convert2monthly
Description
Examples
Aggregate Timetable Data to Monthly Periodicity
Apply separate aggregation methods to related variables in a timetable
while maintaining consistency between aggregated results when converting to a monthly periodicity. You can use convert2monthly
to aggregate both intra-daily data and aggregated daily data. These methods result in equivalent monthly aggregates. Lastly, you can aggregate results on a specific day of each month (for example, the 15th), rather than the default end of the month.
Load a timetable (DataTimeTable
) of simulated stock price data and corresponding logarithmic returns. The data stored in DataTimeTable
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 DataTimeTable
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("SimulatedStockSeries.mat","DataTimeTable"); head(DataTimeTable)
Time Price Log_Return ____________________ ______ __________ 01-Jan-2018 11:52:48 100 -0.025375 01-Jan-2018 13:23:13 101.14 0.011336 01-Jan-2018 14:45:09 101.5 0.0035531 01-Jan-2018 15:30:30 100.15 -0.01339 02-Jan-2018 10:43:37 99.72 -0.0043028 03-Jan-2018 10:02:21 100.11 0.0039033 03-Jan-2018 11:22:37 103.96 0.037737 03-Jan-2018 13:42:27 107.05 0.02929
First aggregate intra-daily prices and returns to daily periodicity. To maintain consistency between prices and returns, for any given trading day aggregate prices by reporting the last recorded price using "lastvalue"
and aggregate returns by summing all logarithmic returns using "sum"
.
DTTDaily = convert2daily(DataTimeTable,Aggregation=["lastvalue" "sum"]); head(DTTDaily)
Time Price Log_Return ___________ ______ __________ 01-Jan-2018 100.15 -0.023876 02-Jan-2018 99.72 -0.0043028 03-Jan-2018 105.57 0.057008 04-Jan-2018 109.01 0.032065 05-Jan-2018 110.69 0.015294 06-Jan-2018 110.48 -0.001899 07-Jan-2018 113.83 0.029872 08-Jan-2018 116.41 0.022412
Use convert2monthly
to aggregate the data to a monthly periodicity and compare the results of two different approaches. The first approach computes monthly results by aggregating the daily aggregates and the second approach computes monthly results by directly aggregating the original intra-daily data. Note that although convert2monthly
reports results on the last business day of each month by default, you can report monthly results on the 15th of each month by using the optional name-value pair argument 'EndOfMonthDay'
.
DTTMonthly1 = convert2monthly(DTTDaily,Aggregation=["lastvalue" "sum"], ... EndOfMonthDay=15); % Daily to monthly DTTMonthly2 = convert2monthly(DataTimeTable,Aggregation=["lastvalue" "sum"], ... EndOfMonthDay=15); % Intra-daily to monthly head(DTTMonthly1)
Time Price Log_Return ___________ ______ __________ 15-Jan-2018 114.72 0.11195 15-Feb-2018 117.27 0.021985 15-Mar-2018 105.3 -0.10767 15-Apr-2018 106.4 0.010392 15-May-2018 100.29 -0.05914 15-Jun-2018 98.72 -0.015778 15-Jul-2018 103.18 0.044187 15-Aug-2018 108.07 0.046304
head(DTTMonthly2)
Time Price Log_Return ___________ ______ __________ 15-Jan-2018 114.72 0.11195 15-Feb-2018 117.27 0.021985 15-Mar-2018 105.3 -0.10767 15-Apr-2018 106.4 0.010392 15-May-2018 100.29 -0.05914 15-Jun-2018 98.72 -0.015778 15-Jul-2018 103.18 0.044187 15-Aug-2018 108.07 0.046304
Notice that the results of the two approaches are the same. For months in which the 15th is not an NYSE trading day, the function reports results on the previous business day.
Use Custom Aggregation Method to Convert Timetable Daily Data to Monthly Periodicity
You can apply custom aggregation methods using function handles. Specify a function handle to aggregate related variables in a timetable
while maintaining consistency between aggregated results when converting from a daily to a monthly periodicity.
