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Development of Trading Systems using Genetic Programming with a Case Study

Bag om Development of Trading Systems using Genetic Programming with a Case Study

Diploma Thesis from the year 2007 in the subject Computer Science - Programming, grade: 1.7, University of Hamburg, language: English, abstract: In this thesis Genetic Progrmming is used to create trading systems for the EUR/USD foreign exchange market using intraday data. In addition to the exchange rates several moving averages are used as inputs. The developed evolutionary algorithm extends the framework ECJ. The created trading systems are being evaluated by a fitness function that consists of a trading simulation. Genetic operators have been adapted to support "node weights". By using these on the one hand macromutaion is tried to be reduced on the other hand the interpretability of the created trading systems is tried to be improved. Results of experiments show that created trading systems are apparently successfull in profitably using informations contained within the exchange rates. Profits of the created trading systems are maximized by using the optimal position size. It is shown that if the minimum investment period is met the achieved results are optimal even when taking into account the used risk adjusted performance figure.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783869432038
  • Indbinding:
  • Paperback
  • Sideantal:
  • 104
  • Udgivet:
  • 19. marts 2012
  • Størrelse:
  • 148x210x6 mm.
  • Vægt:
  • 145 g.
  • 8-11 hverdage.
  • 10. december 2024
Forlænget returret til d. 31. januar 2025

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Beskrivelse af Development of Trading Systems using Genetic Programming with a Case Study

Diploma Thesis from the year 2007 in the subject Computer Science - Programming, grade: 1.7, University of Hamburg, language: English, abstract: In this thesis Genetic Progrmming is used to create trading systems for the EUR/USD foreign exchange market using intraday data. In addition to the exchange rates several moving averages are used as inputs.
The developed evolutionary algorithm extends the framework ECJ. The created trading systems are being evaluated by a fitness function that consists of a trading simulation. Genetic operators have been adapted to support "node weights". By using these on the one hand macromutaion is tried to be reduced on the other hand the interpretability of the created trading systems is tried to be improved.
Results of experiments show that created trading systems are apparently successfull in profitably using informations contained within the exchange rates. Profits of the created trading systems are maximized by using the optimal position size. It is shown that if the minimum investment period is met the achieved results are optimal even when taking into account the used risk adjusted performance figure.

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