter_sentiment: Contains full functionality. Makes predictions with sentiment analysis included.
got/got3: Get Old Tweets. This repository can be found here.
img: Contains demo graphs.
misc: For implementations I might use later. For brainstorming.
model.py: The Recurrent Neural Network I am using to train and make predictions.
read_tickers.py: Python script to get all of the tickers in NASDAQ.
scratch.py: Playground script.
