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# RUnit tests TTR DVI
#
# Create input data
data(ttrc)
rownames(ttrc) <- ttrc$Date
ttrc$Date <- NULL
#################################################
test.correct_column_names <- function() {
dat <- setNames(ttrc$Close, rownames(ttrc))
dvi <- DVI(dat)
checkEquals(colnames(dvi), c("dvi.mag", "dvi.str", "dvi"))
}
TTR/inst/unitTests/runit.TTR.Trend.R 0000644 0001762 0000144 00000007734 13552670134 016732 0 ustar ligges users #
# RUnit tests TTR moving averages
#
# test reclass works and throws error
# test xtsAttributes, both CLASS and USER
# test all.equal(CLASS) and !all.equal(CLASS) cases
# Create input data
data(ttrc)
rownames(ttrc) <- ttrc$Date
ttrc$Date <- NULL
iAll <- as.matrix(ttrc[1:250,])
iTop <- iAll; iTop[1:10,] <- NA
iMid <- iAll; iMid[9:20,] <- NA
hl <- c('High','Low')
hlc <- c('High','Low','Close')
cl <- 'Close'
# Load output data
load(system.file("unitTests/output.trend.rda", package="TTR"))
#################################################
# ADX
test.ADX <- function() {
checkEqualsNumeric( ADX(iAll[,hlc]), output$allADX )
checkEquals( attributes(ADX(iAll[,hlc])), attributes(output$allADX) )
checkEqualsNumeric( ADX(iTop[,hlc]), output$topADX )
checkEquals( attributes(ADX(iTop[,hlc])), attributes(output$topADX) )
#checkException( ADX(iMid[,hlc]) )
}
test.ADX.does.not.overwrite.maArgs <- function() {
wilder.and.matype <- ADX(iAll[,hlc], maType = "EMA", wilder = FALSE)
wilder.only <- ADX(iAll[,hlc], wilder = FALSE)
checkEqualsNumeric( wilder.and.matype, wilder.only )
}
# Aroon
test.aroon.orig <- function() {
# non-xts
ia <- iAll[,hl]
it <- iTop[,hl]
im <- iMid[,hl]
rownames(ia) <- rownames(it) <- rownames(im) <- NULL
oa <- aroon(ia); rownames(oa) <- rownames(iAll)
ot <- aroon(it); rownames(ot) <- rownames(iTop)
checkEqualsNumeric( oa, output$allAroon )
checkEquals( attributes(oa), attributes(output$allAroon) )
checkEqualsNumeric( ot, output$topAroon )
checkEquals( attributes(ot), attributes(output$topAroon) )
#checkException( aroon(im) )
}
test.aroon.xts <- function() {
# xts
checkEqualsNumeric( aroon(iAll[,hl]), output$allAroon )
checkEquals( attributes(aroon(iAll[,hl])), attributes(output$allAroon) )
checkEqualsNumeric( aroon(iTop[,hl]), output$topAroon )
checkEquals( attributes(aroon(iTop[,hl])), attributes(output$topAroon) )
#checkException( aroon(iMid[,hl]) )
}
# Average True Range
test.ATR.orig <- function() {
# non-xts
ia <- iAll[,hlc]
it <- iTop[,hlc]
im <- iMid[,hlc]
rownames(ia) <- rownames(it) <- rownames(im) <- NULL
aATR <- ATR(ia); rownames(aATR) <- rownames(iAll)
tATR <- ATR(it); rownames(tATR) <- rownames(iTop)
checkEqualsNumeric( aATR, output$allATR )
checkEquals( attributes(aATR), attributes(output$allATR) )
checkEqualsNumeric( tATR, output$topATR )
checkEquals( attributes(tATR), attributes(output$topATR) )
#checkException( ATR(im) )
}
test.ATR.xts <- function() {
# xts
checkEqualsNumeric( ATR(iAll[,hlc]), output$allATR )
checkEquals( attributes(ATR(iAll[,hlc])), attributes(output$allATR) )
checkEqualsNumeric( ATR(iTop[,hlc]), output$topATR )
checkEquals( attributes(ATR(iTop[,hlc])), attributes(output$topATR) )
#checkException( ATR(iMid[,hlc]) )
}
test.ATR.does.not.overwrite.maArgs <- function() {
wilder.and.matype <- ATR(iAll[,hlc], maType = "EMA", wilder = FALSE)
wilder.only <- ATR(iAll[,hlc], wilder = FALSE)
checkEqualsNumeric( wilder.and.matype, wilder.only )
}
# Commodity Channel Index
test.CCI <- function() {
checkEqualsNumeric( CCI(iAll[,hlc]), output$allCCI )
checkEquals( attributes(CCI(iAll[,hlc])), attributes(output$allCCI) )
checkEqualsNumeric( CCI(iTop[,hlc]), output$topCCI )
checkEquals( attributes(CCI(iTop[,hlc])), attributes(output$topCCI) )
#checkException( CCI(input$mid[,c('High','Low','Close')]) )
}
# Trend Detection Index
test.TDI <- function() {
ia <- iAll[,cl]
it <- iTop[,cl]
names(ia) <- names(it) <- NULL
checkEqualsNumeric( TDI(ia), output$allTDI )
checkEquals( attributes(TDI(ia)), attributes(output$allTDI) )
#checkEqualsNumeric( TDI(iTop[,cl]), output$topTDI )
#checkException( TDI(iMid[,cl]) )
}
# Vertical Horizontal Filter
test.VHF <- function() {
ia <- iAll[,cl]
it <- iTop[,cl]
names(ia) <- names(it) <- NULL
checkEqualsNumeric( VHF(ia), output$allVHF )
