How do you count points above the horizontal line?

Hello everyone. Could you please help me write code to count points above the horizontal red line. Thanks.

Answer 1

[Arthur Roué]

% Data array
Data = [....]

% Treshold
Limit = 0;

% Count
AboveLimit = sum(Data > Limit)

[Afua Amoako Dadey]

okay thanks but mr Arthur what if the code is as below

%Close prices in matrix

nstock_val=[Close(1:30928,1) Close1(1:30928,1) Close2(1:30928,1) Close3(1:30928,1) Close4(1:30928,1)]; % reading the data
t = [Gmttime]; % reading the data
time = datetime(t,'inputformat','dd.MM.yyyy HH:mm:ss.SSS');

LSE_matrix =log(nstock_val); %log of the datanstock_val=[Close(1:30928,1) Close1(1:30928,1) Close2(1:30928,1) Close3(1:30928,1) Close4(1:30928,1)]; % reading the data
I=1:(size(LSE_matrix,1)-1); % selecting the indices of all prices but the last time  when stock was opened


data = t(:,1);
id = [false; diff(time(:,1))> seconds(60)];%finds all row indices where the previous row
%differs by more than 60 seconds
data(id,:) = [];% deletes all data in those rows.
time(id) = [];

dLSE_col1 = LSE_matrix(I+1,1) - LSE_matrix(I,1);%  log difference

dLSE_col2 = LSE_matrix(I+1,2) - LSE_matrix(I,2);

dLSE_col3 = LSE_matrix(I+1,3) - LSE_matrix(I,3);

dLSE_col4 = LSE_matrix(I+1,4) - LSE_matrix(I,4);

dLSE_col5 = LSE_matrix(I+1,5) - LSE_matrix(I,5);
matrix_logdiff=[dLSE_col1 dLSE_col2 dLSE_col3 dLSE_col4 dLSE_col5];

nsample = floor((2/3)* length(matrix_logdiff)); %two thirds of the log
% difference of the data

matrix_logdiff_nsample = matrix_logdiff(1:nsample,:);
[RAW, REW] = mcd(matrix_logdiff, 'bdp', 0.5);
% mean_vec = mean(matrix_logdiff_nsample); %finding the mean.
% 
% mean_vec = mean_vec';
% 
% cov_matrix = cov(matrix_logdiff_nsample); %the covariance
 mean_vec= REW.loc;
 cov_matrix=REW.cov;
 dt=1;

%GBM parameter estimate
capbhat = sqrtm(cov_matrix./dt);    

ahat_vec  = (mean_vec/dt)+ diag((capbhat).^2)/2;

s0_vector =[nstock_val(nsample,1) nstock_val(nsample,2) nstock_val(nsample,3) nstock_val(nsample,4) nstock_val(nsample,5)];

s0_vector = s0_vector';
chi2quantile = chi2inv(0.99,p);
plot(REW.md,'b.')
 line([0 3.5e+04],[chi2quantile, chi2quantile], 'color', 'r')
 xlabel('observation indices')
 ylabel('square Mahalanobis Distance')
 

[Arthur Roué]

In your case, it should be

sum(REW.md > chi2quantile)

[Afua Amoako Dadey]

Thanks so much mr Arthur .. Please can i ask you a different question

[Arthur Roué]

Go for it !

[Afua Amoako Dadey]

The thing is im working on this stock prices and im suppose to write a code to read only stocks(close prices) from the different LSEs whose index are not more than 60 seconds

%Close prices in matrix 

nstock_val=[Close(1:30928,1) Close1(1:30928,1) Close2(1:30928,1) Close3(1:30928,1) Close4(1:30928,1)]; % reading the data
t = (Gmttime); % reading the data
time = datetime(t,'inputformat','dd.MM.yyyy HH:mm:ss.SSS');

LSE_matrix =log(nstock_val); %log of the datanstock_val=[Close(1:30928,1) Close1(1:30928,1) Close2(1:30928,1) Close3(1:30928,1) Close4(1:30928,1)]; % reading the data
I=1:(size(LSE_matrix,1)-1); % selecting the indices of all prices but the last time  when stock was opened

% data = time(:,1);
% id = [false; diff(time(:,1))> seconds(60)];%finds all row indices where the previous row
% %differs by more than 60 seconds
% data(id,:) = [];% deletes all data in those rows.
% time(id) = [];
I= I(diff(time(:,1)) <= seconds(60));

dLSE_col1 = LSE_matrix(I+1,1) - LSE_matrix(I,1);%  log difference

dLSE_col2 = LSE_matrix(I+1,2) - LSE_matrix(I,2);

dLSE_col3 = LSE_matrix(I+1,3) - LSE_matrix(I,3);

dLSE_col4 = LSE_matrix(I+1,4) - LSE_matrix(I,4);

dLSE_col5 = LSE_matrix(I+1,5) - LSE_matrix(I,5);
matrix_logdiff=[dLSE_col1 dLSE_col2 dLSE_col3 dLSE_col4 dLSE_col5];

nsample = floor((2/3)* length(matrix_logdiff)); %two thirds of the log
% difference of the data

 matrix_logdiff_nsample = matrix_logdiff(1:nsample,:);
 mean_vec = mean(matrix_logdiff_nsample); %finding the mean.
% 
 mean_vec = mean_vec';
% 
 cov_matrix = cov(matrix_logdiff_nsample); %the covariance

 dt=1;

%GBM parameter estimate
capbhat = sqrtm(cov_matrix./dt);    

ahat_vec  = (mean_vec/dt)+ diag((capbhat).^2)/2;

s0_vector =[nstock_val(nsample,1) nstock_val(nsample,2) nstock_val(nsample,3) nstock_val(nsample,4) nstock_val(nsample,5)];

s0_vector = s0_vector';

[Arthur Roué]

I require some time to analyze your issue, can you create another MATLAB question ? This way, maybe me or another user will help you

[Afua Amoako Dadey]

okay Thanks