Basically you want to perform a full outer merge on two datasets based on the date as key.
Consider the following as example:
%# vector of dates (serial datetime)
days = datenum( num2str((1:31)','2011-10-%02d') ); %'# one month (October 2011)
%# lets build two datasets similar to what you described
idx1 = rand(size(days)) > 0.2; %# randomly pick dates for 1st
M1 = [days(idx1) rand(sum(idx1),2)*1000]; %# sotcks: days,opening,closing
idx2 = rand(size(days)) > 0.5; %# randomly pick dates for 2nd
M2 = [days(idx2) rand(sum(idx2),2)*1000]; %# bonds: days,opening,closing
%# get the full range of dates, and convert them to indices starting at 1
[allDays,~,ind] = unique( [M1(:,1);M2(:,1)] );
indM1 = ind(1:size(M1,1));
indM2 = ind(size(M1,1)+1:end);
%# merge the two datasets (days,opening,closing,opening,closing)
M = nan(numel(allDays),size(M1,2)+size(M2,2)-1);
M(:,1) = allDays; %# available days from both
M(indM1,2:3) = M1(:,2:3); %# insert 1st dataset values
M(indM2,4:5) = M2(:,2:3); %# insert 2nd dataset values
%# final merged dataset formatted
C = [cellstr(datestr(M(:,1),'yyyy-mm-dd')) num2cell(M(:,2:end))]
The result:
C =
'2011-10-01' [ NaN] [ NaN] [332.5714] [241.5017]
'2011-10-03' [941.9189] [ 86.8151] [ NaN] [ NaN]
'2011-10-04' [655.9138] [429.3973] [ NaN] [ NaN]
'2011-10-05' [451.9457] [257.2828] [853.0636] [243.1452]
'2011-10-06' [839.6974] [297.5554] [ NaN] [ NaN]
'2011-10-07' [532.6235] [424.8584] [ NaN] [ NaN]
'2011-10-09' [553.8871] [119.2073] [ NaN] [ NaN]
'2011-10-11' [680.0655] [495.0669] [442.3979] [154.1594]
'2011-10-13' [367.1899] [706.4072] [904.3555] [956.4164]
'2011-10-14' [ NaN] [ NaN] [ 33.1794] [935.6614]
'2011-10-15' [239.2906] [243.5734] [ NaN] [ NaN]
'2011-10-16' [578.9235] [785.0701] [532.4265] [818.7144]
'2011-10-17' [866.8871] [ 74.0896] [716.4973] [728.2618]
'2011-10-18' [406.7768] [393.8834] [179.3018] [175.8117]
'2011-10-19' [112.6151] [ 3.3941] [336.5329] [360.3710]
'2011-10-20' [443.8458] [220.6769] [ NaN] [ NaN]
'2011-10-21' [ NaN] [ NaN] [187.7129] [188.7900]
'2011-10-22' [300.1844] [ 1.3006] [ NaN] [ NaN]
'2011-10-23' [401.3869] [189.1797] [ NaN] [ NaN]
'2011-10-24' [833.3636] [142.4841] [321.9272] [ 1.1984]
'2011-10-25' [ NaN] [ NaN] [403.8567] [316.4195]
'2011-10-26' [403.6287] [268.0760] [ NaN] [ NaN]
'2011-10-27' [390.1759] [174.8921] [ NaN] [ NaN]
'2011-10-28' [ NaN] [ NaN] [548.5663] [699.6170]
'2011-10-29' [360.4489] [138.6490] [ 48.7386] [625.2552]
'2011-10-30' [140.2554] [598.8856] [552.7321] [543.0622]
'2011-10-31' [260.1302] [901.0579] [274.8114] [439.0372]
The merged result contains opening/closing prices from both datasets. When one of them is not available on a specific date, it is replaced by NaN. Note how there are some unrepresented days in the result, this is because both datasets did not list prices on those days.
