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Trading agents competing: performance, progress, and market effectiveness

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4 Author(s)
M. P. Wellman ; Artificial Intelligence Lab., Michigan Univ., Ann Arbor, MI, USA ; S. -F. Cheng ; D. M. Reeves ; K. M. Lochner

The annual trading agent competition offers agent designers a forum for evaluating programmed trading techniques in a challenging market scenario. TAC aims to spur research by enabling researchers to compare techniques on a common problem and build on each other's ideas. A fixed set of assumptions and environment settings facilitates communication of methods and results. As a multiyear event, TAC lets researchers observe trading agents' progress over time, in effect accelerating the evolution of an adapted population of traders. Given all the participant effort invested, it is incumbent on us to learn as much from the experience as possible. After three years of TAC, we're ready to examine there we stand. To do this, we used data from actual TAC tournaments and some post-competition experimentation. We based our analysis almost entirely on outcomes (profits and allocations), with very little direct accounting for specific agent techniques.

Published in:

IEEE Intelligent Systems  (Volume:18 ,  Issue: 6 )