An artificial neural network can be used to solve various statistical problems by approximating a function that provides a mapping from input to output data. No universal method exists for architecting an optimal neur...
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An artificial neural network can be used to solve various statistical problems by approximating a function that provides a mapping from input to output data. No universal method exists for architecting an optimal neural network. Training one with a low error rate is often a manual process requiring the programmer to have specialized knowledge of the domain for the problem at hand. A distributed architecture is proposed and implemented for generating a neural network capable of solving a particular problem without specialized knowledge of the problem domain. The only knowledge the application needs is a training set that the network will be trained with. The application uses a master-slave architecture to generate and select a neural network capable of solving a given problem.
This thesis examines the application of genetic algorithms to the optimization of a composite set of technical indicator filters to confirm or reject buy signals in stock trading, based on probabilistic values derived...
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This thesis examines the application of genetic algorithms to the optimization of a composite set of technical indicator filters to confirm or reject buy signals in stock trading, based on probabilistic values derived from historical data. The simplicity of the design, which gives each filter within the composite filter the ability to act independently of the other filters, is outlined, and the cumulative indirect effect each filter has on all the others is discussed. This system is contrasted with the complexity of systems from previous research that attempt to merge several indicator filters together by giving each one a weight as a percentage of the whole, or which build a decision tree based rule comprised of several indicators. The detrimental effects of short-term market fluctuations on the effectiveness of the optimization are considered, and attempts to mitigate these effects by reducing the length of the optimization interval are discussed. Finally, the optimized indicators are used in simulated trading, using historical data. The results from the simulation are compared with the annual returns of the NASDAQ – 100 Index on a yearly basis over a period of four years. The comparison shows that the composite indicator filter is proficient enough at filtering out inferior buy signals to substantially outperform the NASDAQ – 100 Index during each year of the simulation.
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