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That was a horrible formatting job.
The message number you want for the code that is formatted is msg
#115464
[This code is leading you down a very difficult path in your
trading. Learn about support/resistance, price action, market
internals and good money management techniques and you'll have your
own personal market ATM machine. Persist with things like this and
you'll never trade full-time for a living...if that is your goal. I
don't say this to irritate or anger you. I say this because trading
never has to be this obtuse to succeed. Simple things work. ]
--- In amibroker@xxxxxxxxxxxxxxx, "scourt2000" <stevehite@xxx> wrote:
>
>
> Here is what you seek. Please use it trading the ER2 e-mini in the
> U.S. market (I need the order flow):
>
> #include <math.h>
> #include <stdlib.h>
> #include <iostream>
> double input_hidden_weights[5][6]=
> {
> {9.40985654649721e-001, 9.23136985641824e+000, -
> 2.86994950445746e+000, -1.26803931926022e+000, -
> 7.70008830456271e+000, 1.25421731000059e+000 },
> {3.08886279182467e+000, 5.78057919510794e+000,
> 1.10741139003555e+001, -3.65436515636221e+000, -
> 1.46817775035674e+001, 2.22520984659718e+001 },
> {1.69160825877441e-001, -9.91646811530742e+000, -
> 1.05635083282757e+001, 4.25517810226395e+000,
2.50631134577816e+000, -
> 2.91169885599088e+000 },
> {-6.47564626574024e-001, -6.79555863835188e+000, -
> 4.77495170169956e+000, 1.35029793165373e+000,
2.78079262918906e+000, -
> 1.90795645602409e+000 },
> {1.43573516935369e+000, 7.90014107622549e+000,
> 1.34639286162717e+000, -4.87454998380292e-001, -
> 5.75300672778634e+000, 2.44385407464775e+000 }
> };
> double hidden_bias[5]={ -1.72504436049218e+000, -
> 1.12353474266980e+001, 1.05782985606737e+001,
6.38618458656671e+000, -
> 4.76099367812151e+000 };
> double hidden_output_wts[1][5]=
> {
> {4.85655076789677e+000, 5.85365165791789e-001,
> 3.11879288899187e+000, -8.48772823147294e+000, -
> 9.35987517826960e+000 }
> };
> double output_bias[1]={ 5.51616000361064e+000 };
> double max_input[6]={ 3.02200000000000e-001, 6.05400000000000e-001,
> 1.81400000000000e-001, 1.59000000000000e-002, 6.06600000000000e-
001,
> 2.43900000000000e-001 };
> double min_input[6]={ -2.82000000000000e-001, -8.46700000000000e-
> 001, -1.95700000000000e-001, -9.70000000000000e-003, -
> 5.52700000000000e-001, -2.45100000000000e-001 };
> double max_target[1]={ 1.35100000000000e-001 };
> double min_target[1]={ -1.50800000000000e-001 };
> double input[6];
> double hidden[5];
> double output[1];
> void FindMax(double* vec, double* max, long* maxIndex,int len)
> {
> long i;
> *max = vec[0];
> *maxIndex = 0;
> for(i=1; i<len; i++)
> {
> if(vec[i]>*max)
> {
> *max = vec[i];
> *maxIndex = i;
> }
> }
> }
> void FindMin(double* vec, double* min, long* minIndex,int len)
> {
> long i;
> *min = vec[0];
> *minIndex = 0;
> for(i=1; i<len; i++)
> {
> if(vec[i]<*min)
> {
> *min = vec[i];
> *minIndex = i;
> }
> }
> }
> void ScaleInputs(double* input, double min, double max, int size)
> {
> double delta;
> long i;
> for(i=0; i<size; i++)
> {
> delta = (max-min)/(max_input[i]-min_input[i]);
> input[i] = min - delta*min_input[i]+ delta*input[i];
> }
> }
> void UnscaleTargets(double* output, double min, double max, int
size)
> {
> double delta;
> long i;
> for(i=0; i<size; i++)
> {
> delta = (max-min)/(max_target[i]-min_target[i]);
> output[i] = (output[i] - min + delta*min_target[i])/delta;
> }
> }
> double logistic(double x)
> {
> if(x > 100.0) x = 1.0;
> else if (x < -100.0) x = 0.0;
> else x = 1.0/(1.0+exp(-x));
> return x;
> }
> void ComputeFeedForwardSignals(double* MAT_INOUT,double*
V_IN,double*
> V_OUT, double* V_BIAS,int size1,int size2,int layer)
> {
> int row,col;
> for(row=0;row < size2; row++)
> {
> V_OUT[row]=0.0;
> for(col=0;col<size1;col++)V_OUT[row]+=(*(MAT_INOUT+(row*size1)+col)
> *V_IN[col]);
> V_OUT[row]+=V_BIAS[row];
> if(layer==0) V_OUT[row] = logistic(V_OUT[row]);
> if(layer==1) V_OUT[row] = tanh(V_OUT[row]);
> }
> }
> void RunNeuralNet_Regression ()
> {
> ComputeFeedForwardSignals((double*)
> input_hidden_weights,input,hidden,hidden_bias,6, 5,0);
> ComputeFeedForwardSignals((double*)
> hidden_output_wts,hidden,output,output_bias,5, 1,1);
> }
>
>
>
>
>
>
> --- In amibroker@xxxxxxxxxxxxxxx, "Vinay Gakkhar," <vgakkhar@>
> wrote:
> >
> > Dear Prashanth, Dear Ton, Dear John,
> >
> > This is with reference to my following request of 5th Oct 2007 to
> > Prashanth, and your following interaction.
