Neural network
In the previous section, I implemented a model using two classes. In reality, it might be possible that traders do not want to enter trade when the market is range-bound. That is to say, we have to add one more class, Nowhere, to the existing two classes. Now we have three classes: Up, Down, and Nowhere. I will be using an artificial neural network to predict Up, Down, or Nowhere direction. Traders buy (sell) when they anticipate a bullish (bearish) trend in some time and no investment when the market is moving Nowhere. An artificial neural network with feedforward backpropagation will be implemented in this section. A neural network requires input and output data to the neural network. Closing prices and indicators derived from closing prices are input layer nodes and three classes (Up, Down, and Nowhere) are output layer nodes. However, there is no limit on the number of nodes in the input layer. I will use a dataset consisting of prices and indicators used in the logistic...