Load a timetable (DataTimeTable
) of simulated stock price data and corresponding logarithmic returns. The data stored in DataTimeTable
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 DataTimeTable
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("SimulatedStockSeries.mat","DataTimeTable"); head(DataTimeTable)
Time Price Log_Return ____________________ ______ __________ 01-Jan-2018 11:52:48 100 -0.025375 01-Jan-2018 13:23:13 101.14 0.011336 01-Jan-2018 14:45:09 101.5 0.0035531 01-Jan-2018 15:30:30 100.15 -0.01339 02-Jan-2018 10:43:37 99.72 -0.0043028 03-Jan-2018 10:02:21 100.11 0.0039033 03-Jan-2018 11:22:37 103.96 0.037737 03-Jan-2018 13:42:27 107.05 0.02929
First add another variable to DataTimeTable
that contains the simple (proportional) returns associated with the prices in DataTimeTable
and examine the first few rows.
DataTimeTable.Simple_Return = exp(DataTimeTable.Log_Return) - 1; % Log returns to simple returns
head(DataTimeTable)
Time Price Log_Return Simple_Return ____________________ ______ __________ _____________ 01-Jan-2018 11:52:48 100 -0.025375 -0.025056 01-Jan-2018 13:23:13 101.14 0.011336 0.0114 01-Jan-2018 14:45:09 101.5 0.0035531 0.0035594 01-Jan-2018 15:30:30 100.15 -0.01339 -0.0133 02-Jan-2018 10:43:37 99.72 -0.0043028 -0.0042936 03-Jan-2018 10:02:21 100.11 0.0039033 0.003911 03-Jan-2018 11:22:37 103.96 0.037737 0.038458 03-Jan-2018 13:42:27 107.05 0.02929 0.029723
Create a function to aggregate simple returns and compute the monthly aggregates. To maintain consistency between prices and returns, for any given month, aggregate prices by reporting the last recorded price by using "lastvalue"
and report logarithmic returns by summing all intervening logarithmic returns by using "sum"
.
Notice that the aggregation function for simple returns operates along the first (row) dimension and omits missing data (NaN
s). For more information on custom aggregation functions, see timetable
and retime
. When aggregation methods are a mix of supported methods and user-supplied functions, the Aggregation
optional name-value argument must be specified as a cell vector of methods enclosed in curly braces.
f = @(x)(prod(1 + x,1,"omitnan") - 1); % Aggregate simple returns DTTMonthly = convert2monthly(DataTimeTable,Aggregation={'lastvalue' 'sum' f}); head(DTTMonthly)
Time Price Log_Return Simple_Return ___________ ______ __________ _____________ 31-Jan-2018 117.35 0.13462 0.1441 28-Feb-2018 113.52 -0.033182 -0.032637 31-Mar-2018 110.74 -0.024794 -0.024489 30-Apr-2018 105.58 -0.047716 -0.046596 31-May-2018 97.88 -0.075727 -0.07293 30-Jun-2018 99.29 0.014303 0.014405 31-Jul-2018 102.72 0.033962 0.034545 31-Aug-2018 124.99 0.19623 0.2168
Input Arguments
TT1
— Data to aggregate to monthly periodicity
timetable
Data to aggregate to a monthly periodicity, specified as a timetable.
Each variable can be a numeric vector (univariate series) or numeric matrix (multivariate series).
Note
NaN
s 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 Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Example: TT2 = convert2monthly(TT1,'Aggregation',["lastvalue"
"sum"])
Aggregation
— Aggregation method for TT1
"lastvalue"
(default) | "sum"
| "prod"
| "mean"
| "min"
| "max"
| "firstvalue"
| character vector | function handle | string vector | cell vector of character vectors or function handles
Aggregation method for TT1
defining how to
aggregate data over business days in an intra-month or inter-day
periodicity, specified as one of the following methods, a string
vector of methods, or a length numVariables
cell vector of methods, where numVariables
is
the number of variables in TT1
.