checkEquals( attributes(VHF(ia)), attributes(output$allVHF) )
#checkEqualsNumeric( VHF(iTop[,cl]), output$topVHF )
#checkException( VHF(iMid[,cl] )
}
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# RUnit tests TTR moving averages
#
# Create input data
data(ttrc)
rownames(ttrc) <- ttrc$Date
ttrc$Date <- NULL
input <- list( all=ttrc[1:250,], top=ttrc[1:250,], mid=ttrc[1:250,] )
input$top[1:10,] <- NA
input$mid[9:20,] <- NA
# Load output data
load(system.file("unitTests/output.MA.rda", package="TTR"))
#################################################
# Simple Moving Average
test.SMA <- function() {
checkEqualsNumeric( SMA(input$all$Close), output$allSMA )
checkEquals( attributes(SMA(input$all$Close)), attributes(output$allSMA) )
checkEqualsNumeric( SMA(input$top$Close), output$topSMA )
checkEquals( attributes(SMA(input$top$Close)), attributes(output$topSMA) )
checkException( SMA(input$mid$Close) )
checkException( SMA(input$all[,1:2]) )
}
# Exponential Moving Average
test.EMA <- function() {
checkEqualsNumeric( EMA(input$all$Close), output$allEMA )
checkEquals( attributes(EMA(input$all$Close)), attributes(output$allEMA) )
checkEqualsNumeric( EMA(input$top$Close), output$topEMA )
checkEquals( attributes(EMA(input$top$Close)), attributes(output$topEMA) )
checkException( EMA(input$mid$Close) )
checkException( EMA(input$all[,1:2]) )
checkException( EMA(input$all$Close, n = -1) )
checkException( EMA(input$all$Close, n = NROW(input$all) + 1) )
}
test.EMA.n.ratio <- function() {
out <- 0:9 * 1.0
is.na(out) <- 1:2
checkEqualsNumeric(EMA(1:10, ratio = 0.5), out)
checkEqualsNumeric(EMA(1:10, n = 3), out)
}
# Exponential Moving Average, Wilder ratio
test.EMA.wilder <- function() {
checkEqualsNumeric( EMA(input$all$Close, wilder=TRUE), output$allEMAwilder )
checkEquals( attributes(EMA(input$all$Close, wilder=TRUE)), attributes(output$allEMAwilder) )
checkEqualsNumeric( EMA(input$top$Close, wilder=TRUE), output$topEMAwilder )
checkEquals( attributes(EMA(input$top$Close, wilder=TRUE)), attributes(output$topEMAwilder) )
checkException( EMA(input$mid$Close, wilder=TRUE) )
}
# Double-Exponential Moving Average
test.DEMA <- function() {
checkEqualsNumeric( DEMA(input$all$Close), output$allDEMA )
checkEquals( attributes(DEMA(input$all$Close)), attributes(output$allDEMA) )
checkEqualsNumeric( DEMA(input$top$Close), output$topDEMA )
checkEquals( attributes(DEMA(input$top$Close)), attributes(output$topDEMA) )
checkException( DEMA(input$mid$Close) )
checkException( DEMA(input$all[,1:2]) )
}
# Weighted Moving Average, 1:n
test.WMA <- function() {
checkEqualsNumeric( WMA(input$all$Close), output$allWMA )
checkEquals( attributes(WMA(input$all$Close)), attributes(output$allWMA) )
checkEqualsNumeric( WMA(input$top$Close), output$topWMA )
checkEquals( attributes(WMA(input$top$Close)), attributes(output$topWMA) )
checkException( WMA(input$mid$Close) )
checkException( WMA(input$all$Close, wts=1) )
checkException( WMA(input$all[,1:2]) )
checkException( WMA(input$all$Close, n = -1) )
checkException( WMA(input$all$Close, n = NROW(input$all) + 1) )
}
# Weighted Moving Average, Volume
test.WMAvol <- function() {
checkEqualsNumeric( WMA(input$all$Close, wts=input$all$Volume), output$allWMAvol )
checkEquals( attributes(WMA(input$all$Close, wts=input$all$Volume)), attributes(output$allWMAvol) )
checkEqualsNumeric( WMA(input$top$Close, wts=input$top$Volume), output$topWMAvol )
checkEquals( attributes(WMA(input$top$Close, wts=input$top$Volume)), attributes(output$topWMAvol) )
checkException( WMA(input$all$Close, wts=input$mid$Volume) )
checkException( WMA(input$all[,1:2], wts=input$all$Volume) )
checkException( WMA(input$all$Close, wts=input$all[,1:2]) )
}
# Exponential, Volume-Weighted Moving Average
test.EVWMA <- function() {
checkEqualsNumeric( EVWMA(input$all$Close, input$all$Volume), output$allEVWMA )
checkEquals( attributes(EVWMA(input$all$Close, input$all$Volume)), attributes(output$allEVWMA) )
checkEqualsNumeric( EVWMA(input$top$Close, input$top$Volume), output$topEVWMA )
checkEquals( attributes(EVWMA(input$top$Close, input$top$Volume)), attributes(output$topEVWMA) )
checkException( EVWMA(input$mid$Close, input$mid$Volume) )
checkException( EVWMA(input$all$Close) )
checkException( EVWMA(input$all[,1:2], input$all$Volume) )
