marsjane/deepobs

The Short-Term Predictability of Returns in Order Book Markets: A Deep Learning Perspective.

deepOBs

This project aims to explore and analyze order book-driven deep learning models for predicting high-frequency returns in financial exchanges with a limit order book market structure.

The main methods, which we believe could be of use to other researchers, are found in:

• data_process.py: functions for processing order book data dowloaded from LOBSTER to raw order book, order flow and volume features and the corresponding returns;
• data_methods.py: auxiliary functions for processed data;
• custom_datasets.py: create custom tf.dataset objects to load features and responses into models;
• model.py: a class to build, train and evaluate deepLOB (Zhang et al., 2019), deepOF (Kolm et al., 2021) and deepVOL as keras.models.Model objects;
• MCS_results.py: functions to perform the bootstrap Model Confidence Set (Hansen et al., 2011) procedure on results

Release v1.0

This release contains code and results for the paper 'The Short-Term Predictability of Returns in Order Book Markets: A Deep Learning Perspective'.