convert2monthly
Description
Examples
Aggregate Timetable Data to Monthly Periodicity
Load the simulated stock price data and corresponding logarithmic returns in SimulatedStockSeries.mat
.
load SimulatedStockSeries
The timetable DataTimeTable
contains measurements recorded at various, irregular times during trading hours (09:30 to 16:00) of the New York Stock Exchange (NYSE) from January 1, 2018, through December 31, 2020.
For example, display the first few observations.
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
DataTimeTable
does not include business calendar awareness. If you want to account for nonbusiness days (weekends, holidays, and market closures) and you have a Financial Toolbox™ license, add business calendar awareness by using the addBusinessCalendar
function.
Aggregate the price series to a monthly series by reporting the final price in each month.
MonthlyPrice = convert2monthly(DataTimeTable(:,"Price"));
tail(MonthlyPrice)
Time Price ___________ ______ 31-May-2020 227.22 30-Jun-2020 224.29 31-Jul-2020 236.4 31-Aug-2020 227.5 30-Sep-2020 246.77 31-Oct-2020 275.07 30-Nov-2020 298.87 31-Dec-2020 301.04
MonthlyPrice
is a timetable containing the final prices for each reported month in DataTimeTable
.
Use Custom Aggregation Method to Convert 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 the simulated stock price data and corresponding logarithmic returns in SimulatedStockSeries.mat
.
load SimulatedStockSeries
Include another variable in the data called Simple_Return
, which contains the simple (proportional) returns associated with the price series, 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
The price series Price
contains absolute measurements, whereas the log and simple returns series, Log_Return
and Simple_Return
, are the rates of change of the price series among successive observations. Because the series have different units, you must specify the appropriate method when you aggregate the series. Specifically, if you report the final price for a given periodicity, you must report the sum of the log returns within each period and a custom transformation for simple returns.
Create a function to aggregate simple returns.
f = @(x)(prod(1 + x,1,'omitnan') - 1);
Aggregate the data so that the result has an monthly periodicity. For each series, specify the aggregation method that is appropriate for the unit.
TT = convert2monthly(DataTimeTable,Aggregation={'lastvalue' 'sum' f}); head(TT)
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
The aggregation function for simple returns operates along the first dimension (row) and omits missing data (NaN
s).
For more information on custom aggregation functions, see timetable
and retime
.
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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