Strategy Optimization by Means of Evolutionary Algorithms With Multiple Closing Criteria for Energy Trading

Abstract

The energy markets are experiencing an enhanced volatility and unpredictability due to the growing integration of renewable energy sources in the grid and to the unstable geopolitical situation that is developing worldwide. Energy traders are therefore raising concerns on how to achieve solutions that not only ensure stability in terms of energy needs, both on the supply and demand side, but also enable profits within these markets. To cope with the complexity of this emerging scenario, tools that support traders in their decisions, such as algorithmic trading strategies, are attracting always more and more attention. In particular, evolutionary algorithms have emerged as an effective tool for developing robust and innovative trading strategies. Indeed, their flexibility and adaptability allow for the inclusion of various performance metrics. This article employs a recently issued evolutionary algorithm, called social network optimization, to identify the optimal closing criteria of already opened positions in an energy commodity market. More specifically, the proposed trading strategy is based on five self-defined parameters, which determine a profitable solution over nearly six years of available data. In particular, the overall average positive return achieved and the maximum monthly yield of 1.9% highlight the adaptability and robustness of the developed algorithmic trading strategy. Therefore, the results suggest the potentialities of developing and upgrading novel trading strategies by exploiting evolutionary computation techniques in the actual complex energy markets.

Keywords

• Commodity trading
• energy markets
• evolutionary algorithms (EAs)
• social network optimization (SNO)
• trading strategies