Data-driven storage operations: Cross-commodity backtest and structured policies

Storage assets are critical for physical trading of commodities under volatile prices. State-of-the-art methods for managing storage facilities such as the reoptimization heuristic (RH), which are part of commercial software, approximate a Markov Decision Process (MDP) assuming full information regarding the state and the stochastic commodity price process and hence suffer from informational inconsistencies with observed price data and structural inconsistencies with the true optimal policy, which are both components of generalization error. Focusing on spot trades, we find via an extensive backtest that this error can lead to significantly suboptimal RH policies. We develop a forward-looking data-driven approach (DDA) to learn policies and reduce generalization error. This approach extends standard (backward-looking) DDA in two ways: (i) It represents historical and estimated future profits as functions of features in the training objective, which typically includes only past profits; and (ii) it enforces structural properties of the optimal policy. To elaborate, DDA trains parameters of bang-bang and base-stock policies, respectively, using linear- and mixed-integer programs, thereby extending known DDAs that parameterize decisions as functions of features without policy structure. We backtest the performance of RH and DDA on six major commodities, employing feature selection across data from Reuters, Bloomberg, and other public data sets. DDA can improve RH on real data, with policy structure needed to realize this improvement. Our research advances the state-of-the-art for storage operations and can be extended beyond spot trading to handle generalization error when also including forward trades.

Automated summary

This study proposes a forward-looking data-driven approach (DDA) for learning trading strategies in storage assets. By incorporating historical data and predicting future profits as feature functions, and enforcing the structural properties of the optimal policy, DDA improves the performance and reduces the generalization error of traditional reoptimization heuristics.