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convert2daily

Aggregate timetable data to daily periodicity

Description

example

TT2 = convert2daily(TT1) aggregates data (for example, high-frequency, and intra-day) to a daily periodicity.

example

TT2 = convert2daily(___,Name,Value) specifies options using one or more optional name-value pair arguments in addition to the input argument in the previous syntax.

Examples

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Apply separate aggregation methods to related variables in a timetable while maintaining consistency between aggregated results for a daily periodicity.

Load a timetable (TT) of simulated stock price data and corresponding logarithmic returns. The data stored in TT is recorded at various times throughout the day on New York Stock Exchange (NYSE) business days from January 1, 2018, to December 31, 2020. The timetable TT also includes NYSE business calendar awareness. If your timetable does not account for nonbusiness days (weekends, holidays, and market closures), add business calendar awareness by using addBusinessCalendar first.

load('SimulatedStock.mat','TT');
head(TT)
ans=8×2 timetable
            Time            Price     Log_Return
    ____________________    ______    __________

    02-Jan-2018 11:52:11    100.71     0.0070749
    02-Jan-2018 13:23:09    103.11      0.023551
    02-Jan-2018 14:45:30    100.24     -0.028229
    02-Jan-2018 15:30:48    101.37       0.01121
    03-Jan-2018 10:02:21    101.81     0.0043311
    03-Jan-2018 11:22:37    100.17      -0.01624
    03-Jan-2018 14:45:20     99.66    -0.0051043
    03-Jan-2018 14:55:39    100.12     0.0046051

Aggregate prices and logarithmic returns to a daily periodicity. To maintain consistency between prices and returns, for any given trading day, aggregate the prices by reporting the last recorded price by using "lastvalue" and aggregate the returns by summing all logarithmic returns by using "sum".

tt = convert2daily(TT,'Aggregation',["lastvalue" "sum"]);
head(tt)
ans=8×2 timetable
       Time        Price     Log_Return
    ___________    ______    __________

    02-Jan-2018    101.37     0.013607 
    03-Jan-2018    100.12    -0.012408 
    04-Jan-2018    106.76     0.064214 
    05-Jan-2018    112.78     0.054856 
    08-Jan-2018    119.07     0.054273 
    09-Jan-2018    119.46      0.00327 
    10-Jan-2018    124.44     0.040842 
    11-Jan-2018    125.63    0.0095174 

To verify consistency, examine the input and output timetables for January 2 and 3, 2018.

TT(1:8,:)  % Input data for 02-Jan-2018 and 03-Jan-2018
ans=8×2 timetable
            Time            Price     Log_Return
    ____________________    ______    __________

    02-Jan-2018 11:52:11    100.71     0.0070749
    02-Jan-2018 13:23:09    103.11      0.023551
    02-Jan-2018 14:45:30    100.24     -0.028229
    02-Jan-2018 15:30:48    101.37       0.01121
    03-Jan-2018 10:02:21    101.81     0.0043311
    03-Jan-2018 11:22:37    100.17      -0.01624
    03-Jan-2018 14:45:20     99.66    -0.0051043
    03-Jan-2018 14:55:39    100.12     0.0046051

tt(1:2,:)  % Return aggregated results
ans=2×2 timetable
       Time        Price     Log_Return
    ___________    ______    __________

    02-Jan-2018    101.37     0.013607 
    03-Jan-2018    100.12    -0.012408 

For each business day in TT, notice that the output aggregated price is the last price of the day and that the aggregated return is the sum of all logarithmic returns. Also, the aggregated returns are consistent with aggregated prices.

For example, the aggregated return for January 3, 2018, is -0.012408, which is the logarithmic return associated with the last prices recorded on January 2 and 3, 2018 (that is, -0.012408 = log(100.12) - log(101.37)).

The dates of the aggregated results are whole dates that indicate the dates for which aggregated results are reported.

Input Arguments

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Data to aggregate to a daily periodicity, specified as a timetable.

Note

NaNs indicate missing values, Timestamps must be in ascending or descending order.

