CNN-LSTM_Limit_Order_Book

This notebook contains an independently developed Keras/Tensorflow implementation of the CNN-LSTM model for Limit Order Book forecasting originally proposed by Zhang et al. (https://arxiv.org/pdf/1808.03668.pdf). The current implementation was adopted in the paper written by Briola et al.(https://arxiv.org/pdf/2007.07319.pdf).

The interested reder can find the original implementation of the model at https://github.com/zcakhaa/DeepLOB-Deep-Convolutional-Neural-Networks-for-Limit-Order-Books/blob/master/jupyter/run_train_represent.ipynb.

If you use the proposed Keras/Tensorflow implementation in your work please cite:

@article{briola2020deep,
  title={Deep Learning modeling of Limit Order Book: a comparative perspective},
  author={Briola, Antonio and Turiel, Jeremy and Aste, Tomaso},
  journal={arXiv preprint arXiv:2007.07319},
  year={2020}
}

and

@misc{briola_antonio_and_turiel_jeremy_david_2020_4068530,
  author       = {Briola, Antonio and Turiel, Jeremy David},
  title        = {CNN-LSTM\_Limit\_Order\_Book\_Tensorflow},
  month        = oct,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v1.1},
  doi          = {10.5281/zenodo.4068530},
  url          = {https://doi.org/10.5281/zenodo.4068530}
}

Please always remember to cite the original papers this work is based on:

@article{zhang2019deeplob,
  title={Deeplob: Deep convolutional neural networks for limit order books},
  author={Zhang, Zihao and Zohren, Stefan and Roberts, Stephen},
  journal={IEEE Transactions on Signal Processing},
  volume={67},
  number={11},
  pages={3001--3012},
  year={2019},
  publisher={IEEE}
}