checkException( EVWMA(input$all$Close, input$all[,1:2]) )
checkException( EVWMA(input$all$Close, n = -1) )
checkException( EVWMA(input$all$Close, n = NROW(input$all) + 1) )
}
# Zero-Lag Exponential Moving Average
test.ZLEMA <- function() {
checkEqualsNumeric( ZLEMA(input$all$Close), output$allZLEMA )
checkEquals( attributes(ZLEMA(input$all$Close)), attributes(output$allZLEMA) )
checkEqualsNumeric( ZLEMA(input$top$Close), output$topZLEMA )
checkEquals( attributes(ZLEMA(input$top$Close)), attributes(output$topZLEMA) )
checkException( ZLEMA(input$mid$Close) )
checkException( ZLEMA(input$all[,1:2]) )
}
TTR/inst/unitTests/runit.TTR.Misc.R 0000644 0001762 0000144 00000005657 13425330036 016544 0 ustar ligges users #
# RUnit tests TTR moving averages
#
# test reclass works and throws error
# test xtsAttributes, both CLASS and USER
# test all.equal(CLASS) and !all.equal(CLASS) cases
# Create input data
data(ttrc)
rownames(ttrc) <- ttrc$Date
ttrc$Date <- NULL
input <- list( all=ttrc[1:250,], top=ttrc[1:250,], mid=ttrc[1:250,] )
input$top[1:10,] <- NA
input$mid[9:20,] <- NA
#iAll <- as.matrix(ttrc[1:250,])
iAll <- ttrc[1:250,]
iTop <- iAll; iTop[1:10,] <- NA
iMid <- iAll; iMid[9:20,] <- NA
hl <- c('High','Low')
hlc <- c('High','Low','Close')
cl <- 'Close'
# Load output data
load(system.file("unitTests/output.misc.rda", package="TTR"))
#################################################
# Rate-of-Change
test.ROC.continuous <- function() {
roc <- ROC(iAll[,cl], type='continuous')
checkEqualsNumeric( roc, output$allROCc )
checkEquals( attributes(roc), attributes(output$allROCc) )
#checkEqualsNumeric( ROC(input$top$Close, type='continuous'), output$topROCc )
#checkException( ROC(input$mid$Close) )
}
test.ROC.discrete <- function() {
roc <- ROC(input$all$Close, type='discrete')
checkEqualsNumeric( roc, output$allROCd )
checkEquals( attributes(roc), attributes(output$allROCd) )
#checkEqualsNumeric( ROC(input$top$Close, type='discrete'), output$topROCd )
}
# Momentum
test.momentum <- function() {
mom <- momentum(input$all$Close)
checkEqualsNumeric( mom, output$allMom )
checkEquals( attributes(mom), attributes(output$allMom) )
#checkEqualsNumeric( momentum(input$top$Close), output$topMom )
#checkException( momentum(input$mid$Close) )
}
# Close Location Value
test.CLV <- function() {
ia <- iAll[,hlc]; rownames(ia) <- NULL
it <- iTop[,hlc]; rownames(it) <- NULL
oa <- as.data.frame(output$allCLV); rownames(oa) <- rownames(ia)
ot <- as.data.frame(output$topCLV); rownames(ot) <- rownames(it)
checkEqualsNumeric( CLV(ia), output$allCLV )
checkEquals( attributes(CLV(ia)), attributes(output$allCLV) )
checkEqualsNumeric( CLV(it), output$topCLV )
checkEquals( attributes(CLV(it)), attributes(output$topCLV) )
}
# Arms' Ease of Movement
test.EMV <- function() {
ia <- iAll[,hl]; rownames(ia) <- NULL
emv.all <- EMV(ia, input$all$Volume)
checkEqualsNumeric( emv.all, output$allEMV )
checkEquals( attributes(emv.all), attributes(output$allEMV) )
#emv.top<- EMV(input$top[,c('High','Low')], input$top$Volume)
#checkEqualsNumeric( emv.top, output$topEMV )
#checkEquals( attributes(emv.top), attributes(output$topEMV) )
#checkException( EMV(input$mid[,c('High','Low')], input$mid$Volume) )
#checkException( EMV(input$all[,c('High','Low')], input$mid$Volume) )
#checkException( EMV(input$mid[,c('High','Low')], input$all$Volume) )
}
# Know Sure Thing
test.KST <- function() {
checkEqualsNumeric( KST(input$all$Close), output$allKST )
checkEquals( attributes(KST(input$all$Close)), attributes(output$allKST) )
#checkEqualsNumeric( KST(input$top$Close), output$topKST )
#checkException( KST(input$mid$Close) )
}
TTR/inst/unitTests/runit.TTR.runFun.R 0000644 0001762 0000144 00000025771 13552670134 017134 0 ustar ligges users #
# RUnit tests TTR running/rolling functions
#
# Create input data
data(ttrc)
rownames(ttrc) <- ttrc$Date
ttrc$Date <- NULL
input <- list( all=ttrc[1:250,], top=ttrc[1:250,], mid=ttrc[1:250,] )
input$top[1:10,] <- NA
input$mid[9:20,] <- NA
# Load output data
load(system.file("unitTests/output.runFun.rda", package="TTR"))
#################################################
# Sum
test.runSum <- function() {
checkEqualsNumeric( runSum(input$all$Close), output$allSum )
checkEquals( attributes(runSum(input$all$Close)), attributes(output$allSum) )
