Support Vector Regression (SVR) is a form of regression derived from Support Vector Machines. At its core, SVR uses kernel methods to map input data into higher-dimensional spaces, allowing for more complex relationships to be captured, which contrasts with dimensionality reduction. For this article though we are exploring strictly its loss function role when used with a multi-layer perceptron. A related but different form of regression we looked at in an earlier article was Gaussian Process ...
Custom signal classes for wizard assembled Expert Advisors can take on various roles, that are worth exploring, and we continue this quest by examining how the Q-Learning algorithm when paired with Markov Chains can help refine the learning process of a multi-layer-perceptron network. Q-Learning is one of the several (approximately 12) algorithms of reinforcement-learning, so essentially this is also a look at how this subject can be implemented as a custom signal and tested within a wizard ...
In the previous article, we started developing a multi-currency EA that works simultaneously with various trading strategies. At the first stage there were only two different strategies. They represented the implementation of the same trading idea, worked on the same trading instrument (symbol) and chart period (timeframe). They differed from each other only in the numerical values of the parameters. We are now only interested in testing the suitability of this approach, and ...
This is a unique research, the idea for which came to me while answering questions that arose during the discussion of one of my articles. I am hopeful that readers will appreciate the value and originality of this work. To carry out the experiment, we need to first initialize the coordinates of the agents forcibly outside the algorithm, using the coordinates of the global minimum, before measuring the fitness function at the first epoch. Such an experiment will ...
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