19. Evolving Currency Trading Agents

The application of Neural Networks to financial analysis in general, and currency trading in particular, has been explored for a number of years. The most commonly [2,3,4,5] used NN training algorithm in this application is the backpropagation. The application of TWEANN systems to the same field is only now starting to emerge, and is showing a significant amount of potential. In this chapter we create a Forex simulator, and then use our neuroevolutionary system to evolve automated currency trading agents. For this application we will utilize not only the standard sliding window approach when feeding the sensory signals to the neural encoded agents, but also the sliding chart window, where we feed the evolved substrate encoded agents the actual candle-stick price charts, and then compare the performance of the two approaches. As of this writing, the use of geometrical pattern sensitive NN based agents in the analysis of financial instrument charts has not yet been explored in any other paper, to this author’s knowledge. Thus in this chapter we pioneer this approach, and explore its performance and properties.