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Let's get acquainted with a new model family: Ordinary Differential Equations. Instead of specifying a discrete sequence of hidden layers, they parameterize the derivative of the hidden state using a neural network. The results of the model are calculated using a "black box", that is, the Differential Equation Solver. These continuous-depth models use a constant amount of memory and adapt their estimation strategy to each input signal. Such models were first introduced in the paper "Neural Ordinary Differential Equations".

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The ADX indicator is an oscillator that is meant to measure how strong a given trend in the market is for the security to which it is attached. This article thus, like some that were preceding it, examines the various patterns exhibited by this indicator in a custom signal class file, for wizard assembled Expert Advisors. We review one pattern at a time as we have in the previous articles covering RSI, Bollinger Bands, and the RSI and then conclude with a test that includes all patterns to

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Nature is an inexhaustible source of inspiration for scientists and engineers seeking to create innovative solutions. One of such remarkable phenomena is mutualism - a mutually beneficial biological interaction between different species. Living organisms participating in mutualistic relationships strive to obtain mutual benefits from this interaction, aimed at the development and diversity of the population.

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We continue these series on the different ways key component classes of wizard assembled Expert Advisors can be implemented by considering 2 Gaussian Process Kernels. The linear-kernel and Matérn Kernel. The former is so simple you cannot find its Wikipedia-page, however the latter has a reference page over here.

There are several additional advantages and uses to GPs like noise handling & reduction as well as adaptation to regime change and many others. As traders, though,

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Forecasting the trajectory of the upcoming price movement probably plays one of the key roles in the process of constructing trading plans for the desired planning horizon. The accuracy of such forecasts is critical. In an attempt to improve the quality of trajectory forecasting, we complicate our trajectory forecasting models.

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