Design, testing, and optimization of trading systems
Design, testing, and optimization of trading systems
Genetic programming using a minimum description length principle
Advances in genetic programming
Machine Learning
Evolving Market Index Trading Rules Using Grammatical Evolution
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Stateful program representations for evolving technical trading rules
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Maximum margin decision surfaces for increased generalisation in evolutionary decision tree learning
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Using hyperheuristics under a GP framework for financial forecasting
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Controlling overfitting in symbolic regression based on a bias/variance error decomposition
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Adaptive distance metrics for nearest neighbour classification based on genetic programming
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
On the investigation of hyper-heuristics on a financial forecasting problem
Annals of Mathematics and Artificial Intelligence
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In this paper we investigate the profitability of evolved technical trading rules when controlling for data-mining bias. For the first time in the evolutionary computation literature, a comprehensive test for a rule's statistical significance using Hansen's Superior Predictive Ability is explicitly taken into account in the fitness function, and multi-objective evolutionary optimisation is employed to drive the search towards individual rules with better generalisation abilities. Empirical results on a spot foreign-exchange market index suggest that increased out-of-sample performance can be obtained after accounting for data-mining bias effects in a multi-objective fitness function, as compared to a single-criterion fitness measure that considers solely the average return.