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
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