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To develop an automated framework for trading strategy design, by employing evolutionary computation in conjunction with other machine learning paradigms
The present framework utilize genetic programming
Much of the existing financial forecasting using GP has focused on high-frequency FX [Jonsson, 1997][Dempster and Jones, 2001][Bhattacharyya et al, 2002] and the general consencus is that there is predictability, and excess return is achievable in the pressence of transaction costs
For stocks, the results are mixed [Allen and Karjalainen, 1999] do not significantly out-perform the buy-and-hold on S&P500 daily data, but [Becker and Sheshadri, 2003] do on monthly.
EC is a concept inspired by the Darwinian survival of the fittest principle – The rationale being, that natural evolution has proved succesfull in solving a wide range of problems throughout time, hence an algorithm that mimics this behavior, might solve a wide range of artificial problems
The concept was pioneered by Holland (1975) in the form of Genetic Algorithms (GA)
A GA is essentially a population based search method, where each candidate solution is incoded in a fixed length binary string.
The population evolves, via mainly three operators, selection, reproduction and mutation.
The selection process is based on the survival of the fittest principle.
GP’s are basically GA’s in which the genome contitutes hierachical computer programs
Using this representation, we can solve problems in a wide range of fields such as, symbolic or ordinary regression, classification, optimal control theory etc. since each of these areas “can be viewed as requiring discovery of a computer program that produces some desired output for particular inputs” (Koza, 1992)
Tree representation of programs, function & terminal Set