checkEqualsNumeric( runSum(input$top$Close), output$topSum )
checkEquals( attributes(runSum(input$top$Close)), attributes(output$topSum) )
checkException( runSum(input$mid$Close) )
checkException( runSum(input$all[,1:2]) )
checkEqualsNumeric( tail(runSum(input$all$Close,250),1), sum(input$all$Close) )
checkException( runSum(input$all$Close, n = -1) )
checkException( runSum(input$all$Close, n = NROW(input$all) + 1) )
}
# Wilder Sum
test.wilderSum <- function() {
checkEqualsNumeric( wilderSum(input$all$Close), output$allwSum )
checkEquals( attributes(wilderSum(input$all$Close)), attributes(output$allwSum) )
checkEqualsNumeric( wilderSum(input$top$Close), output$topwSum )
checkEquals( attributes(wilderSum(input$top$Close)), attributes(output$topwSum) )
checkException( wilderSum(input$mid$Close) )
checkException( wilderSum(input$all[,1:2]) )
checkException( wilderSum(input$all$Close, n = -1) )
checkException( wilderSum(input$all$Close, n = NROW(input$all) + 1) )
}
# Min
test.runMin <- function() {
checkEqualsNumeric( runMin(input$all$Close), output$allMin )
checkEquals( attributes(runMin(input$all$Close)), attributes(output$allMin) )
checkEqualsNumeric( runMin(input$top$Close), output$topMin )
checkEquals( attributes(runMin(input$top$Close)), attributes(output$topMin) )
checkException( runMin(input$mid$Close) )
checkException( runMin(input$all[,1:2]) )
checkException( runMin(input$all$Close, n = -1) )
checkException( runMin(input$all$Close, n = NROW(input$all) + 1) )
checkEqualsNumeric( tail(runMin(input$all$Close,250),1), min(input$all$Close) )
}
test.runMin.cumulative <- function() {
ttr <- runMin(input$all$Close, 1, TRUE)
base <- cummin(input$all$Close)
is.na(base) <- 1
checkEqualsNumeric(base, ttr)
}
# Max
test.runMax <- function() {
checkEqualsNumeric( runMax(input$all$Close), output$allMax )
checkEquals( attributes(runMax(input$all$Close)), attributes(output$allMax) )
checkEqualsNumeric( runMax(input$top$Close), output$topMax )
checkEquals( attributes(runMax(input$top$Close)), attributes(output$topMax) )
checkException( runMax(input$mid$Close) )
checkException( runMax(input$all[,1:2]) )
checkException( runMax(input$all$Close, n = -1) )
checkException( runMax(input$all$Close, n = NROW(input$all) + 1) )
checkEqualsNumeric( tail(runMax(input$all$Close,250),1), max(input$all$Close) )
}
test.runMax.cumulative <- function() {
ttr <- runMax(input$all$Close, 1, TRUE)
base <- cummax(input$all$Close)
is.na(base) <- 1
checkEqualsNumeric(base, ttr)
}
# Mean
test.runMean <- function() {
checkEqualsNumeric( runMean(input$all$Close), output$allMean )
checkEquals( attributes(runMean(input$all$Close)), attributes(output$allMean) )
checkEqualsNumeric( runMean(input$top$Close), output$topMean )
checkEquals( attributes(runMean(input$top$Close)), attributes(output$topMean) )
checkException( runMean(input$mid$Close) )
checkException( runMean(input$all[,1:2]) )
checkException( runMean(input$all$Close, n = -1) )
checkException( runMean(input$all$Close, n = NROW(input$all) + 1) )
checkEqualsNumeric( tail(runMean(input$all$Close,250),1), mean(input$all$Close) )
}
test.runMean.cumulative <- function() {
ttr <- runMean(input$all$Close, 5, TRUE)
base <- cumsum(input$all$Close) / seq_along(input$all$Close)
is.na(base) <- 1:4
checkEqualsNumeric(base, ttr)
}
# Median
test.runMedian <- function() {
checkEqualsNumeric( runMedian(input$all$Close), output$allMedian )
checkEquals( attributes(runMedian(input$all$Close)), attributes(output$allMedian) )
checkEqualsNumeric( runMedian(input$top$Close), output$topMedian )
checkEquals( attributes(runMedian(input$top$Close)), attributes(output$topMedian) )
checkException( runMedian(input$mid$Close) )
checkException( runMedian(input$all[,1:2]) )
checkException( runMedian(input$all$Close, n = -1) )
checkException( runMedian(input$all$Close, n = NROW(input$all) + 1) )
checkEqualsNumeric( tail(runMedian(input$all$Close,250),1), median(input$all$Close) )
}
test.runMedian.cumulative <- function() {
cummedian <- compiler::cmpfun(
function(x) {
med <- x * NA_real_
for (i in seq_along(x)) {
med[i] <- median(x[1:i])
}
med
}
)
base <- cummedian(input$all$Close)
is.na(base) <- 1:4
ttr <- runMedian(input$all$Close, 5, "mean", TRUE)