Alternatively, you could look into the dataset class from the Statistics Toolbox (which is designed for such cases). Using the same example:
%# build dataset object for the two sets
varNames1 = {'days' 'stock_open' 'stock_close'};
varNames2 = {'days' 'bond_open' 'bond_close'};
d1 = dataset([M1, varNames1]);
d2 = dataset([M2, varNames2]);
%# join on days (full-outer join)
d = join(d1,d2, 'keys','days', 'type','fullouter', 'MergeKeys',true);
d.days = datestr(d.days,'yyyy-mm-dd'); %# format the days column as string
The result:
d =
days stock_open stock_close bond_open bond_close
2011-10-01 NaN NaN 332.57 241.5
2011-10-03 941.92 86.815 NaN NaN
2011-10-04 655.91 429.4 NaN NaN
2011-10-05 451.95 257.28 853.06 243.15
2011-10-06 839.7 297.56 NaN NaN
2011-10-07 532.62 424.86 NaN NaN
2011-10-09 553.89 119.21 NaN NaN
2011-10-11 680.07 495.07 442.4 154.16
2011-10-13 367.19 706.41 904.36 956.42
2011-10-14 NaN NaN 33.179 935.66
2011-10-15 239.29 243.57 NaN NaN
2011-10-16 578.92 785.07 532.43 818.71
2011-10-17 866.89 74.09 716.5 728.26
2011-10-18 406.78 393.88 179.3 175.81
2011-10-19 112.62 3.3941 336.53 360.37
2011-10-20 443.85 220.68 NaN NaN
2011-10-21 NaN NaN 187.71 188.79
2011-10-22 300.18 1.3006 NaN NaN
2011-10-23 401.39 189.18 NaN NaN
2011-10-24 833.36 142.48 321.93 1.1984
2011-10-25 NaN NaN 403.86 316.42
2011-10-26 403.63 268.08 NaN NaN
2011-10-27 390.18 174.89 NaN NaN
2011-10-28 NaN NaN 548.57 699.62
2011-10-29 360.45 138.65 48.739 625.26
2011-10-30 140.26 598.89 552.73 543.06
2011-10-31 260.13 901.06 274.81 439.04
EDIT:
Say you had the following two files containing the data:
bonds.csv
10/6/1977 7.72 7.72
10/7/1977 7.73 7.73
10/11/1977 7.77 7.77
10/12/1977 7.79 7.79
10/13/1977 7.79 7.79
10/14/1977 7.79 7.79
10/17/1977 7.79 7.79
10/18/1977 7.8 7.8
stocks.csv
10/06/77 95.68 96.05
10/07/77 96.05 95.97
10/10/77 95.97 95.75
10/11/77 95.75 94.93
10/12/77 94.82 94.04
10/13/77 94.04 93.46
10/14/77 93.46 93.56
10/17/77 93.56 93.47
You can read the data using the TEXTSCAN function:
%# read bonds data
fid = fopen('bonds.csv','rt');
C = textscan(fid, '%s %f %f', 'Delimiter',' ', 'CollectOutput',true);
fclose(fid);
M1 = [datenum(C{1},'mm/dd/yyyy') C{2}];
%# read stocks data
fid = fopen('stocks.csv','rt');
C = textscan(fid, '%s %f %f', 'Delimiter',' ', 'CollectOutput',true);
fclose(fid);
M2 = [datenum(C{1},'mm/dd/yy') C{2}];
Now you can use the same code above (starting at "get the full range of dates...", or use the DATASET class). After joining them, this gives me:
C =
'1977-10-06' [7.72] [7.72] [95.68] [96.05]
'1977-10-07' [7.73] [7.73] [96.05] [95.97]
'1977-10-10' [ NaN] [ NaN] [95.97] [95.75]
'1977-10-11' [7.77] [7.77] [95.75] [94.93]
'1977-10-12' [7.79] [7.79] [94.82] [94.04]
'1977-10-13' [7.79] [7.79] [94.04] [93.46]
'1977-10-14' [7.79] [7.79] [93.46] [93.56]
'1977-10-17' [7.79] [7.79] [93.56] [93.47]
'1977-10-18' [ 7.8] [ 7.8] [ NaN] [ NaN]
