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

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