Because there have been many significant advancements lately, artificial intelligence and neural networks are making quite a few headlines - and will continue to do so as they impact more aspects of our lives. While the math and science underlying each are fairly complicated, understanding the basics and applying them in user-friendly programs such as
TradingSolutions, is relatively simple. Here's an overview of each:
Artificial intelligence is a term used to refer to a broad category of technologies that allow a computer to simulate learning or subjective reasoning. It includes neural networks, genetic algorithms, expert systems and other approaches that you may have heard of. Each of these technologies has different strengths and weaknesses that make them good at solving particular problems.
Neural networks offer some exciting prospects because they can actually learn from data to train themselves to perform more effectively. Neural networks try to model how the biological human brain works by simulating the basic functions of natural neurons. Artificial neural networks consist of layers of artificial neurons, which are then connected to each other with varying parameters that represent the strengths of these connections. The networks "learn" when they adjust these strengths to cause the overall network to improve its results.
Sound complicated? What's important to understand is that a neural network allows your computer to sort through large amounts of data and recognize and match complicated, vague or incomplete patterns. For this reason, neural networks are well suited for projecting what will most likely happen next.
TradingSolutions makes it easy to utilize this benefit of neural networks without requiring you to understand the details of the work being done behind the scenes.
Genetic algorithms are specialized computer programs that are able to efficiently solve a problem by intelligently searching through a large number of potential solutions for that problem. They are typically able to find good solutions to a problem much faster than traditional search methods. The idea behind genetic algorithms is to simulate the way nature uses evolution by applying the "Survival of the Fittest" principle to small sets of its potential solutions. The good solutions reproduce to form new and hopefully better solutions to the problem, while the bad solutions are removed.
Genetic algorithms can be combined with neural networks to enhance their performance by taking some of the guesswork out of choosing the best neural network parameters and inputs. They can also be used to increase the opportunities of your trading signals.