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# Rmetrics is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# Rmetrics is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Library General Public License for more details.
#
# You should have received a copy of the GNU Library General
# Public License along with this library; if not, write to the
# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA 02111-1307 USA
################################################################################
test.timeSeries =
function()
{
# timeSeries - Creates a 'timeSeries' object from scratch
# Settings:
setRmetricsOptions(myFinCenter = "GMT")
set.seed(4711)
data = matrix(round(rnorm(12), 3))
data
class(data)
charvec = format(timeCalendar(2006))
charvec
class(charvec)
# Compose Univariate daily random sequence
setRmetricsOptions(myFinCenter = "GMT")
uTS = timeSeries(data, charvec, units = "uTS")
series(uTS)
print(uTS)
# FinCenter Functionality:
timeSeries(data, charvec, units = "uTS", zone = "GMT", FinCenter = "GMT")
timeSeries(data, charvec, units = "uTS", zone = "Zurich", FinCenter = "Zurich")
timeSeries(data, charvec, units = "uTS", zone = "GMT", FinCenter = "Zurich")
timeSeries(data, charvec, units = "uTS", zone = "Zurich", FinCenter = "GMT")
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.readSeries =
function()
{
# readSeries - Reads from a spreadsheet and creates a 'timeSeries'
# Load Microsoft Data:
data(MSFT)
MSFT.df = as.data.frame(MSFT)
# Read Data Frame:
write.table(MSFT.df, file = "msft.dat.csv", sep = ";")
read.table("msft.dat.csv", sep = ";")
# Read Time Series:
# X = readSeries("msft.dat.csv")
# X = X[1:12, ]
# class(X)
# Show Part of Series:
# head(X)[, 1:5]
# head(X[, 1:5])
# head(X[, 1:5], 2)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.returns =
function()
{
# returns - Computes returns from a 'timeSeries' object
# Load Time Series:
X = MSFT
head(X)
# returns :
OPEN = X[, 1]
print(OPEN)
MSFT.RET = returns(OPEN)
print(MSFT.RET)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.applySeries =
function()
{
# applySeries - Applies a function to blocks of a 'timeSeries'
NA
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.orderStatistics =
function()
{
# orderStatistics - Compute order statistic of a 'timeSeries'
# Load Data:
X = MSFT
head(X)
# returns:
OPEN = X[, 1]
print(OPEN)
# ORDER STATISTICS:
orderStatistics(OPEN)
orderStatistics(X[, -5])
orderStatistics(X[, -5])$Open
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.series =
function()
{
# series - Extracts data slot from 'timeSeries' object
# Load Microsoft Data:
X = MSFT
X = X[1:12, ]
class(X)
# Return Series:
OPEN = X[, 1]
OPEN
returns(OPEN)
# Volatility Series:
abs(returns(OPEN))
# Data Matrix:
series(OPEN)
Y = series(X)
Y
class(Y)
# Position Vector:
PO = time(OPEN)
PO
PX = time(X)
PX
class(PX)
checkEquals(
target = sum(as.integer(PO - PX)),
current = 0)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.isUnivariate =
function()
{
# isUnivariate Tests if an object of class 'timeSeries' is univariate
# Load Microsoft Data:
X = MSFT
OPEN = X[, 1]
# Is Univariate?
checkTrue(!isUnivariate(X))
checkTrue(isUnivariate(OPEN))
checkTrue(isMultivariate(X))
checkTrue(!isMultivariate(OPEN))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.isMultivariate =
function()
{
# isMultivariate - Tests if an object of class 'timeSeries' is multivariate
# Load Microsoft Data:
X = MSFT
OPEN = X[, 1]
# Is Multivariate?
checkTrue(isMultivariate(X))
checkTrue(!isMultivariate(OPEN))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.displayMethods =
function()
