hello everyone please can someone help me with stock price prediction. That is I wrote this code and from the I vector , I want a code that will remove indexes from the I vector more than 60 seconds. Thanks

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Afua Amoako Dadey 2020-7-6

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评论： Afua Amoako Dadey 2020-7-9

 
LSE_matrix =log(nstock_val); %log of the data
I=1:(size(LSE_matrix,1)-1); % selecting the indices of all prices but the last time  when stock was opened
dLSE_col1 = LSE_matrix(I+1,1) - LSE_matrix(I,1);%  log difference

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Afua Amoako Dadey 2020-7-6

Atttached is of picture the corresponding times. So say we consider 498 and 498. its more than 60 sec. how do i write a code to remove such an index. Thanks for the help.

jonas 2020-7-6

Dont you think it would be easier if you upload a sample of the data?

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Answer 1

jonas 2020-7-6

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编辑：jonas 2020-7-6

在 MATLAB Online 中打开

You can adapt this to your needs

A = readtable('LSE1.csv')
t = datetime(A{:,1},'inputformat','dd.MM.yyyy HH:mm:ss.SSS')
data = A{:,2:end};
id = [false;diff(t)>seconds(60)];
data(id,:) = [];
t(id) = [];

If the period between t(n) and t(n+1) is longer than 60s, then the data recorded at t(n+1) is deleted.

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Afua Amoako Dadey 2020-7-9

编辑：Afua Amoako Dadey 2020-7-9

在 MATLAB Online 中打开

Thanks ... but can i show you one more thing.

%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(1:30928,1) Gmttime1(1:30928,1) Gmttime2(1:30928,1)  Gmttime3(1:30928,1) Gmttime4(1:30928,1)]; % 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:end);
 id = [false;diff(time(:,1))> seconds(60)]
 data(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,:);
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';

jonas 2020-7-9

编辑：jonas 2020-7-9

在 MATLAB Online 中打开

I don't understand exactly what the problem is, but I'm not a big fan of how you structure the data. I would put all my .csv files in a separate folder and read them as follows:

addpath('C:\files\') % change this to the correct path
files = dir('C:\files\*.csv'); % again..
for i = 1:numel(files)
    opts = detectImportOptions(files(i).name);
    opts.VariableTypes{1} = 'datetime';
    opts = setvaropts(opts,'GmtTime','inputformat','dd.MM.yyyy HH:mm:ss.SSS');
    A{i} = readtimetable(files(i).name,opts);
end

You will end up with 5 tables stored in cell array A. You can then append them to a single table or loop over each tables, no need to work with "Var1, Var2, Var3..."

Afua Amoako Dadey 2020-7-9