  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.
