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
