After playing around with Stock Neuromaster, I realized that it transforms the time series data into a zig zag line or moving average and then learns the relationship between it and the price & volume data. The zig zag line connects high and low points together based on a % retracement level you define or a time period you choose for a moving average. Once the data is transformed to reflect this, its fed into the neural net as your output variable and the time series data becomes your input variables.
In this video tutorial, viewers will be able to create a similar type of model for Apple and learn how to use a great time saving data preprocessing operator called LabelTrend2Classification, see how to create Attribute and Data files, and use the NeuralNet learner from Rapidminer 4.2
As a parting gift, you can download the data files and Rapidminer experiment here.