{
# print.timeSeries Print method for a 'timeSeries' object
# plot.timeSeries Plot method for a 'timeSeries' object
# lines.timeSeries Lines method for a 'timeSeries' object
# points.timeSeries Points method for a 'timeSeries' object
## FIXME(MM) - if we store this -- make it a package data set!
## Microsoft Data:
## MSFT.df = data.frame(matrix(c(
## 20010326, 57.1250, 57.5000, 55.5625, 56.0625, 31559300,
## 20010327, 56.0625, 58.5625, 55.8750, 58.2500, 47567800,
## 20010328, 57.3750, 57.9375, 55.3750, 55.5625, 39340800,
## 20010329, 55.3750, 57.1875, 54.5625, 55.3750, 43492500,
## 20010330, 55.7500, 56.1875, 53.8750, 54.6875, 45600800,
## 20010402, 54.8125, 56.9375, 54.6250, 55.8125, 37962000,
## 20010403, 55.3125, 55.3125, 52.7500, 53.3750, 47093800,
## 20010404, 53.3750, 55.0000, 51.0625, 51.9375, 52023300,
## 20010405, 53.7500, 57.3750, 53.5000, 56.7500, 56682000,
## 20010406, 56.3750, 57.1875, 55.0625, 56.1875, 46311000,
## 20010409, 56.5700, 57.4200, 55.6600, 57.1500, 28147800,
## 20010410, 57.9500, 60.0900, 57.7800, 59.6800, 54599700,
## 20010411, 60.6500, 61.5000, 59.7000, 60.0400, 54939800,
## 20010412, 59.5600, 62.3100, 59.3500, 62.1800, 43760000,
## 20010416, 61.4000, 61.5800, 60.1200, 60.7900, 32928700,
## 20010417, 60.5200, 62.1100, 60.0400, 61.4800, 42574600,
## 20010418, 63.3900, 66.3100, 63.0000, 65.4300, 78348200,
## 20010419, 65.8100, 69.0000, 65.7500, 68.0400, 79687800,
## 20010420, 70.3000, 71.1000, 68.5000, 69.0000, 96459800,
## 20010423, 68.1100, 68.4700, 66.9000, 68.2500, 46085600,
## 20010424, 68.2000, 69.9300, 67.1400, 67.5500, 44588300,
## 20010425, 67.5700, 69.7900, 67.2500, 69.6900, 38372000,
## 20010426, 70.0700, 71.0000, 68.2500, 69.1300, 59368800,
## 20010427, 69.5300, 69.6800, 66.2100, 67.1200, 60786200,
## 20010430, 68.5300, 69.0600, 67.6800, 67.7500, 37184100,
## 20010501, 67.6600, 70.3000, 67.6000, 70.1700, 41851400,
## 20010502, 71.0000, 71.1500, 69.3500, 69.7600, 46432200,
## 20010503, 69.2500, 70.1800, 68.1400, 68.5300, 33136700,
## 20010504, 68.0000, 71.0500, 67.9600, 70.7500, 59769200,
## 20010507, 70.8300, 72.1500, 70.7000, 71.3800, 54678100),
## byrow = TRUE, ncol = 6))
## colnames(MSFT.df) = c("YYMMDD", "Open", "High", "Low", "Close", "Volume")
# Data:
X = MSFT
X = X[1:12, ]
OPEN = X[, 1]
# Print:
print(X)
print(OPEN)
# Plot:
par(mfrow = c(1, 1))
plot(OPEN, type = "l")
# GMT - Plot:
tC = timeCalendar(2006, 1, 1, 0:23, 0, 0, zone = "GMT", FinCenter = "GMT")
tS = timeSeries(data = matrix(rnorm(24), ncol = 1), charvec = tC)
plot(tS)
# Zurich - Plot:
tC = timeCalendar(2006, 1, 1, 0:23, 0, 0, zone = "GMT", FinCenter = "Zurich")
tS = timeSeries(data = matrix(rnorm(24), ncol = 1), charvec = tC,
zone = "GMT", FinCenter = "Zurich")
plot(tS)
# New York - Plot:
tC = timeCalendar(2006, 1, 1, 0:23, 0, 0, zone = "GMT", FinCenter = "NewYork")
tS = timeSeries(data = matrix(rnorm(24), ncol = 1), charvec = tC,
zone = "GMT", FinCenter = "NewYork")
plot(tS, type = "h")
lines (tS, col = "red", lty = 3)
points(tS, col = "blue", pch = 19)
abline(h=0, col = "grey")
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.dummyDailySeries =
function()
{
# dummyDailySeries - Creates a dummy daily 'timeSeries' object
# Create Dummy Time Series:
setRmetricsOptions(myFinCenter = "GMT")
tS = dummyDailySeries(matrix(rnorm(12)))
print(tS)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.alignDailySeries =
function()
{
# alignDailySeries - Aligns a 'timeSeries' object to new positions
# Time Series:
setRmetricsOptions(myFinCenter = "GMT")
tS = MSFT[1:25, ]
print(tS)
dim(tS)
# Align Daily Series:
alignDailySeries(tS, method = "interp")
# Align Daily Series:
alignDailySeries(tS, method = "fillNA")
# Align Daily Series:
alignDailySeries(tS, method = "fillNA", include.weekends = TRUE)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
## DW >
## test.ohlcDailyPlot =
## function()
## {
## # ohlcDailyPlot - Plots openhighlowclose bar chart
##
## # Price or Incdex Series:
## setRmetricsOptions(myFinCenter = "GMT")
## tS = MSFT[1:25, ]
## print(tS)
## dim(tS)
## colnames(tS)
##
## # Graph Frame:
## par(mfrow = c(2, 1), cex = 0.7)
## ohlcDailyPlot(tS)
##
## # Return Value:
## return()
## }
# ------------------------------------------------------------------------------
test.modelSeries =
function()
{
if (FALSE) {
# Move to fArma ...
# Undocumented Material:
Matrix = cbind(X = rnorm(10), Y = rnorm(10))
Matrix = cbind(Matrix, Z = Matrix[, "Y"] - Matrix[, "X"])
TS = dummyDailySeries(Matrix, units = c("X", "Y", "Z") )
head(TS)
.modelSeries(Y ~ ar(2), data = TS, lhs = TRUE)
.modelSeries(log(abs(Z)) ~ lm(X + sin(Y)), data = TS, fake = TRUE)
.modelSeries(log(abs(Z)) ~ lm(X + sin(Y)), data = TS, lhs = TRUE)
.modelSeries(Y ~ ar(2), data = as.data.frame(TS), lhs = TRUE)
.modelSeries(log(abs(Z)) ~ lm(X + sin(Y)), data = TS, fake = TRUE)
.modelSeries(log(abs(Z)) ~ lm(X + sin(Y)), data = TS, lhs = TRUE)
require(timeSeries)
.modelSeries(Y ~ ar(2), data = rnorm(10))
.modelSeries(Y ~ ar(2), data = as.ts(rnorm(10)))
.modelSeries(x ~ arima(2, 0, 1), data = armaSim(n=10))
.modelSeries(~ ar(2), rnorm(10))
# attach(TS) # CHECK
# .modelSeries(Y ~ ar(2), lhs = TRUE)
.modelSeries(Y ~ ar(2) + garch(1,1), data = rnorm(10))
.modelSeries(Y ~ ar(2) + garch(1,1), data = rnorm(10), lhs = TRUE)
.modelSeries(Y ~ ar(2) + garch(1,1), data = TS, lhs = TRUE)
} else {
NA
}
# Return Value:
return()
}
################################################################################
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