Genetic optimization of adaptive trading agents for double-auction markets

The continuous double-auction (CDA) is a powerful market mechanism, noted for its speed and efficiency, and is the mechanism underlying the organization of open-outcry trading pits at major international derivatives markets. Cliff and Bruten (1997) demonstrated that software trading agents need more than zero intelligence to give human-like price-equilibration behavior and presented results from experiments with simple adaptive trading agents in CDA markets. These agents give very good performance on standard measures of trading activity such as allocative efficiency, Smith´s (1962) α measure, and profit dispersion, but only when parameters governing the adaptation mechanism are set to appropriate values. Determining good or optimal combinations of parameters by hand is possible, but can be labor-intensive. The paper presents the first results from using a genetic algorithm to optimize key parameters governing adaptation in the trading agents. It is shown that a simple genetic algorithm, in combination with an appropriate evaluation function, can rapidly deliver good parameter settings from random initial-value conditions