Articoli
Deep Neural Networks (Part VIII). Increasing the classification quality of bagging ensembles per MetaTrader 5
The article considers three methods which can be used to increase the classification quality of bagging ensembles, and their efficiency is estimated. The effects of optimization of the ELM neural network hyperparameters and postprocessing parameters are evaluated
Deep Neural Networks (Part VII). Ensemble of neural networks: stacking per MetaTrader 5
We continue to build ensembles. This time, the bagging ensemble created earlier will be supplemented with a trainable combiner — a deep neural network. One neural network combines the 7 best ensemble outputs after pruning. The second one takes all 500 outputs of the ensemble as input, prunes and
Deep Neural Networks (Part VI). Ensemble of neural network classifiers: bagging per MetaTrader 5
The article discusses the methods for building and training ensembles of neural networks with bagging structure. It also determines the peculiarities of hyperparameter optimization for individual neural network classifiers that make up the ensemble. The quality of the optimized neural network
Deep Neural Networks (Part V). Bayesian optimization of DNN hyperparameters per MetaTrader 5
The article considers the possibility to apply Bayesian optimization to hyperparameters of deep neural networks, obtained by various training variants. The classification quality of a DNN with the optimal hyperparameters in different training variants is compared. Depth of effectiveness of the DNN
Deep Neural Networks (Part IV). Creating, training and testing a model of neural network per MetaTrader 5
This article considers new capabilities of the darch package (v.0.12.0). It contains a description of training of a deep neural networks with different data types, different structure and training sequence. Training results are included
Deep Neural Networks (Part III). Sample selection and dimensionality reduction per MetaTrader 5
This article is a continuation of the series of articles about deep neural networks. Here we will consider selecting samples (removing noise), reducing the dimensionality of input data and dividing the data set into the train/val/test sets during data preparation for training the neural network
Deep Neural Networks (Part II). Working out and selecting predictors per MetaTrader 5
The second article of the series about deep neural networks will consider the transformation and choice of predictors during the process of preparing data for training a model
Deep Neural Networks (Part I). Preparing Data per MetaTrader 5
This series of articles continues exploring deep neural networks (DNN), which are used in many application areas including trading. Here new dimensions of this theme will be explored along with testing of new methods and ideas using practical experiments. The first article of the series is dedicated
Self-optimization of EA: Evolutionary and genetic algorithms per MetaTrader 5
This article covers the main principles set fourth in evolutionary algorithms, their variety and features. We will conduct an experiment with a simple Expert Advisor used as an example to show how our trading system benefits from optimization. We will consider software programs that implement
Deep neural network with Stacked RBM. Self-training, self-control per MetaTrader 4
This article is a continuation of previous articles on deep neural network and predictor selection. Here we will cover features of a neural network initiated by Stacked RBM, and its implementation in the "darch" package
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Gli Stati Uniti si preparano a fermare le quotazioni dei metalli preziosi e a fissare il tasso di cambio ufficiale del dollaro 29.12.14 17:10 Economia Fonte: Les États-Unis préparent la fine della cotation des métaux précieux et verrouillent le cours officiel du dollar A partire dal 22 dicembre