> >
> > Can i have the AFL John had posted in this forum on 23rd Sept,
> 2007, which
> > was reproduced by Prashanth in his reply to Ton on 6th October ,
> 2007 ? I
> > have not been able to locate it.
> >
> > Best regards,
> >
> > Vinay
> >
> >
> > Re: [amibroker] NEURAL ANALYSIS IN AMIBROKER
> >
> > Ton Sieverding
> > Sat, 06 Oct 2007 12:07:16 -0700
> >
> > Thanks John. Most important part for me is to hear that you got
AB
> running
> > a
> > NN. Please let me know the Intermaket data you were using. Also
> what's the
> > learning procedure for the NN ? In other words, can you please
give
> us a
> > little
> > more background music ?
> >
> > Regards, Ton.
> >
> > ----- Original Message -----
> > From: [EMAIL PROTECTED]
> > To: amibroker@xxxxxxxxxxxxxxx
> > Sent: Saturday, October 06, 2007 11:05 AM
> > Subject: Re: [amibroker] NEURAL ANALYSIS IN AMIBROKER
> >
> > PLease bare in mind that this is only the source code to the
NN
> in C++
> > You would still need to create the function and feed data in.
> > Thsi network is based on an example network i built a while
> back to
> > predict
> > the log return of the Aussie Dollar futures contract over the
next
> 50
> > days. It
> > needs to be fed the log returns of 6 intermarkets to provide a
> relevant
> > output.
> > the reason i posted the code was not so someone could use it
as
> a NN, but
> > to
> > emphasise teh fact that a NN built using a 3rd party tool COULD be
> > integrated
> > into AB using ADK and used in a trading system.
> >
> > John
> >
> > Prashanth <[EMAIL PROTECTED]> wrote:
> > Hello Ton,
> >
> > Attached is the AFL that a person by name John had posted
in
> this
> > forum on
> > 23-09-07.
> >
> > Cheers
> >
> > Prashanth
> >
> > ----- Original Message -----
> > From: "Ton Sieverding" <[EMAIL PROTECTED]>
> > To: <amibroker@xxxxxxxxxxxxxxx>
> > Sent: Saturday, October 06, 2007 12:11 AM
> > Subject: Re: [amibroker] NEURAL ANALYSIS IN AMIBROKER
> >
> > > Prashanth do you still know where to find the code for a
> NN ? I
> > really
> > would
> > > like to see how this works because I just don't
understand
> what AB
> > has to
> > do
> > > with NN ...
> > >
> > > Regards, Ton.
> > >
> > > ----- Original Message -----
> > > From: "Prashanth" <[EMAIL PROTECTED]>
> > > To: <amibroker@xxxxxxxxxxxxxxx>
> > > Sent: Friday, October 05, 2007 7:49 PM
> > > Subject: Re: [amibroker] NEURAL ANALYSIS IN AMIBROKER
> > >
> > >
> > > > Hello Vinay,
> > > >
> > > > I think this question of you rs was answered a while
> back. As far
> > as my
> > > > belief and knowledge is concerned, No, you cant create
a
> Neural
> > Network
> > > > using just AB.
> > > >
> > > > Someone had posted a formula which he said that proved
> that NN can
> > infact
> > > be
> > > > coded in AB, but since my C++ files have a compliation
> error, I
> > havent
> > > been
> > > > able to test out the same.
> > > >
> > > > There are a lot of NN based trading products in the
> market though
> > I am
> > not
> > > > sure as to whether they really can make money for the
> buyer since
> > if it
> > > > could, it woudnt be for sale in the first place.
> > > >
> > > > Cheers
> > > >
> > > > Prashanth
> > > >
> > > > ----- Original Message -----
> > > > From: "Vinay Gakkhar." <[EMAIL PROTECTED]>
> > > > To: "amibroker" <amibroker@xxxxxxxxxxxxxxx>
> > > > Sent: Friday, October 05, 2007 3:40 PM
> > > > Subject: [amibroker] NEURAL ANALYSIS IN AMIBROKER
> > > >
> > > >
> > > > > Dear Prashanth,
> > > > >
> > > > > Can you please tell me whether there is any formula
or
> add-on
> > for
> > > > > Amibroker which analyses the available past data like
a
> neural
> > network
> > > > > does, finds the common pattern, and shows the result
> like neural
> > > network?
> > > > >
> > > > > Thanks,
> > > > >
> > > > > vinay
> >
>
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