Efficient markets are characterised by profit-driven participants continuously refining their positions towards the latest insights. Margins for profit generation are generally small, shaping a difficult landscape for automated trading strategies. This paper introduces a novel, fully-automated cross-border intraday (XBID) trading strategy tailored for single-price imbalance energy markets. This strategy relies on a strategically devised mixture model to predict future system imbalance prices, which, upon benchmarking against several state-of-the-art models, outperforms its counterparts across every metric. However, these models were fit to a finite amount of training data typically causing them to perform worse on unseen data when compared to their training set. To address this issue, a coherent risk measure is added to the cost function to take additional uncertainties in the prediction model into account. This paper introduces a methodology to select the tuning parameter of this risk measure adaptively by continuously quantifying the model accuracy on a window of recently observed data. The performance of this strategy is validated with a simulation on the Belgian energy market using real-time market data. The adaptive tuning approach enables the strategy to achieve higher absolute profits with a reduced number of trades.

翻译：高效市场以利润驱动型参与者根据最新信息不断调整其头寸为特征。利润空间通常较小，这为自动化交易策略营造了艰难的环境。本文提出了一种全新的全自动跨境日内（XBID）交易策略，专为单一价格不平衡电力市场设计。该策略依赖于一种精心设计的混合模型来预测未来系统不平衡价格，与多个最先进模型进行基准测试后，该模型在所有指标上均优于其他模型。然而，这些模型基于有限训练数据拟合，导致其在未见数据上的表现通常劣于训练集。为解决此问题，我们在成本函数中引入了一致性风险度量，以考虑预测模型中的额外不确定性。本文提出了一种通过持续量化最近观测数据窗口上的模型精度来自适应选择该风险度量调优参数的方法。利用比利时电力市场的实时市场数据进行的仿真验证了该策略的有效性。自适应调优方法使策略能够在减少交易次数的同时实现更高的绝对利润。