Special monthly series
fin-monthly.RdFunctions and methods dealing with special monthly
"timeSeries" objects.
Usage
rollMonthlyWindows(x, period = "12m", by = "1m")
rollMonthlySeries(x, period = "12m", by = "1m", FUN, ...)
countMonthlyRecords(x)Arguments
- x
a
"timeSeries"object.- period,by
-
character strings specifying the rollling period composed by the length of the period and its unit. Examples:
"3m","6m","12m", and"24m"represent quarterly, semi-annual, annual and bi-annual shifts, respectively. It is the responsibility of the user to determine proper start of the series. - FUN
-
the function for the statistic to be applied. For example,
colMeanin the case of aggregation. - ...
arguments passed to the function
FUN.
Details
rollMonthlySeries computes the statistics defined by the
function FUN over rolling windows, internally computed by the
function rollMonthlyWindows. Note, the periods may be
overlapping, may be dense, or even may have gaps.
countMonthlyRecords computes a "timeSeries" that holds
the number of records for each month, see examples. The dates are set
to the end of the month.
rollMonthlyWindows computes start and end dates for rolling
time windows. Argument period specifies the length of the
periods over which FUN is applied, while by gives the
amount by which the window is shifted. Non-overlapping windows
correspond to by >= period.
Value
for countMonthlyRecords and rollMonthlySeries,
a "timeSeries" object.
for rollMonthlyWindows, a list with attribute "control" keeping
the start and end dates of the series.
The components of the list are:
- from
an object from class
"timeDate".- to
an object from class
"timeDate".
Examples
## load Microsoft daily dataset
x <- MSFT
## count monthly records
head(x) # 3 obs. for Sep 2000
#> GMT
#> Open High Low Close Volume
#> 2000-09-27 63.4375 63.5625 59.8125 60.6250 53077800
#> 2000-09-28 60.8125 61.8750 60.6250 61.3125 26180200
#> 2000-09-29 61.0000 61.3125 58.6250 60.3125 37026800
#> 2000-10-02 60.5000 60.8125 58.2500 59.1250 29281200
#> 2000-10-03 59.5625 59.8125 56.5000 56.5625 42687000
#> 2000-10-04 56.3750 56.5625 54.5000 55.4375 68226700
counts <- countMonthlyRecords(x)
counts
#> GMT
#> Counts
#> 2000-09-30 3
#> 2000-10-31 22
#> 2000-11-30 21
#> 2000-12-31 20
#> 2001-01-31 21
#> 2001-02-28 19
#> 2001-03-31 22
#> 2001-04-30 20
#> 2001-05-31 22
#> 2001-06-30 21
#> 2001-07-31 21
#> 2001-08-31 23
## diy computation of the counts
diy <- rollMonthlySeries(x[ , 1], period = "1m", by = "1m", FUN = NROW)
## difference is only in some attributes (e.g. column names)
all.equal(diy, counts)
#> [1] "Names: 1 string mismatch"
#> [2] "Attributes: < Component “dimnames”: Component 2: 1 string mismatch >"
#> [3] "Attributes: < Component “units”: 1 string mismatch >"
## quaterly non-overlapping time periods
windows <- rollMonthlyWindows(counts[-1, ], period = "3m", by = "3m")
windows
#> $from
#> GMT
#> [1] [2000-10-01] [2000-11-01] [2000-12-01] [2001-01-01] [2001-02-01]
#> [6] [2001-03-01] [2001-04-01] [2001-05-01]
#>
#> $to
#> GMT
#> [1] [2000-12-31] [2001-01-31] [2001-02-28] [2001-03-31] [2001-04-30]
#> [6] [2001-05-31] [2001-06-30] [2001-07-31]
#>
#> attr(,"control")
#> GMT
#> start end
#> [2000-10-31] [2001-08-31]
## nicely print results as a data.frame, each row is a time window
data.frame(cbind(FROM = format(windows$from), TO = format(windows$to)))
#> FROM TO
#> 1 2000-10-01 2000-12-31
#> 2 2000-11-01 2001-01-31
#> 3 2000-12-01 2001-02-28
#> 4 2001-01-01 2001-03-31
#> 5 2001-02-01 2001-04-30
#> 6 2001-03-01 2001-05-31
#> 7 2001-04-01 2001-06-30
#> 8 2001-05-01 2001-07-31
## compute the average number of monthly trading days per quarter
rollMonthlySeries(counts[-1, ], period = "3m", by = "3m", FUN = mean)
#> GMT
#> Counts
#> 2000-12-31 21.00000
#> 2001-01-31 20.66667
#> 2001-02-28 20.00000
#> 2001-03-31 20.66667
#> 2001-04-30 20.33333
#> 2001-05-31 21.33333
#> 2001-06-30 21.00000
#> 2001-07-31 21.33333