In the first article of the series, we loaded the dataset, placed labels, enriched the dataset and also performed dataset labeling. The second article was devoted to the creation and training of the model, as well as implementation of cross-validation and bagging. Now that our model is trained and tested, it is time to start real trading using the MetaTrader 5 library for Python. This powerful library allows us to automate trading directly through Python using the functions ...
In the previous article, we got acquainted with the ATFNet algorithm, which is an ensemble of 2 time series forecasting models. One of them works in the time domain and constructs predictive values of the studied time series based on the analysis of signal amplitudes. The second model works with the frequency characteristics of the analyzed time series and records its global dependencies, their periodicity and spectrum. Adaptive merging of two independent forecasts, according to the author ...
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