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The focus of this work is the computation of efficient strategies for commodity trading in a multimarket environment. In today's "global economy" commodities are often bought in one location and then sold (right away, or after some storage period) in different markets. Thus, a trading decision in one location must be based on expectations about future price curves in all other relevant markets, and on current and future storage and transportation costs. Investors try to compute a strategy that maximizes expected return, usually with some limitations on assumed risk. With standard stochastic assumptions on commodity price fluctuations, computing an optimal strategy can be modeled as a Markov decision process (MDP). However, in general, such a formulation does not lead to efficient algorithms. In this work a model for representing the multimarket trading problem is proposed and how to obtain efficient structured algorithms for computing optimal strategies is shown for a number of commonly used trading objective functions (expected net present value, mean-variance, and value at risk).