An Efficient Turnkey Agent for Repeated Trading with Overall Budget and Preferences

For various e-commerce applications autonomous agents can do the actual trading on behalf of their users. We consider an agent who trades repeatedly on behalf of his user, given an overall budget and preferences per time step, both specified at the start. For many e-commerce settings such an agent has limited computational resources, limited prior information concerning price fluctuations, and little time for online learning. We therefore develop an efficient heuristic that requires little prior information to work well from the start, even for very roughed nonsmooth problem instances. Extensive computer experiments conducted for a wide variety of customer preferences show virtually no difference in performance between a dynamic programming (DP) approach and the developed heuristic carrying out the agent's task. The DP approach has, however, the important drawback of generally being too computationally intensive.