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}
}