In this project, I build a LSTM-RNN to predict stock prices using keras with tensorflow. The training data comes from historical closing precies of S&P500. The accuracy measured by Root Mean Square Error (RMSE) is around 0.99. And I did experiments on the network's hyper-parameters such as LSTM cell hidden state size, truncated back propagation length and depth of the network. At last, I build a website using this prediction model as engine with Flask and python.
python 2.7 pip 9.0.1 flask 0.12.1
tensorflow 0.12.1 keras1.2.1
Note: Please activate tensorflow virtual env first.
To runthe experiment on LSTM structures:
cd ./model
python experiment.py
To run the web app, first change back to the root
python app.py