This is an example on how to use RNNs to predict stock market price.
I've used LSTM as a type of RNN.
This LSTM cell image is from Colah's blog.
The dataset used in this project is Tesla stocks history (From August 2014 - August 2017). I have downloaded this file from Google stocks, but you have csv file inside the project folder. Name of the file is tesla_stocks.csv.
- Python version used in this project: 3.5+
- Pandas 0.18.0
- Numpy 1.10.4
- Matplotlib 1.5.1
- Scikit-learn 0.17.1
- TensorFlow 1.2.0
This project has 2 different implementations.
-
Implementation by using Tensorflow built-in RNN functions. This is a version which you would use in an industry. This implementation can be found inside tensorflow_lstm.ipynb.
-
Implementation number 2 has been done without using any high level functions from TensorFlow. This implementation is good for understanding how RNNs are working. This implementations is in file lstm_from_scratch_tensorflow.ipynb.
To run this project you will need some software, like Anaconda, which provides support for running .ipynb files (Jupyter Notebook).
After making sure you have that, you can run from a terminal or cmd next lines:
For the 1st version of the code:
ipython notebook tensorflow_lstm.ipynb
or
jupyter notebook tensorflow_lstm.ipynb
For the 2nd version of the code:
ipython notebook lstm_from_scratch_tensorflow.ipynb
or
jupyter notebook lstm_from_scratch_tensorflow.ipynb
IT License
Copyright (c) 2017 Luka Anicin
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