checkEqualsNumeric(base, ttr)
is.na(base) <- 1:5
ttr <- runMedian(input$all$Close, 6, "mean", TRUE)
checkEqualsNumeric(base, ttr)
}
# Covariance
test.runCov <- function() {
checkEqualsNumeric( runCov(input$all$High, input$all$Low), output$allCov )
checkEquals( attributes(runCov(input$all$High, input$all$Low)), attributes(output$allCov) )
checkEqualsNumeric( runCov(input$top$High, input$top$Low), output$topCov )
checkEquals( attributes(runCov(input$top$High, input$top$Low)), attributes(output$topCov) )
checkException( runCov(input$mid$High, input$mid$Low) )
checkException( runCov(input$all$High) )
checkException( runCov(input$all[,1:2], input$all$Low) )
checkException( runCov(input$all$Close, n = -1) )
checkException( runCov(input$all$Close, n = NROW(input$all) + 1) )
checkEqualsNumeric( tail(runCov(input$all$High, input$all$Low, 250),1), cov(input$all$High, input$all$Low) )
}
test.runCov.cumulative <- function() {
cumcov <- compiler::cmpfun(
function(x) {
cov <- x * NA_real_
for (i in seq_along(x)) {
y <- x[1:i]
cov[i] <- cov(y, y)
}
cov
}
)
x <- input$all$Close
base <- cumcov(x)
is.na(base) <- 1:4
ttr <- runCov(x, x, 5, "all.obs", TRUE, TRUE)
checkEqualsNumeric(base, ttr)
is.na(base) <- 1:5
ttr <- runCov(x, x, 6, "all.obs", TRUE, TRUE)
checkEqualsNumeric(base, ttr)
}
# Correlation
test.runCor <- function() {
checkEqualsNumeric( runCor(input$all$High, input$all$Low), output$allCor )
checkEquals( attributes(runCor(input$all$High, input$all$Low)), attributes(output$allCor) )
checkEqualsNumeric( runCor(input$top$High, input$top$Low), output$topCor )
checkEquals( attributes(runCor(input$top$High, input$top$Low)), attributes(output$topCor) )
checkException( runCor(input$mid$High, input$mid$Low) )
checkException( runCor(input$all$High) )
checkException( runCor(input$all[,1:2], input$all$Low) )
checkException( runCor(input$all$Close, n = -1) )
checkException( runCor(input$all$Close, n = NROW(input$all) + 1) )
checkEqualsNumeric( tail(runCor(input$all$High, input$all$Low, 250),1), cor(input$all$High, input$all$Low) )
}
# Variance
test.runVar <- function() {
checkEqualsNumeric( runVar(input$all$Close), output$allVar )
checkEquals( attributes(runVar(input$all$Close)), attributes(output$allVar) )
checkEqualsNumeric( runVar(input$top$Close), output$topVar )
checkEquals( attributes(runVar(input$top$Close)), attributes(output$topVar) )
checkException( runVar(input$mid$Close) )
checkException( runVar(input$all[,1:2], input$all$Low) )
checkException( runVar(input$all$Close, n = -1) )
checkException( runVar(input$all$Close, n = NROW(input$all) + 1) )
checkEqualsNumeric( tail(runVar(input$all$Close,n=250),1), var(input$all$Close) )
}
# Standard Deviation
test.runSD <- function() {
checkEqualsNumeric( runSD(input$all$Close), output$allSD )
checkEquals( attributes(runSD(input$all$Close)), attributes(output$allSD) )
checkEqualsNumeric( runSD(input$top$Close), output$topSD )
checkEquals( attributes(runSD(input$top$Close)), attributes(output$topSD) )
checkException( runSD(input$mid$Close) )
checkException( runSD(input$all[,1:2]) )
checkException( runSD(input$all$Close, n = -1) )
checkException( runSD(input$all$Close, n = NROW(input$all) + 1) )
checkEqualsNumeric( tail(runSD(input$all$Close,250),1), sd(input$all$Close) )
}
# Absolute deviation
test.runMAD <- function() {
checkEqualsNumeric( runMAD(input$all$Close), output$allMAD )
checkEquals( attributes(runMAD(input$all$Close)), attributes(output$allMAD) )
checkEqualsNumeric( runMAD(input$top$Close), output$topMAD )
checkEquals( attributes(runMAD(input$top$Close)), attributes(output$topMAD) )
checkException( runMAD(input$mid$Close) )
checkException( runMAD(input$all[,1:2]) )
checkException( runMAD(input$all$Close, n = -1) )
checkException( runMAD(input$all$Close, n = NROW(input$all) + 1) )
checkEqualsNumeric( tail(runMAD(input$all$Close,250),1), mad(input$all$Close) )
}
test.runMAD.cumulative <- function() {
cummad <- compiler::cmpfun(
function(x) {
mad <- x * NA_real_
for (i in seq_along(x)) {
y <- x[1:i]
mad[i] <- mad(y)
}
mad
}
)
x <- input$all$Close
base <- cummad(x)
is.na(base) <- 1:4
ttr <- runMAD(x, 5, cumulative = TRUE)
checkEqualsNumeric(base, ttr)
is.na(base) <- 1:5
ttr <- runMAD(x, 6, cumulative = TRUE)
checkEqualsNumeric(base, ttr)
}
