Better NN EA development - page 12
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My friend SignalsBG make some research in Better system results and conclusions are published here^ .May be this will be usefull for you.
I parste SignalsBG‘s posts here for us discuss...just for common Target...
SignalsBG:
Many people believe that this system is based on Neural Networks, but i don't think so.The neural networks are nothing more than simple optimization.There is nothing mysterious about them.The chance of such system working good is the same as one simple system with 10 parameters which are optimized for the past.So, to create neural network with such a performance, you need good rules, and if you have such rules, you don't need neural network.
There are several things that we know about that system.It is easy to see, that there are three independent systems.They are with different Id , and slight change in their parameters.So let's take the first system.It's id is 854.
You can visualize the deals of that subsystem on M1 chart , using the script in the attachment.
It's easy to see that there is a simple rule what trade is next.
So if we have buy, the next must be sell.If we have sell the next deal must be buy.There is also fixed stop and limit of 60 points.
If have a closer look, we will see that all the deals are at local extremums.So we are adding Stochastic oscilator to the chart(M1).We will use standard parameters 5 3 3.
So, all the deals are when the oscillator is oversold.This is for both buy and sell.After that, there is some time without position.(between the two yellow lines).And after that he is starting to use only the overbought levels of the indicator for both buy and sell.
So there is a filter, which levels(overbought oversold) to use for the next signal, and a filter to choose the right signal. Such filter can be something like MACD, or simple moving average.
PNN (Probabilisti Neural Network)
Class 1:
Iris Setosa
Attributes:
sepal_length sepal_width petal_length petal_width
5.1 3.5 1.4 .2
4.9 3. 1.4 .2
4.7 3.2 1.3 .2
4.6 3.1 1.5 .2
5. 3.6 1.4 .2
5.4 3.9 1.7 .4
4.6 3.4 1.4 .3
5. 3.4 1.5 .2
4.4 2.9 1.4 .2
4.9 3.1 1.5 .1
5.4 3.7 1.5 .2
4.8 3.4 1.6 .2
4.8 3. 1.4 .1
5.8 4. 1.2 .2
4.3 3. 1.1 .1
5.7 4.4 1.5 .4
5.4 3.9 1.3 .4
5.1 3.5 1.4 .3
5.7 3.8 1.7 .3
5.1 3.8 1.5 .3
5.4 3.4 1.7 .2
5.1 3.7 1.5 .4
4.6 3.6 1. .2
5.1 3.3 1.7 .5
4.8 3.4 1.9 .2
5. 3. 1.6 .2
5. 3.4 1.6 .2
5.2 3.5 1.5 .2
5.2 3.4 1.4 .2
4.7 3.2 1.6 .2
4.8 3.1 1.6 .2
5.4 3.4 1.5 .4
5.2 4.1 1.5 .1
5.5 4.2 1.4 .2
4.9 3.1 1.5 .2
5. 3.2 1.2 .2
5.5 3.5 1.3 .2
4.9 3.6 1.4 .1
4.4 3. 1.3 .2
5.1 3.4 1.5 .2
5. 3.5 1.3 .3
4.5 2.3 1.3 .3
4.4 3.2 1.3 .2
5. 3.5 1.6 .6
5.1 3.8 1.9 .4
4.8 3. 1.4 .3
5.1 3.8 1.6 .2
4.6 3.2 1.4 .2
5.3 3.7 1.5 .2
5. 3.3 1.4 .2
Class 2:
Iris Versicolor
Attributes:
sepal_length sepal_width petal_length petal_width
7. 3.2 4.7 1.4
6.4 3.2 4.5 1.5
6.9 3.1 4.9 1.5
5.5 2.3 4. 1.3
6.5 2.8 4.6 1.5
5.7 2.8 4.5 1.3
6.3 3.3 4.7 1.6
4.9 2.4 3.3 1.
6.6 2.9 4.6 1.3
5.2 2.7 3.9 1.4
5. 2. 3.5 1.
5.9 3. 4.2 1.5
6. 2.2 4. 1.
6.1 2.9 4.7 1.4
5.6 2.5 3.9 1.1
6.7 3.1 4.4 1.4
5.6 3. 4.5 1.5
5.8 2.7 4.1 1.
