In this paper we present TRADES, a data-driven agent-based simulator for barter trade exchanges. We provide an overview of the barter trade exchange industry, focusing on the operational aspects of trade exchanges and motivating the design of our simulator. Our simulator is built by learning probabilistic models of company purchase behavior using transaction history data from an operating trade exchange. We quantitatively evaluate the accuracy of our simulator by comparing simulated trade to the transaction data, showing a high degree of agreement between the two. We also demonstrate use of the simulator to evaluate the effectiveness of a particular trade brokering strategy.