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Abstract-The article presents the performance analysis issues of buy-sell decisions agents' in a-Trader system. The system allows for supporting of investment decision on FOREX market. The first part of article contains a description of a aTrader system. Next, the algorithms of the selected buy-sell decision agents is presented. In the last part of article the evaluation function of agents' performance is detailed, and the approach to performance analysis is proposed and illustrated.

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Abstract-The paper presents issues related to developing methods for fundamental analysis used to expand capabilities of multi-agent trading system, to better predict the financial market. The fundamental analysis indicators can be used as confirmation of decisions generated by other strategies of the system. The first part of the article discusses briefly the fundamental analysis issues in relation to the online trading on FOREX market. The statistical analysis of correlations of the different time series indicators and algorithms of fundamental analysis agents are examined. The final part discusses the results of the performance evaluation of selected investment strategies, including fundamental-based agents. I. INTRODUCTIONn trading support systems, the advices might be computed by one or many algorithms, or by one or many software agents using one or many information sources. An overview of of the agents operating on financial markets has already been given by B. LeBaron [1]. Currently, most trading systems are based on one or only a few algorithms. For example, the solutions described by L. Mendes, P. Godinho and J. Dias [2] use genetic algorithms to perform analysis on the basis of historical quotations. The system described by J.R. Thompson, J.R. Wilson and E. P. Fitts [3] is based on the multifractal time series. In the system described in [4,5] technical analysis indicators are used. There are many solutions based on multi-agent approach. H. C. Aladag, U. Yolco and E. Egrioglu [6] present an evaluation of the portfolio optimisation strategies by three agents: rational agent, interference agent, and technical analysis agent. P. Singh and B. Borah [7] apply a multi-agent system where the agents' intelligence is based on fuzzy expert system. O. Badawy and A. Almotwaly [8] developed the neural networks and neuro-fuzzy computing for taking into account the geometrical patterns of the financial data. M. Aloud, E.P.K. Tsang and R. Olsen [9] introduce an agent-based model for simple prediction of financial markets, where each agent predicts the development of selected subsets of the assets pairs in real time by separately examining the similarities between ask and bid assets histories. P. Kaltwasser [10] describes an agent that uses multiple behavioral techniques to make bidding decisions in the face of market uncertainty. J. Glattfelder, A. Dupuis and R. Olsen [11] develop a system that supports multiple strategies, but they use only movingaverage crossover strategies in the current stage of development. The paper by R. Barbosa and O. Belo [12] describes a multi-agent system that consists of a set of trading models such as an ensemble of classifiers, regression models, case-based reasoning, and an expert system. F. H. Westerhoff [13] presents a system where two groups of agents applying the methods of fundamental and technical analysis try to shape market dynamics.Summing up, more often the trading advice is provide by multiple software agents that use mainly technical analysis. However, many papers relate...

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