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arXiv · 0705.1757

Scalability and Optimisation of a Committee of Agents Using Genetic Algorithm

T. Marwala·P. De Wilde·L. Correia·P. Mariano·R. Ribeiro·V. Abramov·N. Szirbik·J. Goossenaerts
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Abstract

A population of committees of agents that learn by using neural networks is implemented to simulate the stock market. Each committee of agents, which is regarded as a player in a game, is optimised by continually adapting the architecture of the agents using genetic algorithms. The committees of agents buy and sell stocks by following this procedure: (1) obtain the current price of stocks; (2) predict the future price of stocks; (3) and for a given price trade until all the players are mutually satisfied. The trading of stocks is conducted by following these rules: (1) if a player expects an increase in price then it tries to buy the stock; (2) else if it expects a drop in the price, it sells the stock; (3)and the order in which a player participates in the game is random. The proposed procedure is implemented to simulate trading of three stocks, namely, the Dow Jones, the Nasdaq and the S&P 500. A linear relationship between the number of players and agents versus the computational time to run the complete simulation is observed. It is also found that no player has a monopolistic advantage.

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T. Marwala, P. De Wilde, L. Correia, P. Mariano, R. Ribeiro, V. Abramov, N. Szirbik, J. Goossenaerts. 2007-05-12. Scalability and Optimisation of a Committee of Agents Using Genetic Algorithm. https://arxiv.org/abs/0705.1757

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