I have a university degree in computer programming and artificial intelligence was one of my hobbies. Such unscrupulous sellers are using the word AI just because it sounds good, it brings something new and naive newbies are jumping without a second thought.
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Of course, that kind of system fail miserably after a few months, the most. Take the following code for example, written in pseudocode: It adds two numbers, a and b and displays the result.
The computer already know how to add two or more numbers because the addition rule is already stored in the programming language specific libraries as a function.
This is the classic approach. The third number is the result of adding the first two numbers. By analyzing the data set, the computer must figure out what is the relation between the first two numbers and the third number in such a way that if two numbers that are not present in the data set are supplied it must be able to display the third number.
In other words, the computer must learn the addition rule! This is artificial intelligence. Before diving into details please note that we have a very exact pattern here: What is a neural network and how does it work? Basically, a neural network is a black box: The block cell of a neural network is the neuron. Like a biological neuron, it takes inputs, process them, then returns the output.
A biological neuron can have 2 states: The mathematical model of a neuron is:. The memory of a neural network lays in its weights.
We have two inputs and one output. I assign a neuron for each input and one neuron for the output. Our small network consists of two input neurons, a layer of two weights and one output neuron. A neural network can have more layers composed of weights: The activation function usually the sigmoid function returns the strength of the signal.
The sigmoid function is chosen because it is a symmetric function with values from -1 to 1. By following the model 2 and mathematical description of a neural network, we must find the weights that satisfies the equations:.
If instead of A1 and A2 we supply 10 an 21 as inputs, the neural network must return 31 as a result. The neural network has just learned the addition rule!
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After all, a neural network is the ultimate form of curve fitting. It needs constant tweaking so it can be able to chase the market, and the tweaking is a laborious process that can take days and a large computing power.
If you see a sales page advertising them as the forex holy grail, just press the X button located at the right top of the page. Do that and save your money. I gathered inputs for as many forex robots I could find and selected only the winning trades. Then, I analyzed the market conditions prior to trade opening volume, volatility, support and resistance points, how close the trade was from a few moving averages, and so on.
I have created a pattern of winning conditions, I normalized it and fed the neural networks and bayesian filters with it.
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The results are great. Are you interested in a pay per profit scheme? Home About me Contact me Old forex robots. Now back to AI. Consider the following data set: Zamolxis Tradind System Zamolxis Forex Robot Download Subscribe Subscribe and Download Zamolxis. Details here Yes No View Results.