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The annual Trading Agent Competition for Market Design, cat, provides a testbed to study the mechanisms that modern stock exchanges use in their effort to attract potential traders while maximizing their profit. This paper presents an evaluation of the agents that participated in the 2008 competition. The evaluation is based on the analysis of the cat finals as well as on the results obtained from post-tournament experiments. We present Mertacor, our entrant for 2008, and compare it with the other available agents. In addition, we introduce a simple yet effective way of computing the global competitive equilibrium that Mertacor utilizes and discuss its importance for the game. [ABSTRACT FROM AUTHOR]

For almost five years we continually operated a simulation testbed exploring strategies for the TAC Travel game. Building on techniques developed in our recent study of continuous double auctions, we performed an equilibrium analysis of our testbed data, and employed reinforcement learning in the equilibrium environment to derive a new entertainment strategy for this domain. A second iteration of this process led to further improvements. We thus demonstrate that interleaving empirical game-theoretic analysis with reinforcement learning in an effective method for generating stronger trading strategies in this domain. [ABSTRACT FROM AUTHOR]

Recently IT giants such as Google, Amazon, Microsoft, and IBM are gearing up to be a part of the Cloud and begin to sell their cloud services. However, the current market trading mechanism is inflexible, and the price is not reasonable enough in some situation. Therefore, we first propose a cloud market framework for people to build a uniform and fully competitive cloud market where users can buy resources from different companies and exchange their idle resources in a more flexible way. Then we define a double auction Bayesian Game-based pricing model (DABGPM) for the suggested cloud market and discuss how to develop an optimal pricing strategy for this model. Our work, we think, makes a good example of more flexible and more reasonable cloud resources trading. [ABSTRACT FROM AUTHOR]

This work introduces algorithmic trading on artificial stock markets and describes past and existing approaches. A proposed framework of the artificial stock market approach is presented, together with the used agent types. Then the simulation results΄ analyses are discussed. Conclusions and future work directions are presented, showing where the MACD algorithm and some rules can be used. The human behavior influence over the market is highlighted. [ABSTRACT FROM AUTHOR]