# Use Timetables in Finance

Use timetables to visualize and calculate weekly statistics from simulated daily stock data.

The data for this example is in the MAT-file SimulatedStock.mat, which loads the following:

• Dates corresponding to the closing stock prices, TMW_DATES

• Opening stock prices, TMW_OPEN

• Daily high of stock prices, TMW_HIGH

• Daily low of stock prices, TMW_LOW

• Closing stock prices, TMW_CLOSE, TMW_CLOSE_MISSING

• Daily volume of traded, TMW_VOLUME

• Data in a table, TMW_TB

Step 2. Create timetables.

In timetables, you can work with financial time series rather than with vectors. When using a timetable, you can easily track the dates. You can manipulate the data series based on the dates, because a timetable object tracks the administration of a time series.

Use the MATLAB® timetable function to create a timetable object. Alternatively, you can use the MATLAB conversion function table2timetable to convert a table to a timetable. In this example, the timetable TMW_TT is constructed from a table and is only for illustration purposes. After you create a timetable object, you can use the Description field of the timetable object to store meta-information about the timetable.

% Create a timetable from vector input
TMW = timetable(TMW_OPEN,TMW_HIGH,TMW_LOW,TMW_CLOSE_MISSING,TMW_VOLUME, ...
'VariableNames',{'Open','High','Low','Close','Volume'},'RowTimes',TMW_DATES);

% Convert from a table to a timetable
TMW_TT = table2timetable(TMW_TB,'RowTimes',TMW_DATES);

TMW.Properties.Description = 'Simulated stock data.';

TMW.Properties
ans =
TimetableProperties with properties:

Description: 'Simulated stock data.'
UserData: []
DimensionNames: {'Time'  'Variables'}
VariableNames: {'Open'  'High'  'Low'  'Close'  'Volume'}
VariableDescriptions: {}
VariableUnits: {}
VariableContinuity: []
RowTimes: [1000x1 datetime]
StartTime: 04-Sep-2012
SampleRate: NaN
TimeStep: NaN
Events: []
CustomProperties: No custom properties are set.
Use addprop and rmprop to modify CustomProperties.

Step 3. Calculate basic data statistics, and fill the missing data.

Use the MATLAB summary function to view basic statistics of the timetable data. By reviewing the summary for each variable, you can identify missing values. You can then use the MATLAB fillmissing function to fill in missing data in a timetable by specifying a fill method.

summaryTMW = summary(TMW);
summaryTMW.Close
ans = struct with fields:
Size: [1000 1]
Type: 'double'
Description: ''
Units: ''
Continuity: []
Min: 83.4200
Median: 116.7500
Max: 162.1100
NumMissing: 3

TMW = fillmissing(TMW,'linear');
summaryTMW = summary(TMW);
summaryTMW.Close
ans = struct with fields:
Size: [1000 1]
Type: 'double'
Description: ''
Units: ''
Continuity: []
Min: 83.4200
Median: 116.7050
Max: 162.1100
NumMissing: 0

summaryTMW.Time
ans = struct with fields:
Size: [1000 1]
Type: 'datetime'
Min: 04-Sep-2012
Median: 31-Aug-2014
Max: 24-Aug-2016
NumMissing: 0
TimeStep: NaN

Step 4. Visualize the data.

To visualize the timetable data, use financial charting functions such as highlow or movavg. For this example, the moving average information is plotted on the same chart for highlow to provide a complete visualization. To obtain the stock performance in 2014, use the MATLAB timerange function to select rows of the timetable. To visualize a technical indicator such as the Moving Average Convergence Divergence (MACD), pass the timetable object into the macd function for analysis.

index = timerange(datetime('01-Jan-2014','Locale','en_US'),datetime('31-Dec-2014','Locale','en_US'),'closed');

highlow(TMW(index,:));
hold on

ema15 = movavg(TMW(:,'Close'),'exponential',15);
ema25 = movavg(TMW(:,'Close'),'exponential',25);

ema15 = ema15(index,:);
ema25 = ema25(index,:);
plot(ema15.Time,ema15.Close,'r');
plot(ema25.Time,ema25.Close,'g');
hold off

legend('Price','15-Day EMA','25-Day EMA')
title('Highlow Plot for TMW')

[macdLine, signalLine] = macd(TMW(:,'Close'));

plot(macdLine.Time,macdLine.Close);
hold on
plot(signalLine.Time,signalLine.Close);
hold off

title('MACD for TMW')
legend('MACD Line', 'Signal Line')

Step 5. Create a weekly return and volatility series.

