HKBU ITF Project:
Deep Learning-Based Trading System for Energy-Relevant Financial Products
The trading system of energy-relevant financial products is highly dependent on data selection and integration, trading signal detection, and trading prediction. As with energy-relevant financial investments, it is important for financial companies to understand financial trading behavior, and the relationship between financial products and the other financial data. The nature and structure of energy-relevant financial trading behavior and their relationships are especially difficult and complicated to determine because of numerous financial trading functions operating in global energy market.
In this project, we will develop a deep learning-based technology to trade energy-relevant financial products in the market. Three key technologies will be developed and implemented: (a) deep learning-based financial data integration and selection, (b) deep learning-based financial signal detection and prediction, and (c) deep learning optimization.
Development and deployment of the trading system will benefit the HKSAR financial industry by enabling them to compete with the others in the global financial market, to enhance decision support for investment and management, and to train financial professionals to use advanced mathematics, IT and financial engineering technologies.
Prof. Michael Kowk-po NG
Chair Professor in Mathematics, Department of Mathematics and Chair Professor (Affiliate) of Department of Computer Science, Hong Kong Baptist University