mql5

Deep neural network with Stacked RBM. Self-training, self-control

In preparation of data for conducting experiments, we will use variables from theprevious articleabout evaluating and selecting predictors. We will form the initial sample, clean it and select the important variables.We will consider ways of dividing the initial sample into training, testing and validation samples.
Using the "darch" package we will build a model of the DBN network, and train it on our sets of data. After testing the model, we will obtain metrics that will enable us to evaluate quality of the model. We will consider many opportunities that the package offers to configure settings of a neural network.
Also, we will see how hidden Markov models can help us improve neural network predictions.
We will develop an Expert Advisor where a model will be trained periodically on the fly without interruption in trade, based on results of continuous monitoring. The DBN model from the "darch" package will be used in the Expert Advisor. We will also incorporate the Expert Advisor built using SAE DBN from the previous article.
Furthermore, we will indicate ways and methods of improving qualitative indicators of the model.