To calculate weekly return from the daily stock prices, you must resample the data frequency from daily to weekly. When working with timetables, use the MATLAB functions retime or synchronize with various aggregation methods to calculate weekly statistics. To adjust the timetable data to a time-vector basis, use retime and use synchronize with multiple timetables.

weeklyOpen = retime(TMW(:,'Open'),'weekly','firstvalue');
weeklyHigh = retime(TMW(:,'High'),'weekly','max');
weeklyLow = retime(TMW(:,'Low'),'weekly','min');
weeklyClose = retime(TMW(:,'Close'),'weekly','lastvalue');
weeklyTMW = [weeklyOpen,weeklyHigh,weeklyLow,weeklyClose];

weeklyTMW = synchronize(weeklyTMW,TMW(:,'Volume'),'weekly','sum');
Time         Open      High      Low      Close       Volume
___________    ______    ______    ______    ______    __________

02-Sep-2012       100    102.38     98.45     99.51    2.7279e+07
09-Sep-2012     99.72    101.55     96.52     97.52    2.8518e+07
16-Sep-2012     97.35     97.52      92.6     93.73    2.9151e+07
23-Sep-2012     93.55     98.03     92.25     97.35     3.179e+07
30-Sep-2012      97.3    103.15     96.68     99.66    3.3761e+07
07-Oct-2012     99.76    106.61      98.7    104.23    3.1299e+07
14-Oct-2012    104.54    109.75    100.55    103.77    3.1534e+07
21-Oct-2012    103.84    104.32     96.95     97.41    3.1706e+07

To perform calculations on entries in a timetable, use the MATLAB rowfun function to apply a function to each row of a weekly frequency timetable.

returnFunc = @(open,high,low,close,volume) log(close) - log(open);
weeklyReturn = rowfun(returnFunc,weeklyTMW,'OutputVariableNames',{'Return'});

weeklyStd = retime(TMW(:,'Close'),'weekly',@std);
weeklyStd.Properties.VariableNames{'Close'} = 'Volatility';

weeklyTMW = [weeklyReturn,weeklyStd,weeklyTMW]
weeklyTMW=208×7 timetable
Time          Return       Volatility     Open      High      Low      Close       Volume
___________    ___________    __________    ______    ______    ______    ______    __________

02-Sep-2012      -0.004912     0.59386         100    102.38     98.45     99.51    2.7279e+07
09-Sep-2012      -0.022309     0.63563       99.72    101.55     96.52     97.52    2.8518e+07
16-Sep-2012      -0.037894     0.93927       97.35     97.52      92.6     93.73    2.9151e+07
23-Sep-2012       0.039817      2.0156       93.55     98.03     92.25     97.35     3.179e+07
30-Sep-2012       0.023965      1.1014        97.3    103.15     96.68     99.66    3.3761e+07
07-Oct-2012       0.043833      1.3114       99.76    106.61      98.7    104.23    3.1299e+07
14-Oct-2012     -0.0073929      1.8097      104.54    109.75    100.55    103.77    3.1534e+07
21-Oct-2012      -0.063922      2.1603      103.84    104.32     96.95     97.41    3.1706e+07
28-Oct-2012      -0.028309      0.9815       97.45      99.1     92.58     94.73    1.9866e+07
04-Nov-2012    -0.00010566       1.224       94.65      96.1     90.82     94.64    3.5043e+07
11-Nov-2012       0.077244      2.4854       94.39    103.98     93.84    101.97    3.0624e+07
18-Nov-2012       0.022823     0.55896      102.23    105.27    101.24    104.59    2.5803e+07
25-Nov-2012      -0.012789       1.337      104.66    106.02    100.85    103.33    3.1402e+07
02-Dec-2012      -0.043801      0.2783      103.37    103.37     97.69     98.94    3.2136e+07
09-Dec-2012      -0.063475      1.9826       99.02     99.09     91.34     92.93    3.4447e+07
16-Dec-2012      0.0025787      1.2789       92.95      94.2     88.58     93.19    3.3247e+07
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