Traders' Long-Run Wealth in an Artificial Financial Market
Computational Economics
Evolving robust GP solutions for hedge fund stock selection in emerging markets
Proceedings of the 9th annual conference on Genetic and evolutionary computation
GEVA: grammatical evolution in Java
ACM SIGEVOlution
Natural Computing in Computational Finance: Volume 2
Natural Computing in Computational Finance: Volume 2
IEEE Transactions on Evolutionary Computation
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A computational model of a double auction market is introduced and extended to allow a controlled cyclic behaviour in the price signal to be developed. Traders are evolved to maximise profit in this market using Grammatical Evolution, and their properties studied for a range of periods and amplitude of the trend in the price signal. The trader grammar allows decision making based on simple trading rules incorporating the concepts of moving-average oscillators and trading range break-out. The results of this investigation demonstrate that traders evolve a short waiting period between decisions, and that there underlying decision logic reflects the scale of the market price frequency. Evidence is presented that suggests evolving a robust profit-making trader, for a range of price frequency changes, requires the training data to have high frequency variation. More generally, to evolve robust solutions for any complex GP problem, a set of local models or an ensemble and state-based approach, is implied by the results.