# Percent Rank
test.runPercentRank_exact.multiplier_bounds <- function() {
x <- input$all$Close
checkException( runPercentRank(x, 10, exact.multiplier = -0.1) )
checkException( runPercentRank(x, 10, exact.multiplier = 1.1) )
}
xdata <- c(7.9, 5.2, 17.5, -12.7, 22, 4.3, -15.7, -9.3, 0.6, 0,
-22.8, 7.6, -5.5, 1.7, 5.6, 15.1, 6.6, 11.2, -7.8, -4.3)
xrank10_1 <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.4, 0.1, 0.8,
0.5, 0.7, 0.9, 1, 0.8, 0.9, 0.2, 0.4)
xrank10_0 <- c(NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.3, 0, 0.7,
0.4, 0.6, 0.8, 0.9, 0.7, 0.8, 0.1, 0.3)
test.runPercentRank_exact.multiplier_eq0 <- function() {
xrank <- round(xrank10_0, 2)
checkIdentical(xrank, runPercentRank(xdata, 10, FALSE, 0))
}
test.runPercentRank_exact.multiplier_eq0.5 <- function() {
xrank <- round(xrank10_0 + 0.05, 2)
checkIdentical(xrank, runPercentRank(xdata, 10, FALSE, 0.5))
}
test.runPercentRank_exact.multiplier_eq1 <- function() {
xrank <- round(xrank10_0 + 0.1, 2)
checkIdentical(xrank, runPercentRank(xdata, 10, FALSE, 1))
}
test.runPercentRank_cumulTRUE_exact.multiplier_eq0 <- function() {
xrank <- c(0, 0, 2, 0, 4, 1, 0, 2, 3, 3, 0, 8,
4, 7, 10, 13, 11, 14, 4, 6) / 1:20
checkIdentical(xrank, runPercentRank(xdata, 10, TRUE, 0))
}
test.runPercentRank_cumulTRUE_exact.multiplier_eq0.5 <- function() {
xrank <- (c(0, 0, 2, 0, 4, 1, 0, 2, 3, 3, 0, 8,
4, 7, 10, 13, 11, 14, 4, 6) + 0.5) / 1:20
#xrank[1] <- 0
checkIdentical(xrank, runPercentRank(xdata, 10, TRUE, 0.5))
}
test.runPercentRank_cumulTRUE_exact.multiplier_eq1 <- function() {
xrank <- (c(0, 0, 2, 0, 4, 1, 0, 2, 3, 3, 0, 8,
4, 7, 10, 13, 11, 14, 4, 6) + 1) / 1:20
#xrank[1] <- 0
checkIdentical(xrank, runPercentRank(xdata, 10, TRUE, 1))
}
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SIGNIFICANT USER-VISIBLE CHANGES
- Update DVI to use runPercentRank. Thanks to Ivan Popivanov for the patch.
- getYahooData now returns an xts object with Date index (not POSIXct).
- MA function colnames no longer based on input colnames.
NEW FEATURES
- Add HMA and ALMA functions/docs. Thanks to Ivan Popivanov.
- Add Ultimate Oscillator function/docs/tests. Thanks to Ivan Popivanov.
BUG FIXES
- runFuns now throw error if there are not enough non-NA values.
- Change all instances of lag() to lag.xts() in case 'x' is a matrix.
Thanks to Ivan Popivanov for the report.
- Correct output column names in ATR docs.
- CLV now sets NaN and Inf values to 0, instead of only NaN values.
- Fix OBV so OBV[t] = OBV[t-1] when Close[t] == Close[t-1].
- Fix dead links in documentation.
#-#-#-#-#-#-#-#-#-# Changes in TTR version 0.22-0 #-#-#-#-#-#-#-#-#-#
SIGNIFICANT USER-VISIBLE CHANGES
- CCI now returns an object with colnames ("cci").
- All moving average functions now attempt to set colnames.
- Added clarification on the displaced nature of DPO.
- SAR now sets the initial gap based on the standard deviation of the
high-low range instead of hard-coding it at 0.01.
NEW FEATURES
- Added rollSFM function that calculates alpha, beta, and R-squared for a
single-factor model, thanks to James Toll for the prototype.
- Added runPercentRank function, thanks to Charlie Friedemann.
- Moved slowest portion of aroon() to C.
- DonchianChannel gains an 'include.lag=FALSE' argument, which includes the
current period's data in the calculation. Setting it to TRUE replicates
the original calculation. Thanks to Garrett See and John Bollinger.
- The Stochastic Oscillator and Williams' %R now return 0.5 (instead of NaN)
when a securities' price doesn't change over a sufficient period.
- All moving average functions gain '...'.
- Users can now change alpha in Yang Zhang volatility calculation.
BUG FIXES
- Fixed MACD when maType is a list. Now mavg.slow=maType[[2]] and
mavg.fast=maType[[1]], as users expected based on the order of the nFast
and nSlow arguments. Thanks to Phani Nukala and Jonathan Roy.
- Fixed bug in lags function, thanks to Michael Weylandt.
- Corrected error in Yang Zhang volatility calculation, thanks to several
people for identifying this error.
- Correction to SAR extreme point calculations, thanks to Vamsi Galigutta.
- adjRatios now ensures all inputs are univariate, thanks to Garrett See.
- EMA and EVWMA now ensure n < number of non-NA values, thanks to Roger Bos.
