Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
C++ how to program
The nature of statistical learning theory
The nature of statistical learning theory
Software—Practice & Experience
Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
Evolving neural networks for static single-position automated trading
Journal of Artificial Evolution and Applications - Regular issue
Time series forecasting with a non-linear model and the scatter search meta-heuristic
Information Sciences: an International Journal
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
A Genetic Programming Environment for System Modeling
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Modeling the ASE 20 Greek index using artificial neural nerworks combined with genetic algorithms
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
An Investigation into the Use of Intelligent Systems for Currency Trading
Computational Economics
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The purpose of this article is to present a novel genetic programming trading technique in the task of forecasting the next day returns when trading the EUR/USD exchange rate based on the exchange rates of historical data. Aiming at testing its effectiveness, we benchmark the forecasting performance of our genetic programming implementation with three traditional strategies (naive strategy, MACD, and a buy & hold strategy) plus a hybrid evolutionary artificial neural network approach. The proposed genetic programming technique was found to demonstrate the highest trading performance in terms of annualized return and information ratio when compared to all other strategies which have been used. When more elaborate trading techniques, such as leverage, were combined with the examined models, the genetic programming approach still presented the highest trading performance. To the best of our knowledge, this is the first time that genetic programming is applied in the problem of effectively modeling and trading with the EUR/USD exchange rate. Our application now offers practitioners with an effective and extremely promising set of results when forecasting in the foreign exchange market. The developed genetic programming environment is implemented using the C++ programming language and includes a variation of the genetic programming algorithm with tournament selection.