Stock Prediction Using RNN


High Frequency Trading Price Prediction using LSTM Recursive Neural Networks

In this project we try to use recurrent neural network with long short term memory to predict prices in high frequency stock exchange. This program implements such a solution on data from NYSE OpenBook history which allows to recreate the limit order book for any given time. Everything is described in our paper: project.pdf

Program is written in Python 2.7 with usage of library Keras – installation instruction To install it one may need Theano installed as well as numpy, scipy, pyyaml, HDF5, h5py, cuDNN (not all are actually needed). It is useful to install also OpenBlas.

Source of Project

Author: Karol Dzitkowski



  • Python 3.5
  • Numpy
  • keras
  • tensorflow

MIT License (MIT)


How to run

  1. cd StockPredictionRNN
  2. cd src/nyse-rnn
  3. mkdir symbols
  4. python
  5. python




Let's Expert Up