- Fix to BBands docs, thanks to Evelyn Mitchell.
#-#-#-#-#-#-#-#-#-# Changes in TTR version 0.21-1 #-#-#-#-#-#-#-#-#-#
BUG FIXES
- Fixed stockSymbols for nasdaq.com changes (again), and attempted to make
stockSymbols more robust to nasdaq.com changes.
- Corrected final calculation in Yang-Zhang volatility, thanks to Shal Patel.
- Corrected k in Yang-Zhang volatility, thanks to Ian Rayner.
- Corrected s2o and s2c in Yang-Zhang volatility, thanks to Ian Rayner.
- Corrected KST when input is xts (res is now * 100), thanks to Yuanwei.
#-#-#-#-#-#-#-#-#-# Changes in TTR version 0.21-0 #-#-#-#-#-#-#-#-#-#
NEW FEATURES
- Added variable moving average function, VMA.
- Added Brian Peterson's price bands function, PBands.
- Added David Varadi's DVI indicator, DVI.
- Added wilder and ratio arguments to DEMA. Thanks to Matthew Fornari for
the suggestion.
BUG FIXES
- Changed wilderSum to seed initial value with raw sum. This matches
Wilder's original calculations. Thanks to Mahesh Bp for the report.
- The BBands sd calculation now uses the population instead of sample
statistic. This is consistent with Bollinger Band literature. Thanks to
Jeff Ryan for the patch.
- Fixed stockSymbols for nasdaq.com changes.
- Fixed ZLEMA default ratio by changing it from 2/(n-1) to 2/(n+1). This
makes it consistent with EMA. Thanks to Dirk Eddelbuettel.
- Corrected close-to-close volatility. Thanks to James Toll for the report.
- adjRatios failed (spectacularly) if there were missing close prices.
Thanks to Garrett See for the report.
#-#-#-#-#-#-#-#-#-# Changes in TTR version 0.20-2 #-#-#-#-#-#-#-#-#-#
NEW FEATURES
- Added VWAP and VWMA, thanks to Brian Peterson.
- Added v-factor generalization to DEMA, thanks to John Gavin.
- Updated volatility() to handle univariate case of calc='close', thanks
to Cedrick Johnson.
- Moved EMA, SAR, and wilderSum from .Fortran to .Call and used
xts:::naCheck in lieu of TTR's NA check mechanism.
- RSI up/down momentum now faster with xts, thanks to Jeff Ryan.
- If 'ratio' is specified in EMA but 'n' is missing, the traditional
value of 'n' is approximated and returned as the first non-NA value.
BUG FIXES
- Fix to stoch() when maType is a list and 'n' is not set in the list's
3rd element, thanks to Wind Me.
- Fixed fastK in stoch() when smooth != 1.
- Fixed segfault caused by EMA when n < NROW(x), thanks to Douglas Hobbs.
- test.EMA.wilder failed under R-devel, thanks to Prof Brian Ripley.
#-#-#-#-#-#-#-#-#-# Changes in TTR version 0.20-1 #-#-#-#-#-#-#-#-#-#
NEW FEATURES
- Updated CMO, DPO, DonchianChannel, RSI, and TDI to *explicitly*
use xts internally.
BUG FIXES
- Fixed bug in WMA, EVWMA, ZLEMA, and GMMA; results were not being
reclassed back to their original class.
- Set colnames after cbind call in the following functions: ADX, aroon,
ATR, BBands, DonchianChannel, EMV, KST, MACD, stoch, SMI, TDI, TRIX.
- Fixed bug in VHF; missing abs() calculation in the denominator.
Thanks to Jürgen Wurzer for the report!
#-#-#-#-#-#-#-#-#-# Changes in TTR version 0.20-0 #-#-#-#-#-#-#-#-#-#
NEW FEATURES
- adjRatios() creates split and/or dividend adjustment ratio series via
C code.
- GMMA() calculates the Guppy Multiple Moving Average.
- volatility() now has Yang Zhang, and Garman-Klass (Yang Zhang)
calculations.
- The functions below now have cumulative argument. This allows the
calculation of "from inception" running series.
- runSum, runMin, runMax
- runMean, runMedian
- runCov, runCor, runVar, runSD, runMAD
- Added internal smoothing to FastK in stoch() via 'smooth' argument,
thanks to Stanley Neo.
- getYahooData() now uses adjRatios(), which yields significant speed
improvements for larger data sets.
BUG FIXES
- Fixed version number; 0.20-0 is now > 0.14-0 (rookie mistake).
- Fixed bug when maType was a list and 'n' was not specified in maType.
This affected: stoch(), SMI(), RSI(), KST(), MACD(), TRIX().
#-#-#-#-#-#-#-#-#-# Changes in TTR version 0.2-0 #-#-#-#-#-#-#-#-#-#
SIGNIFICANT USER-VISIBLE CHANGES
- getYahooData() now returns an xts object.
- Added colnames to output for ADX, EMV, and CLV (for xts).
- momentum() in CMO() no longer sets na=100.
- Replaced 'na' argument in momentum() and ROC() with 'na.pad'.
- Moved maType argument default values from function formals to
function body for the following functions:
ADX, ATR, CCI, DPO, EMV, KST, MACD, RSI, TRIX, BBands,
chaikinVolatility, stoch, SMI
NEW FEATURES
- All functions now use xts internally, adding support for all major time
series classes. If try.xts() fails on the input object(s), they will be
converted to a matrix and a matrix object will be returned.