By default, all days are business days. If your timetable does not account for nonbusiness days (weekends, holidays, and market closures), add business calendar awareness by using addBusinessCalendar first. For example, the following command adds business calendar logic to include only NYSE business days.

TT = addBusinessCalendar(TT);

Data Types: timetable

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: TT2 = convert2daily(TT1,'Aggregation',["lastvalue" "sum"])

Aggregation method for TT1 data for intra-day aggregation, specified as the comma-separated pair consisting of 'Aggregation' and a character vector, string, or function handle applied to all time series in TT1, or a cell vector of character vectors, string vector, or cell vector of function handles the same length as the number of variables in TT1.

The aggregation methods define how data is aggregated over business days in an intra-day periodicity. Available aggregation methods are:

  • 'sum' — Sum the values in each day.

  • 'mean' — Calculate the mean of the values in each day.

  • 'prod' — Calculate the product of the values in each day.

  • 'min' — Calculate the minimum of the values in each day.

  • 'max' — Calculate the maximum of the values in each day.

  • 'firstvalue' — Use the first value in each day.

  • 'lastvalue' — Use the last value in each day.

All methods listed above omit missing data (NaNs) in direct aggregation calculations. However, in situations in which missing values appear in the first row of TT1, missing values can also appear in the aggregated results TT2.

Additionally, you can specify aggregation methods as function handles. To include missing data, specify functions as function handles that include the missing data when aggregating data. Aggregation functions must accept the underlying data stored in TT1 and return an output that is a scalar or a row vector, and must accept empty inputs. Each aggregation function is applied to the corresponding variable and called one at a time. Each variable must contain either a single numeric vector or numeric matrix. For example, consider a daily timetable representing TT1 with three variables.

              Time             AAA       BBB            CCC       
      ____________________    ______    ______    ________________
      01-Jan-2018 09:45:47    100.00    200.00    300.00    400.00
      01-Jan-2018 12:48:09    100.03    200.06    300.09    400.12
      02-Jan-2018 10:27:32    100.07    200.14    300.21    400.28
      02-Jan-2018 12:46:09    100.08    200.16    300.24    400.32
      02-Jan-2018 14:14:13    100.25    200.50    300.75    401.00
      02-Jan-2018 15:52:31    100.19    200.38    300.57    400.76
      03-Jan-2018 09:47:11    100.54    201.08    301.62    402.16
      03-Jan-2018 11:24:23    100.59    201.18    301.77    402.36
      03-Jan-2018 14:41:17    101.40    202.80    304.20    405.60
      03-Jan-2018 16:00:00    101.94    203.88    305.82    407.76
      04-Jan-2018 09:55:51    102.53    205.06    307.59    410.12
      04-Jan-2018 10:07:12    103.35    206.70    310.05    413.40
      04-Jan-2018 14:26:23    103.40    206.80    310.20    413.60
      05-Jan-2018 13:13:12    103.91    207.82    311.73    415.64
      05-Jan-2018 14:57:53    103.89    207.78    311.67    415.56

The corresponding default daily results representing TT2 (where the 'lastvalue' is reported for each day) are as follows.

        Time         AAA       BBB            CCC       
      ___________    ______    ______    ________________
      01-Jan-2018    100.03    200.06    300.09    400.12
      02-Jan-2018    100.19    200.38    300.57    400.76
      03-Jan-2018    101.94    203.88    305.82    407.76
      04-Jan-2018    103.40    206.80    310.20    413.60
      05-Jan-2018    103.89    207.78    311.67    415.56

Data Types: char | string | cell | function_handle

Output Arguments

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Daily data, returned as a timetable. The function returns NaNs for variables in TT2 for business days when no data is recorded for those variables in TT1. If TT1 is in ascending order, so too is TT2, and if TT1 is in descending order, so too is TT2.

The first date in TT2 is the first business date on or after the first date in TT1. The last date in TT2 is the last business date on or before the last date in TT1.

Introduced in R2021a