Fuzzy-Neural Decision Maker for Technical Analysis Indicators Using Genetic Optimization of Fuzzy Functions


Technical analysts use statistics of market activity, such as past prices and volume, to identify patterns that can suggest future stock activity. Considerable skill and experience is involved in such an analysis. Recent work has explored the use of intelligent techniques to analyze and determine technical trading signals, with some success. This paper builds on previous efforts by proposing a fuzzy-neural decision making system that implements genetic optimization of the fuzzy membership functions. A base-10 genetic algorithm is used to optimize the shape of the membership functions foT determining the fuzzified values of the technical indicators. A neural network is developed to determine a final trading decision using the fuzzified values as inputs. Preliminary simulation results show improved performance when compared to the technical indicator alone, as well as an un-optimized fuzzy-neuro system using the technical indicator.

  • Abstract
  • Introduction
  • Research Background
  • Technical Indicators
  • Fuzzy-Neuro Decision Maker with Genetic Optimization of Membership Functions
  • Simulation and Results
  • Conclusions
  • References

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