How do you count points above the horizontal line?

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Afua Amoako Dadey 2020-7-20

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https://ww2.mathworks.cn/matlabcentral/answers/567828-how-do-you-count-points-above-the-horizontal-line

评论： Afua Amoako Dadey 2020-7-20

采纳的回答： Arthur Roué

1a.jpg

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

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Answer 1

Arthur Roué 2020-7-20

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https://ww2.mathworks.cn/matlabcentral/answers/567828-how-do-you-count-points-above-the-horizontal-line#answer_468267

在 MATLAB Online 中打开

% Data array
Data = [....]
% Treshold
Limit = 0;
% Count
AboveLimit = sum(Data > Limit)

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Afua Amoako Dadey 2020-7-20

在 MATLAB Online 中打开

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')
 

Afua Amoako Dadey 2020-7-20

在 MATLAB Online 中打开

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 2020-7-20

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 2020-7-20

okay Thanks

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How do you count points above the horizontal line?

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How do you count points above the horizontal line?

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