I am modelling a Vanilla Interest Rate Swap using the "RQuantLib" Package. I am following the example given in the Cran Paper "RQuantLib". For the Fixed Leg of the Interest Rate Swap, the given R code in the example is;
bond <- list(faceAmount=100,
issueDate=as.Date("2004-11-30"),
maturityDate=as.Date("2008-11-30"),
redemption=100,
effectiveDate=as.Date("2004-11-30"))
dateparams <- list(settlementDays=1,
calendar="us", dayCounter = 'Thirty360', period=2,
businessDayConvention = 4, terminationDateConvention=4,
dateGeneration=1, endOfMonth=1)
coupon.rate <- c(0.02875)
params <- list(tradeDate=as.Date('2002-2-15'),
settleDate=as.Date('2002-2-19'),
dt=.25,
interpWhat="discount",
interpHow="loglinear")
setEvaluationDate(as.Date("2004-11-22"))
discountCurve.flat <- DiscountCurve(params, list(flat=0.05))
FixedRateBond(bond, coupon.rate, discountCurve.flat, dateparams)
#Same bond with a discount curve constructed from market quotes
tsQuotes <- list(d1w =0.0382,
d1m =0.0372,
fut1=96.2875,
fut2=96.7875,
fut3=96.9875,
fut4=96.6875,
fut5=96.4875,
fut6=96.3875,
fut7=96.2875,
fut8=96.0875,
s3y =0.0398,
s5y =0.0443,
s10y =0.05165,
s15y =0.055175)
discountCurve <- DiscountCurve(params, tsQuotes)
FixedRateBond(bond, coupon.rate, discountCurve, dateparams)
#example with default dateparams
FixedRateBond(bond, coupon.rate, discountCurve)
##exampe with defaul bond parameter and dateparams
bond <- list(issueDate=as.Date("2004-11-30"),
maturityDate=as.Date("2008-11-30"))
dateparams <- list(calendar="us",
dayCounter = "ActualActual",
period="Annual")
FixedRateBond(bond, coupon.rate, discountCurve, dateparams)
However, in this R Code the following transcripts are showing errors;
setEvaluationDate(as.Date("2004-11-22"))
discountCurve.flat <- DiscountCurve(params, list(flat=0.05))
and the errors are as below;
> setEvaluationDate(as.Date("2004-11-22"))
Error: could not find function "setEvaluationDate"
> discountCurve.flat <- DiscountCurve(params, list(flat=0.05))
Error: could not find function "DiscountCurve"
I have tried to investigate why the R Code is failing to compile but I failed. Can anyone assist please?
Maybe you didn't load the package :
R> library(RQuantLib)
R> setEvaluationDate(Sys.Date())
[1] TRUE
R>
Related
I am having a small issue with my Rstudio code. I will try to replicate my code but unfortunately there is no easy data for me to show. This is about the package forecast. What I am looking for is somehwat simpler for what is in the manual. But unfortunately, I am not able to work round it.
so the issue is with an expanding window forecast. So I have a dependent variable Y and 3 regressors (X). I am trying to build a recursive one steap ahead forecast for each X.
Here is my code.
library(forecast)
library(zoo)
library(timeDate)
library(xts)
## Load data
data = Dataset[,2:ncol(Dataset)]
st <- as.Date("1990-1-1")
en <- as.Date("2020-12-1")
tt <- seq(st, en, by = "1 month")
data = xts(data, order.by=tt)
##########################################################################
RECFORECAST=function (Y,X,h,window){
st <- as.Date("1990-1-1")
en <- as.Date("2020-12-1")
tt <- seq(st, en, by = "1 month")
datas= cbind(Y,X)
newfcast= matrix(0,nrow(datas),h)
for (k in 1:nrow(datas)){
sample =datas[1:(window+k-1),]
# print(sample)
v= window+k
# print(v)
# fit = Arima(sample[,1], order=c(0,0,0),xreg=sample[,2])
fit = lm(sample[,1]~sample[,2], data = sample)
# fcast=forecast(fit,xreg=rep(sample[v,2],h))$mean
fcast = forecast.lm(fit,sample[v,2],h=1)$mean
print(fcast)
# print(fcast)
# newfcast[k+window+1,]=fcast
}
print(newfcast)
return(newfcast)
}
## Code to send the loop into forecasts
StoreMatrix = data$growth ## This is the first column data[,1]
for (i in 2:4)
{
try({
X=data[,i]
Y=data[,1]
RecModel=RECFORECAST(Y,X,h=1,window=60) ##Here the initial window is 60 obs
StoreMatrix=cbind(StoreMatrix,RecModel)
print(StoreMatrix)
}, silent=T)
}
The bits # were different ways I tried to crosscheck my data and they may not be useful. I have tried so many things but I don't seem to be able to get my head through it. At the end I want to have a matrix (StoreMatrix) with the first variable being the realization, and each of the columns with the corresponding 1 step ahead forecast.
