newdigital

In the previous article, we have implemented the ability to choose the strategy option - with a constant position size and with a variable position size. This allowed us to introduce normalization of the results of the strategies' work according to the maximum drawdown and provided the possibility of combining them into groups, for which the maximum drawdown was also within the specified limits. For the sake of demonstration, we manually selected several of the most attractive combinations

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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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The Ichimoku Kinko Hyo, that is often referred to as the Ichimoku Cloud, is a wide-embracing technical analysis system designed to provide a holistic view of market trends; support and resistance levels; as well as momentum. Developed in the late 1930s and refined over the subsequent three decades, Ichimoku Kinko Hyo translates from Japanese to "one-look equilibrium chart" or "at-a-glance balance chart,"; names which emphasize its design to offer a single, detailed perspective on the market

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Overfitting in machine learning can take on many different forms. Most commonly, it happens when an AI model learns too much of the noise in the data, and fails to make any useful generalizations. This leads to dismal performance when we assess the model on data it has not seen before. There are many techniques that have been developed to mitigate overfitting, but such methods can often prove challenging to implement, especially when you are just getting started on your journey. However,

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Forecasting time series of future prices is critical in various financial market scenarios. Most of the methods that currently exist are based on certain autocorrelation in the data. In other words, we exploit the presence of correlation between time steps that exists both in the input data and in the predicted values.
Among the models gaining popularity are those based on the Transformer architecture that use Self-Attention mechanisms for dynamic autocorrelation estimation. Also, we

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