MATLAB: For an existing financial time-series object, is there a simple way to add columns

MATLABmatlab financial time series add new column pandas

I'm referring to pandas DataFrame as an example of adding a new ('return') column:

spy['return'] = spy['close'].pct_change()

What's the equivalent in matlab for the line above?

thx

Best Answer

digging a bit more, it seems fts behave a lot like structures ... so:

>> class(dt)

ans =

fints

>> dt.MA10 = fts2mat(tsmovavg(dt.Close, 's', 10))

dt =

    desc:  (none)
    freq:  Daily (1)
    'dates:  (2711)'    'Open:  (2711)'    'High:  (2711)'    'Low:  (2711)'    'Close:  (2711)'    'Vol:  (2711)'    'OI:  (2711)'    'MA10:  (2711)'
    '20-Mar-2006'       [       138.63]    [       139.44]    [       138.4]    [        139.31]    [       11992]    [     219881]    [          NaN]
    '21-Mar-2006'       [       140.59]    [       140.73]    [      139.85]    [        140.24]    [       15357]    [     214085]    [          NaN]

will just add a new column ... or:

col_name = 'ma_20'

dt.(col_name) = fts2mat(tsmovavg(dt.Close, 's', 20))

dt =

    desc:  (none)
    freq:  Daily (1)
    'dates:  (2711)'    'Open:  (2711)'    'High:  (2711)'    'Low:  (2711)'    'Close:  (2711)'    'Vol:  (2711)'    'OI:  (2711)'    'MA10:  (2711)'    'ma_20:  (2711)'
    '20-Mar-2006'       [       138.63]    [       139.44]    [       138.4]    [        139.31]    [       11992]    [     219881]    [          NaN]    [           NaN]
    '21-Mar-2006'       [       140.59]    [       140.73]    [      139.85]    [        140.24]    [       15357]    [     214085]    [          NaN]    [           NaN]
    '22-Mar-2006'       [       140.99]    [       141.33]    [      140.71]    [        140.87]    [        8884]    [     204050]    [          NaN]    [           NaN]

Simple after all...

Related Solutions

MATLAB: Problems with the vector output from FOR as a plot3 argument

The cell arrays don’t make sense in this context. Just use regular matrix notation.

This works for me (I added grid on so the plot has a context):

H=6.31;
K=104;
M=2208.5;
X=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999,1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129,1130,1131,1132,1133,1134,1135,1136,1137,1138,1139,1140,1141,1142,1143,1144,1145,1146,1147,1148,1149,1150,1151,1152,1153,1154,1155,1156,1157,1158,1159,1160,1161,1162,1163,1164,1165,1166,1167,1168,1169,1170,1171,1172,1173,1174,1175,1176,1177,1178,1179,1180,1181,1182,1183,1184,1185,1186,1187,1188,1189,1190,1191,1192,1193,1194,1195,1196,1197,1198,1199,1200,1201,1202,1203,1204,1205,1206,1207,1208,1209,1210,1211,1212,1213,1214,1215,1216,1217,1218,1219,1220,12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1086,11082,11078,11074,11070,11066,11062,11058,11054,11050,11046,11042,11038,11034,11030,11026,11022,11018,11014,11010,11006,11002,10998,10994,10990,10986,10982,10978,10974,10970,10966,10962,10958,10954,10950,10946,10942,10938,10934,10930,10926,10922,10918,10914,10910,10906,10902,10898,10894,10890,10886,10882,10878,10874,10870,10866,10862,10858,10854,10850,10846,10842,10838,10834,10830,10826,10822,10818,10814,10810,10806,10802,10798,10794,10790,10786,10782,10778,10774,10770,10766,10762,10758,10754,10750,10747,10744,10741,10738,10735,10732,10729,10726,10723,10720,10717,10714,10711,10708,10705,10702,10699,10696,10693,10690,10687,10684,10681,10678,10675,10672,10669,10666,10663,10660,10657,10654,10651,10648,10645,10642,10639,10636,10633,10630,10627,10624,10621,10618,10615,10612,10609,10606,10603,10600,10597,10594,10591,10588,10585,10582,10579,10576,10573,10570,10567,10564,10561,10558,10555,10552,10549,10546,10543,10540,10537,10534,10531,10528,10525,10522,10519,10516,10513,10510,10507,10504,10501,10498,10495,10492,10489,10486,10483,10480,10477,10474,10471,10468,10465,10462,10459,10456,10453,10450,10447,10444,10441,10438,10435,10432,10429,10426,10423,10420,10417,10414,10411,10408,10405,10402,10399,10396,10393,10390,10387,10384,10381,10378,10375,10372,10369,10366,10363,10360,10357,10354,10351,10348,10345,10342,10339,10336,10333,10330,10327,10324,10321,10318,10315,10312,10309,10306,10303,10300,10297,10294,10291,10288,10285,10282,10279,10276,10273,10270,10267,10264,10261,10258,10255,10252,10249,10246,10243,10240,10237,10234,10231,10228,10225,10222,10219,10216,10213,10210,10207,10204,10201,10198,10195,10192,10189,10186,10183,10180,10177,10174,10171,10168,10165,10162,10159,10156,10153,10150,10147,10144,10141,10138,10135,10132,10129,10126,10123,10120,10117,10114,10111,10108,10105,10102,10099,10096,10093,10090,10087,10084,10081,10078,10075,10072,10069,10066,10063,10060,10057,10054,10051,10048,10045,10042,10039,10036,10033,10030,10027,10024,10021,10018,10015,10012,10009,10006,10003,10000];
Z=1:1:2001;
for n=1:1:2001
     if(n+K>2000)
         Z(n)=0;
     else
         if(Y(n+K)/10>=M)
         Z(n)=0;
         else
             Z(n)=floor(((Y(n+K))*H)/(M-(Y(n+K))/10));
         end
     end
%     fprintf('%d\n',Z(n))
end
figure
plot3(X,Y,Z)
grid on

MATLAB: Financial time series object – how to delete a column

It's essentially just a structure so rmfield is implemented as is for a struct object. Ain't the easiest thing to find that out in the documentation, but if you go to the root page and then the 'Functions' link you'll find it...

You can, of course, address the column as

dt.times=[];

to remove the data but there's still an empty field that way.

ADDENDUM

Per the doc, it appears the only way to not have the times series is to pass only a date vector containing no time values...with a series of integer datenum, the first example for fints shows only a dates series.

NB: however, if the data are lacking time information and there are multiple elements for a given day, the internal logic will remove all except the first instance of each day from the resultant time series created--iow, a time series by definition can contain only a single observation at a given time.

So the answer to the question is "you can't" but you can avoid creating the times series initially (in which case you won't have any need to remove it. :) )

Best Answer

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