The main lines where there seems to be an issue are these ones:
# fcast=forecast(fit,xreg=rep(sample[v,2],h))$mean
fcast = forecast.lm(fit,sample[v,2],h=1)$mean
Note sure how to solve this. Thank you very much.
Glad to have found this community!
As a beginning project, I wish to examine what happens when you open a position when the price is above an SMA and close it once the price is below.
I am currently working on a script in R as follows:
`##############################
# 0 - Load libraries
##############################
require(quantmod)
require(PerformanceAnalytics)
require(TTR)
# Step 1: Get the data
loadSymbols("^GSPC")
# Step 2: Create your indicator
SMA <- SMA(x = (GSPC), n = 200)
# Step 3: Construct your trading rule
signal <- Lag(ifelse(SMA$SMA > CLV(GSPC), 1, -1))
# Step 4: The trading rules/equity curve
ret <- ROC(Cl(GSPC))*signal
ret <- ret[2009-06-02/2020-09-07]
eq <- exp(cumsum(ret))
plot(eq)
# Step 5: Evaluate strategy performance
table.Drawdowns(ret, top=10)
table.DownsideRisk(ret)
charts.PerformanceSummary(ret)
plot(cl(GSPC))
lines(sma, col = "Blue")`
The script does not produce any output when I run it. I have re-installed all packages and I am running R version 3.6.
Please can somebody tell me why?
I am currently working on retrieving mass queries from Google Trends through the package gtrendsr in R. In order to do that, I have first imported a list of words (454 words) as a CSV file in to R and initally declared a vector of these words denoted as "a".
df <- data.frame()
getSeries <- function(a){
out <- NULL
for(i in length(a)){
trend1 <- gtrends(i, geo = "US", time = "2009-01-01 2009-02-01")
trend2 <- gtrends(i, geo = "US", time = "2009-03-01 2009-04-01")
vec <- rbind(as.matrix(trend1),as.matrix(trend2))
out <- cbind(out,vec)
}
return(out)
Sys.sleep(10)
}
getSeries(a)
By applying this Code, I receive the following error message:
Error: widget$status_code == 200 is not TRUE
I am quite a noob in R, but I assume that the error above has something to do with the mass queries I am requesting through this code. Did somebody already have faced this issue and came up with a solution?
I am using the the developer package of gtrendsR:
if (!require("devtools")) install.packages("devtools")
devtools::install_github('PMassicotte/gtrendsR')
I am looking forward to all of your Support. Thanks :)
I am trying to estimate the static yield curve for Brazil using termstrc package in R. I am using the function estim_nss.couponbonds and putting 0% coupon-rates and $0 cash-flows, except for the last one which is $1000 (the face-value at maturity) -- as far as I know this is the function to do this, because the estim_nss.zeroyields only calculates the dynamic curve. The problem is that I receive the following error message:
"Error in (pos_cf[i] + 1):pos_cf[i + 1] : NA/NaN argument In addition: Warning message: In max(n_of_cf) : no non-missing arguments to max; returning -Inf "
I've tried to trace the problem using trace(estim_nss.couponbons, edit=T) but I cannot find where pos_cf[i]+1 is calculated. Based on the name I figured it could come from the postpro_bondfunction and used trace(postpro_bond, edit=T), but I couldn't find the calculation again. I believe "cf" comes from cashflow, so there could be some problem in the calculation of the cashflows somehow. I used create_cashflows_matrix to test this theory, but it works well, so I am not sure the problem is in the cashflows.
