deep-learning-for-order-book-price-and-movement-predictions

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deep-learning-for-order-book-price-and-movement-predictions

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Limit Order Book data analysis and modeling using LSTM network

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.gitignore
Binance_market_data.ipynb
data_to_CSV.ipynb
Deep Learning.ipynb
LOB to training data_1.ipynb
LOB to training data_2.ipynb
README.md
SHAP values .ipynb
Volatility of log returns.ipynb

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# Limit order book modelling with Deep Learning (LSTM network) for price and market movement predictions

Repo contains files and data for:

* Cleaning limit order book data scraped from Binance
* Exploratory Data Analysis on ETHBTC trades and orders
* Price volatility calculation
* Feature engineering the order book and trades data for Deep Learning
* Modelling the order book using Long Short Term Memory (LSTM) network for market movement and price predictions
* Shap values of the created models

## Acknowledgments:

Parts of this project uses researches from:

D. T. Tran, M. Magris, J. Kanniainen, M. Gabbouj, A. Iosifidis, "Tensor Representation in High-Frequency Financial Data for Price Change Prediction" 2017. [https://arxiv.org/pdf/1709.01268.pdf](https://arxiv.org/pdf/1709.01268.pdf)

J. Sirignano, R. Cont, "Universal features of price formation in financial markets: perspectives from Deep Learning" 2018. [https://arxiv.org/pdf/1803.06917.pdf](https://arxiv.org/pdf/1803.06917.pdf)

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