A paper by Wei Wang (Ph.D. candidate) and Professor Nanpeng Yu was selected as one of the Best Conference Papers at the 2019 IEEE Power and Energy Society General Meeting (PESGM) held in Atlanta, Georgia. IEEE PESGM is the flagship conference for the IEEE Power and Energy Society.
The paper entitled “A Machine Learning Framework for Algorithmic Trading with Virtual Bids in Electricity Markets” develops a data-driven algorithmic trading strategy for virtual transactions in electricity markets. In the proposed algorithmic trading strategy, a budget and risk constrained portfolio optimization problem is solved, which selects the virtual transactions to be executed. In order to maximize the expected net earnings of the virtual bid portfolio, a mixture density network model is developed to provide robust and accurate forecasts for electricity price spread between day-ahead and real-time market. By leveraging a coherent risk measure and historical price samples, the risk-constrained portfolio optimization problem is solved efficiently. Backcasting results based on market data from ISO New England show that our proposed mixture density network based trading strategy consistently outperforms the benchmark online learning approach.
Find out more at https://intra.ece.ucr.edu/~nyu/papers/2019-VB-ML-Trading