For the researcher who would like to gain more information about the basic principles of our strategies, we could easily say that those strategies have their origin in Quantum Physics. Put it simply, it’s the physics that explains how everything works: the best description we have of the nature of the particles that make up matter and the forces with which they interact.

Quantum mechanics is a fundamental theory in physics that provides a description of the physical properties of nature at the scale of atoms and subatomic particles.

It is the foundation of all quantum physics including quantum chemistry (focused on the application of quantum mechanics in physical models and experiments of chemical systems), quantum field theory (combination of special relativity and quantum mechanics, but not general relativity’s description of gravity), quantum technology (a class of technology that works by using the principles of quantum mechanics (the physics of sub-atomic particles), including quantum entanglement and quantum superposition), and quantum information science (an area of study about information science related to quantum effects in physics.

It includes theoretical issues in computational models as well as more experimental topics in quantum physics including what can and cannot be done with quantum information).

“Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. In fact, it is the number of node layers, or depth, of neural networks that distinguishes a single neural network from a deep learning algorithm, which must have more than three.

Deep learning, while sounding flashy, is really just a term to describe certain types of neural networks and related algorithms that consume often very raw input data. They process this data through many layers of nonlinear transformations of input data in order to calculate a target output. Deep learning represents the very cutting edge of artificial intelligence (AI).

Instead of teaching computers to process and learn from data (which is how machine learning works), with deep learning, the computer trains itself to process and learn from data.

 Systematic Trading: Why Should You Use a Trading System? The most important reason to use a trading system is to gain a “statistical edge.” This often-used term simply means that you have tested the system and the profit of the average trade— including all losing and winning trades—is a positive number. This average trade profit is large enough to make this system worth trading—it covers trading costs, slippage, and is, on average, likely to perform better than competing systems.