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Trading Simulator Station?
Dear OnTheRoad,
Can you please explain to me in short what is this software? and how efficient is the random number generator within it?
Thank you for your contribution.
LittleWalker.
please consider the attachments
Hi,
I really didn't test these programs,
I saw them in FF , and attached them here , might be useful,
and if you are a coder,then you can help Mr Barnix for coding good strategy tester for his Better EA,
I am coding now a trade training algorithm, but it need massive effort and time to completion,
thx.
SVM_EA_v25c_
Forwardtest demo
Should I normalize/standardize/rescale the
First, some definitions. "Rescaling" a vector means to add or subtract a
constant and then multiply or divide by a constant, as you would do to
change the units of measurement of the data, for example, to convert a
temperature from Celsius to Fahrenheit.
"Normalizing" a vector most often means dividing by a norm of the vector,
for example, to make the Euclidean length of the vector equal to one. In the
NN literature, "normalizing" also often refers to rescaling by the minimum
and range of the vector, to make all the elements lie between 0 and 1.
"Standardizing" a vector most often means subtracting a measure of location
and dividing by a measure of scale. For example, if the vector contains
random values with a Gaussian distribution, you might subtract the mean and
divide by the standard deviation, thereby obtaining a "standard normal"
random variable with mean 0 and standard deviation 1.
Two of the most useful ways to standardize inputs are:
o Mean 0 and standard deviation 1
o Midrange 0 and range 2 (i.e., minimum -1 and maximum 1)
Note that statistics such as the mean and standard deviation are computed
from the training data, not from the validation or test data. The validation
and test data must be standardized using the statistics computed from the
training data.
Formulas are as follows:
Should I normalize/standardize/rescale the
First, some definitions. "Rescaling" a vector means to add or subtract a
constant and then multiply or divide by a constant, as you would do to
change the units of measurement of the data, for example, to convert a
temperature from Celsius to Fahrenheit.
"Normalizing" a vector most often means dividing by a norm of the vector,
for example, to make the Euclidean length of the vector equal to one. In the
NN literature, "normalizing" also often refers to rescaling by the minimum
and range of the vector, to make all the elements lie between 0 and 1.
"Standardizing" a vector most often means subtracting a measure of location
and dividing by a measure of scale. For example, if the vector contains
random values with a Gaussian distribution, you might subtract the mean and
divide by the standard deviation, thereby obtaining a "standard normal"
random variable with mean 0 and standard deviation 1.
Two of the most useful ways to standardize inputs are:
o Mean 0 and standard deviation 1
o Midrange 0 and range 2 (i.e., minimum -1 and maximum 1)
Note that statistics such as the mean and standard deviation are computed
from the training data, not from the validation or test data. The validation
and test data must be standardized using the statistics computed from the
training data.
Formulas are as follows:THANKS barnix, i love ur work... its keep me inspired ...hmm barnix do you've read this site
http://www.hernandezgomez.com/index.php/using-a-pnn-probabilistic-neural-network-and-metatrader-4-mql4-to-trade-forex/
explanation from Paco Hernandez Gomez... Similar with your PNN training ....
regards,
MANSTIR
1-Tester_v04_5_EURUSD
2-SVM_EA_v25c
1-Tester_v04_5a_EURUSD
standardize with range
2-Tester_v04_5_EURUSD
standardize with med,std
1-Tester_v04_5b_EURUSD
standardize with range calculated from train data
2-Tester_v04_5_EURUSD
standardize with med,std