"sum"
— Sum the values in each year or day."mean"
— Calculate the mean of the values in each year or day."prod"
— Calculate the product of the values in each year or day."min"
— Calculate the minimum of the values in each year or day."max"
— Calculate the maximum of the values in each year or day."firstvalue"
— Use the first value in each year or day."lastvalue"
— Use the last value in each year or day.@customfcn
— A custom aggregation method that accepts a table variable and returns a numeric scalar (for univariate series) or row vector (for multivariate series). The function must accept empty inputs[]
.
If you specify a single method, convert2monthly
applies the specified method to all time series in TT1
. If you specify a string vector or cell vector aggregation
, convert2monthly
applies aggregation(
to j
)TT1(:,
; j
)convert2monthly
applies each aggregation method one at a time (for more details, see retime
). For example, consider a daily timetable
representing TT1
with three
variables.
Time AAA BBB CCC ___________ ______ ______ ________________ 01-Jan-2018 100.00 200.00 300.00 400.00 02-Jan-2018 100.03 200.06 300.09 400.12 03-Jan-2018 100.07 200.14 300.21 400.28 . . . . . . . . . . . . . . . 31-Jan-2018 114.65 229.3 343.95 458.60 . . . . . . . . . . . . . . . 28-Feb-2018 129.19 258.38 387.57 516.76 . . . . . . . . . . . . . . . 31-Mar-2018 162.93 325.86 488.79 651.72 . . . . . . . . . . . . . . . 30-Apr-2018 171.72 343.44 515.16 686.88 . . . . . . . . . . . . . . . 31-May-2018 201.24 402.48 603.72 804.96 . . . . . . . . . . . . . . . 30-Jun-2018 223.22 446.44 669.66 892.88
TT2
(in which all days are business
days and the 'lastvalue'
is reported on the
last business day of each month) are as
follows.Time AAA BBB CCC ___________ ______ ______ ________________ 31-Jan-2018 114.65 229.30 343.95 458.60 28-Feb-2018 129.19 258.38 387.57 516.76 31-Mar-2018 162.93 325.86 488.79 651.72 30-Apr-2018 171.72 343.44 515.16 686.88 31-May-2018 201.24 402.48 603.72 804.96 30-Jun-2018 223.22 446.44 669.66 892.88
All methods omit missing data (NaN
s) in direct aggregation calculations on each variable. However, for situations in which missing values appear in the first row of TT1
, missing values can also appear in the aggregated results TT2
. To address missing data, write and specify a custom aggregation method (function handle) that supports missing data.
Data Types: char
| string
| cell
| function_handle
Daily
— Intra-day aggregation method for TT1
"lastvalue"
(default) | "sum"
| "prod"
| "mean"
| "min"
| "max"
| "firstvalue"
| character vector | function handle | string vector | cell vector of character vectors or function handles
Intra-day aggregation method for TT1
, specified as an aggregation method, a
string vector of methods, or a length numVariables
cell vector of
methods. For more details on supported methods and behaviors, see the
'Aggregation'
name-value argument.
Data Types: char
| string
| cell
| function_handle
EndOfMonthDay
— Day of the month that ends months
last business day of month (default) | integer with value 1
to
31
Day of the month that ends months, specified as a scalar integer
with value 1
to 31
. For
months with fewer days than EndOfMonthDay
,
convert2monthly
reports aggregation results
on the last business day of the month.
Data Types: double
Output Arguments
TT2
— Monthly data
timetable
Monthly data, returned as a timetable. The time arrangement of
TT1
and TT2
are the
same.
If a variable of TT1
has no business-day records
during a month within the sampling time span,
convert2monthly
returns a NaN
for that variable and month in TT2
.
If the first month (month1
) of
TT1
contains at least one business day, the
first date in TT2
is the last business date of
month1
. Otherwise, the first date in
TT2
is the next end-of-month business date of
TT1
.
If the last month (monthT
) of
TT1
contains at least one business day, the
last date in TT2
is the last business date of
monthT
. Otherwise, the last date in
TT2
is the previous end-of-month business date
of TT1
.
Version History
Introduced in R2021a
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