Statistical arbitrage trading across electricity markets using advantage actor–critic methods

In this paper, risk-constrained arbitrage trading strategies that exploit price differences arising across short-term electricity markets, namely day-ahead (DAM), continuous intraday (CID) and balancing (BAL) markets, are developed and evaluated. To open initial DAM positions, a rule-based trading policy using DAM and CID price forecasts is proposed. DAM prices are predicted using both technical indicator features and data augmentation methods, such as autoencoders and generative adversarial networks. Meanwhile, CID prices are predicted using novel features that are engineered from the limit order book. Using the forecasts, the direction of price movements is correctly predicted the majority of the time. To manage open DAM positions while optimising the risk-reward ratio, deep reinforcement learning agents trained using the advantage actor–critic algorithm (A2C) are employed. Evaluated across Dutch short-term markets, A2C yields profits surpassing those obtained using A3C and other benchmarks. We expect our study to benefit electricity traders and researchers who seek to develop state-of-art intelligent trading strategies. •Automated trading strategies for short-term electricity markets are developed.•Technical indicators and data augmentation methods are together used in price forecasting.•A rule-based trading agent is developed using price forecasts for the day-ahead market.•An agent capable of trading electricity on the intraday market is trained using A2C.•A2C is shown to outperform benchmarks, such as A3C; generating higher revenues.