6.2 2.2 4.5 1.5
5.6 2.5 3.9 1.1
5.9 3.2 4.8 1.8
6.1 2.8 4. 1.3
6.3 2.5 4.9 1.5
6.1 2.8 4.7 1.2
6.4 2.9 4.3 1.3
6.6 3. 4.4 1.4
6.8 2.8 4.8 1.4
6.7 3. 5. 1.7
6. 2.9 4.5 1.5
5.7 2.6 3.5 1.
5.5 2.4 3.8 1.1
5.5 2.4 3.7 1.
5.8 2.7 3.9 1.2
6. 2.7 5.1 1.6
5.4 3. 4.5 1.5
6. 3.4 4.5 1.6
6.7 3.1 4.7 1.5
6.3 2.3 4.4 1.3
5.6 3. 4.1 1.3
5.5 2.5 4. 1.3
5.5 2.6 4.4 1.2
6.1 3. 4.6 1.4
5.8 2.6 4. 1.2
5. 2.3 3.3 1.
5.6 2.7 4.2 1.3
5.7 3. 4.2 1.2
5.7 2.9 4.2 1.3
6.2 2.9 4.3 1.3
5.1 2.5 3. 1.1
5.7 2.8 4.1 1.3
Class 3:
Iris Virginica
Attributes:
sepal_length sepal_width petal_length petal_width
6.3 3.3 6. 2.5
5.8 2.7 5.1 1.9
7.1 3. 5.9 2.1
6.3 2.9 5.6 1.8
6.5 3. 5.8 2.2
7.6 3. 6.6 2.1
4.9 2.5 4.5 1.7
7.3 2.9 6.3 1.8
6.7 2.5 5.8 1.8
7.2 3.6 6.1 2.5
6.5 3.2 5.1 2.
6.4 2.7 5.3 1.9
6.8 3. 5.5 2.1
5.7 2.5 5. 2.
5.8 2.8 5.1 2.4
6.4 3.2 5.3 2.3
6.5 3. 5.5 1.8
7.7 3.8 6.7 2.2
7.7 2.6 6.9 2.3
6. 2.2 5. 1.5
6.9 3.2 5.7 2.3
5.6 2.8 4.9 2.
7.7 2.8 6.7 2.
6.3 2.7 4.9 1.8
6.7 3.3 5.7 2.1
7.2 3.2 6. 1.8
6.2 2.8 4.8 1.8
6.1 3. 4.9 1.8
6.4 2.8 5.6 2.1
7.2 3. 5.8 1.6
7.4 2.8 6.1 1.9
7.9 3.8 6.4 2.
6.4 2.8 5.6 2.2
6.3 2.8 5.1 1.5
6.1 2.6 5.6 1.4
7.7 3. 6.1 2.3
6.3 3.4 5.6 2.4
6.4 3.1 5.5 1.8
6. 3. 4.8 1.8
6.9 3.1 5.4 2.1
6.7 3.1 5.6 2.4
6.9 3.1 5.1 2.3
5.8 2.7 5.1 1.9
6.8 3.2 5.9 2.3
6.7 3.3 5.7 2.5
6.7 3. 5.2 2.3
6.3 2.5 5. 1.9
6.5 3. 5.2 2.
6.2 3.4 5.4 2.3
5.9 3. 5.1 1.8
Attributes1= sepal_length
Attributes2= sepal_width
Attributes3= petal_length
Attributes4= petal_width
========================
Class 1= Iris Setosa
Class 2= Iris Versicolor
Class 3= Iris Virginica
barnix, what is all this? Image recognition?
Please, if is possible add to posts source code.
barnix, what is all this? Image recognition? Please, if is possible add to posts source code.
The IRIS plants database is one of the best known database used in pattern recognition studies. The original method was proposed by a guy named R.A Fisher back in 1936 google for his paper "The use of multiple measurements in taxonomic problems"
There's some MQL code for solving these types of problems earlier in this thread
Explanation from Neural Networks
by
Timothy A. Corbett-Clark
The Artificial Neural Network Architecture (ANNA)
Simulación computacional - The Artificial Neural Network Architecture (ANNA)