Pretests for Genetic-Programming Evolved Trading Programs: “zero-intelligence” Strategies and Lottery Trading
Over the last decade, numerous papers have investigated the use of GP for creating financial trading strategies. Typically in the literature results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a series of pretests, based on several variants of random search, aiming at giving more clear-cut answers on whether a GP scheme, or any other machine-learning technique, can be effective with the training data at hand. The analysis is illustrated with GP-evolved strategies for three stock exchanges exhibiting different trends.
KeywordsGenetic Programming Trading Strategy Random Search Stock Index Training Interval
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- 5.Chen, S.-H., Kuo, T.-W., Hoi, K.-M.: Genetic Programming and Financial Trading: How Much about ”What we Know”. In: Handbook of Financial Engineering, Kluwer Academic Publishers (forthcoming, 2006)Google Scholar