Fuzzy Logic

Mathematical logic that deals with modelling uncertainty taking into account the “nuances” of reality (things do not work in a binary way). Useful for dealing with the uncertainty, suitable for representing the situations in which financial and energy markets move. It is the evolution of Real Fuzzy Logic, enhanced through a complex numbers-valued degree of truth, this allows for better prediction of periodic and chaotic systems, where multiple fuzzy variables are correlated in a complex way that cannot be effectively described by simple real-valued fuzzy interactions.

Neural Networks

Subset of machine learning, at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another. Neural networks rely on training data to learn and improve their accuracy over time. However, once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and AI. Trough the learning process we calculate the best values of parameters of each algorithm. These parameters are related to input variables, their logical connections and to the mathematical model itself.


Adaptive algorithms, based on natural evolution of species, aimed at solving research and optimization problems. The genetic algorithm repeatedly modifies a population of individual solutions, reflecting the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation. Genetic Algorithms solve the model network parameters maximizing the fitness function, minimizing drawdowns and maximizing profits.

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