Foundations in Grammatical Evolution for Dynamic Environments
Foundations in Grammatical Evolution for Dynamic Environments
Symbiosis, complexification and simplicity under GP
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Linear Genetic Programming
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces
Genetic Programming and Evolvable Machines
Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model
IEEE Transactions on Evolutionary Computation
A GA combining technical and fundamental analysis for trading the stock market
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Evolutionary data selection for enhancing models of intraday forex time series
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
GP under streaming data constraints: a case for pareto archiving?
Proceedings of the 14th annual conference on Genetic and evolutionary computation
On the impact of streaming interface heuristics on GP trading agents: an FX benchmarking study
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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This research investigates the ability of genetic programming (GP) to build profitable trading strategies for the Foreign Exchange Market (FX) of three major currency pairs (EURUSD, USDCHF and EURCHF) using one hour prices from 2008 to 2011. We recognize that such environments are likely to be non-stationary. Thus, we do not require a single training partition to capture all likely future behaviours. We address this by detecting poor trading behaviours and use this to trigger retraining. In addition the task of evolving good technical indicators (TI) and the rules for deploying trading actions is explicitly separated. Thus, separate GP populations are used to coevolve TI and trading behaviours under a mutualistic symbiotic association. The results of 100 simulations demonstrate that an adaptive retraining algorithm significantly outperforms a single-strategy approach (population evolved once) and generates profitable solutions with a high probability.