convert2daily
Description
Examples
Aggregate Prices and Logarithmic Returns to Daily Periodicity
Apply separate aggregation methods to related variables in a timetable
while maintaining consistency between aggregated results for a daily 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
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"
.
DTT = convert2daily(DataTimeTable,Aggregation=["lastvalue" "sum"]); head(DTT)
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
To verify consistency, examine the input and output timetables for January 2 and 3, 2018.
dt = datetime(2018,1,4);
DataTimeTable(DataTimeTable.Time < dt,:) % Input data through 03-Jan-2018
ans=9×2 timetable
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
03-Jan-2018 14:45:20 105.57 -0.013922
DTT(1:3,:) % Return aggregated results
ans=3×2 timetable
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
logLastPriceJan2 = log(99.72); logLastPriceJan3 = log(105.57); aggLogRetJan3 = logLastPriceJan3 - logLastPriceJan2
aggLogRetJan3 = 0.0570
For each business day in DTT
, 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 0.0570
, which is the logarithmic return associated with the last prices recorded on January 2 and 3, 2018.
The dates of the aggregated results are whole dates that indicate the dates for which aggregated results are reported.
Input Arguments
TT1
— Data to aggregate to daily periodicity
timetable
Data to aggregate to a daily 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 = convert2daily(TT1,'Aggregation',["lastvalue"
"sum"])
Aggregation
— Intra-day aggregation method for data in 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
defining
how data is aggregated over business days, 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 timetable 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, convert2daily
applies the specified method to all time series in TT1
. If you specify a string vector or cell vector aggregation
, convert2daily
applies aggregation(
to j
)TT1(:,
; j
)convert2daily
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 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
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
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
Output Arguments
TT2
— Daily data
timetable
Daily data, returned as a timetable. The time arrangement of TT1
and TT2
are the same.
If a variable of TT1
has no records for a
business day within the sampling time span,
convert2daily
returns a NaN
for that variable and business day in 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
.
Version History
Introduced in R2021a
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