- Added 'bounded' arg to stoch() and SMI(), which includes the current
period in the calculation.
- Added the zig zag indicator: ZigZag().
- Added volatility estimators/indicators: volatility(), with the following
calculations:
- Close-to-Close
- Garman Klass
- Parkinson
- Rogers Satchell
- Added Money Flow Index: MFI().
- Added Donchian channel: DonchianChannel().
- Added 'multiple' argument to TDI(), allowing more user control.
- Added naCheck() and implemented it in the moving average functions.
BUG FIXES
- Corrected NaN replacement in CLV().
- Corrected williamsAD(): AD=0 if C(t)=C(t-1).
- Corrected runMedian() and runMAD(). The argument controlling which type
of median to calculate for even-numbered samples wasn't being passed to
the Fortran routine.
- aroon() calculation starts at period n+1, instead of n.
- Added NA to first element of closeLag of ATR().
- Corrected BBands() and CCI() for rowMeans use on xts objects.
- Made changes to Rd files to pass R CMD check on R-devel (2.9.0).
#-#-#-#-#-#-#-#-#-# Changes in TTR version 0.14-0 #-#-#-#-#-#-#-#-#-#
SIGNIFICANT USER-VISIBLE CHANGES
- Changed default 'type' of 'ROC' to 'continuous'.
- Changed 'BBands' %B output value from 'pct.b' to 'pctB'.
- Changed 'WPR' output value from 'pct.R' to 'pctR'.
- Changed 'WPR' MA output value from 'ma.emv' to 'emvMA'.
- Changed 'aroon' output values from 'aroon.xx' to 'aroonXx'.
- Renamed:
'chaikinMF' to 'CMF'
'stochastic' to 'stoch'
'bollingerBands' to 'BBands'
- Set 'na=NA' for 'momentum' and 'ROC' functions in files KST.R,
RSI.R, and TDI.R, and changed 'ROC' to use 'type="discrete"'
in 'chaikinVolatility.R'
- Made the following changes to the 'ZLEMA' function:
- Add ratio argument with default = NULL
- Non-integer lags are a weighted mean of the two nearest
observations, based on thier proximity to the lag value
- Change 'lag = ratio^(-1)' to fully support 'ratio' argument
- Changed the 'BBands' function's 'sd' argument from a list
that allows other dispersion functions to simply indicate the
number of standard deviations to use
NEW FEATURES
- Changed MA-type args and updated documentation for: RSIm ADX, ATR,
CCI, DPO, EMV, RSI, BBands, chaikinVolatility, stoch, SMI, TRIX,
MACD, and KST.
- Added Stochastic Momentum Index (SMI) and williamsAD functions
and documentation.
- Added Fortran implementations of SMA, EMA, WMA, EVWMA, ZLEMA, PSAR.
- Added NA checking/handling for many functions.
- Added 'ratio' argument to EMA with default=NULL.
- Changed all usage of 'rollFun' to their respective Fortran
implementations and removed the 'rollFun' function. Added Fortran
based functions are: runSum, wilderSum, runMin, runMax, runMean,
runCov, runCor, runVar, runSD, runMedian, runMAD.
- Changed 'CCI' to use 'runMAD' internally.
DEPRECATED & DEFUNCT
- Removed 'oscillator' function and transferred functionality
to 'MACD' function.
- Removed chaikinOscillator, since it can be created via
MACD(chaikinAD(...)).
BUG FIXES
- match.arg(type) in ROC changed to simple subsetting of type.
- Changed trailing zeros to trailing NAs in DPO.
- Fixed 'WMA' bug that allowed 'x' and 'wts' vectors to have different
length if either series had leading NAs (similar to EVWMA function).
- Fixed 'runCov' bug that allowed 'x' and 'y' vectors to have different
length if either series had leading NAs (similar to EVWMA function).
- Corrected EVWMA to start at period 'n' instead of 'n-1'.
- Removed 'message' function from CCI.R, VHF.R, WPR.R, aroon.R
bollingerBands.R, and stochastics.R.
#-#-#-#-#-#-#-#-#-# Changes in TTR version 0.13-2 #-#-#-#-#-#-#-#-#-#
SIGNIFICANT USER-VISIBLE CHANGES
- Changed order of oscillator() arguments from 'ma.slow, ma.fast, ma.sig' to
the traditional 'ma.fast, ma.slow, ma.sig'. Thanks to Jeff Ryan.
- The arguments to the chaikinOscillator function were changed as above.
- Changed EVWMA so period n contains the value for periods (i-n+1):n and
so periods 1:(n-2) will be NA.
- Changed EMA so periods 1:n will be NA.
#-#-#-#-#-#-#-#-#-# Changes in TTR version 0.13-1 #-#-#-#-#-#-#-#-#-#
SIGNIFICANT USER-VISIBLE CHANGES
- Changed 'bbands()' to 'bollingerBands()'
- Changed 'DX()' to 'ADX()'
- Changed 'stoch()' to 'stochastic()'
BUG FIXES
- Corrected mis-spellings in documentation.
#-#-#-#-#-#-#-#-#-#
SIGNIFICANT USER-VISIBLE CHANGES
NEW FEATURES
DEPRECATED & DEFUNCT
BUG FIXES