Finding a set of indicators to feed into the neural network inputs. Discussion. A tool for evaluating the results. - page 2
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On the fingers.
One speaks, the other answers.
The height of a person (cm):
1-unlikely
10- it can't be
30- unlikely
100-maybe
176-true
200-small chance
230-can't be.
300-unlikely.
This is an example of a non-linear transformation.
What if we do it on the principle of the lucky monkey. For example let's take CCI and check it on all available history, we will choose profitable sectors that will not lose all the time. Then we take Momentum, Bollinger, Muvings and choose profitable areas. Trading is done virtually and a system that shows as good as the initial selection is admitted for real trading. If history repeats it should work. Also, the advantage of this approach is an approximate estimate of the duration of a good situation. What are your criteria for selecting profitable areas like number of trades, the average transaction, maximum drawdown, duration of a profitable area, I have a small idea, I'll tell you later.
You can go in the other thread here.Hello
I have always been interested to learn about NS, but as soon as I start to read some literature on the subject my head starts to boil and finally I cannot even understand what NS is
could you give a simple example (on the fingers, so to speak) to explain what it is
>> thank you
Catch.
Catch.
i takoe uge chital mnogo navernoe mne ne dano poniat chto takoe NC
cpacibo
I takoe uge chital mnogo navernoe mne ne dano poniat chto takoe NC
cpacibo
What do you need!?
Yes understand how the NS understands that 176 cm is true
>> yes understand how the NS understands that 176cm is true
>>. But sin90% = 1 is true?
to understand how the NS understands that 5'7" is true.
Here comes the process!
My previous example is a non-linear bell-shaped transformation function
In general, the most commonly used function, at least by me, is (2/(1-2^(-x))-1.
Experiment in excel or matcad with the example above. much will become clear.
you go to another thread here.
I too am very interested in finding a minimum set of indicators and evaluation of results, but for my own purposes.
Only instead of closing price we should use the result of trade. Who is good at geometry, correct the code
double Dispersia (int i, int N)
{
// in this subprogram we calculate dispersion of close price deviation from
// linear regression line
// imho the dispersion from the average value is not quite adequate as the distribution
// described by e.g. y=b*x+c then the dispersion (from the average) depends on the slope angle, //the depth of
// the sample and the spread. In my case the variance depends only on the spread.
In my case the dispersion depends only on the spread. // It is of course preferable to use a power or an exponent, especially when calculating systems // based on acceleration rates.
//driven acceleration rates, then I am sorry, I am too dumb.
double Pi=3.141592653589793 ; // forget the equivalence operator .
int j;
double a,b,Summ_x,Summ_y,Summ_x_2,Summ_xy,Deviation,StdDeviation,Sredn_y,AC;
for (int x=1;x<N;x++)
{ j=N-x+i;
Summ_x=Summ_x+x;
Summ_y=Summ_y+Close[j];
Summ_xy=Summ_xy+x*Close[j];
Summ_x_2=Summ_x_2+MathPow(x,2);
}
b=((N-1)*Summ_xy-Summ_x*Summ_y)/((N-1)*Summ_x_2-MathPow(Summ_x,2));
a=(Summ_y-b*Summ_x)/(N-1);
Sredn_y=Summ_y/(N-1);
for ( x=N ;x>=1 ;x--)
{
j=N-x+i ;
if ( b >0 )
{
AC= MathAbs(Close[j]-(b*x+a))*MathSin(Pi/2-MathArctan(b)) ;
}
if ( b<0 )
{
AC=MathAbs(Close[j]-(b*x+a))*MathSin(MathArctan(b)-Pi/2) ;
}
Deviation=Deviation+ MathPow(AC,2) ;
}
StdDeviation=MathSqrt(Deviation/N);
return(StdDeviation*StdDev)
}
If we estimate the result according to this formula, then TS is described by two parameters the slope angle of the regression line the more the better and pseudodispersion the closer to zero the better.