In this project, our goal is to use previous period's (day t-k to day t) news sentiment and stock performance of five companies (AAPL, GOOG, AMZN, FB and MSFT) to predict their next day's (day t+1) stock performance. We first used our LSTM model to replicate a previous study using its data. The paper is Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis by Yifan Liu, Zengchang Qin, Pengyu Li, Tao Wan. Then in our second step, we collected price information and news from 11-11-2012 to 11-11-2017. We used the tools listed in the SentimentAnalysis folder to extract the sentiment information, then run the scripts to obtain the performance of our model.

Environment:

• Python 2.7
• Ubuntu 16.04

Packages:

sudo apt-get install python-pip python-dev
sudo pip install Scrapy
sudo pip install tensorflow
sudo pip install numpy
sudo apt-get install libatlas-base-dev gfortran
sudo pip install scipy
sudo pip install -U scikit-learn
sudo pip install matplotlib
sudo pip install keras
sudo pip install pandas

To run sentiment analysis, a rosette key is required. You may acquire a key from https://www.rosette.com/

python stock_pred.py <STOCK_PRICE_FILE> <SHEET_NAME>

• <STOCK_PRICE_FILE> is ./us_stock_data/stock_price.xlsx or ./chinese_stock_data/stock_price.xlsx
• <SHHET_NAME> is the stock you are interested in

python stock_pred_with_sent.py <STOCK_PRICE_WITH_SENT_FILE> <SHEET_NAME>

• <STOCK_PRICE_WITH_SENT_FILE> is ./us_stock_data/stock_price_with_sentiment.xlsx or ./chinese_stock_data/stock_price_with_sentiment.xlsx
• <SHHET_NAME> is the stock you are interested in

Contain results obtained on Chinese stocks and on US stocks

Contain a web crawler that extract url from Reuters.com and sample output

Contain tools for conducting sentiment analysis. Detailed description is included in the folder.

Contain raw data of Chinese stocks. Collect from previous research Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis by Yifan Liu, Zengchang Qin, Pengyu Li, Tao Wan.

Contain raw data of US stocks.