The code is:
#Creating the 'couponbond' class
ISIN <- as.character(c('ltn_2017','ltn_2018', 'ltn_2019', 'ltn_2021','ltn_2023')) #Bond's identification
MATURITYDATE <- as.Date(c(42736, 43101, 43466, 44197, 44927), origin='1899-12-30') #Dates are in system's format
ISSUEDATE <- as.Date(c(41288,41666,42395, 42073, 42395), origin='1899-12-30') #Dates are in system's format
COUPONRATE <- rep(0,5) #Coupon rates are 0 because these are zero-coupon bonds
PRICE <- c(969.32, 867.77, 782.48, 628.43, 501.95) #Prices seen 'TODAY'
ACCRUED <- rep(0.1,5) #There is no accrued interest in the brazilian bond's market
#Creating the cashflows sublist
CFISIN <- as.character(c('ltn_2017','ltn_2018', 'ltn_2019', 'ltn_2021', 'ltn_2023')) #Bond's identification
CF <- c(1000,1000,1000,1000,1000)# The face-values
DATE <- as.Date(c(42736, 43101, 43466, 44197, 44927), origin='1899-12-30') #Dates are in system's format
CASHFLOWS <- list(CFISIN,CF,DATE)
names(CASHFLOWS) <- c("ISIN","CF","DATE")
TODAY <- as.Date(42646, origin='1899-12-30')
brasil <- list(ISIN,MATURITYDATE,ISSUEDATE,
COUPONRATE,PRICE,ACCRUED,CASHFLOWS,TODAY)
names(brasil) <- c("ISIN","MATURITYDATE","ISSUEDATE","COUPONRATE",
"PRICE","ACCRUED","CASHFLOWS","TODAY")
mybonds <- list(brasil)
class(mybonds) <- "couponbonds"
#Estimating the zero-yield curve
ns_res <-estim_nss.couponbonds(mybonds, 'brasil' ,method = "ns")
#Testing the hypothesis that the error comes from the cashflow matrix
cf_p <- create_cashflows_matrix(mybonds[[1]], include_price = T)
m_p <- create_maturities_matrix(mybonds[[1]], include_price = T)
b <- bond_yields(cf_p,m_p)
Note that I am aware of this question which reports the same problem. However, it is for the dynamic curve. Besides that, there is no useful answer.
Your code has two problems. (1) doesn't name the 1st list (this is the direct reason of the error. But if modifiy it, another error happens). (2) In the cashflows sublist, at least one level of ISIN needs more than 1 data.
# ...
CFISIN <- as.character(c('ltn_2017','ltn_2018', 'ltn_2019',
'ltn_2021', 'ltn_2023', 'ltn_2023')) # added a 6th element
CF <- c(1000,1000,1000,1000,1000, 1000) # added a 6th
DATE <- as.Date(c(42736,43101,43466,44197,44927, 44928), origin='1899-12-30') # added a 6th
CASHFLOWS <- list(CFISIN,CF,DATE)
names(CASHFLOWS) <- c("ISIN","CF","DATE")
TODAY <- as.Date(42646, origin='1899-12-30')
brasil <- list(ISIN,MATURITYDATE,ISSUEDATE,
COUPONRATE,PRICE,ACCRUED,CASHFLOWS,TODAY)
names(brasil) <- c("ISIN","MATURITYDATE","ISSUEDATE","COUPONRATE",
"PRICE","ACCRUED","CASHFLOWS","TODAY")
mybonds <- list(brasil = brasil) # named the list
class(mybonds) <- "couponbonds"
ns_res <-estim_nss.couponbonds(mybonds, 'brasil', method = "ns")
Note: the error came from these lines
bonddata <- bonddata[group] # prepro_bond()'s 1st line (the direct reason).
# cf <- lapply(bonddata, create_cashflows_matrix) # the additional error
create_cashflows_matrix(mybonds[[1]], include_price = F) # don't run
hi i am getting this error message while installing DMwR package from RGUI-3.3.1.
Error in read.dcf(file.path(pkgname, "DESCRIPTION"), c("Package", "Type")) :
cannot open the connection
In addition: Warning messages:
1: In unzip(zipname, exdir = dest) : error 1 in extracting from zip file
2: In read.dcf(file.path(pkgname, "DESCRIPTION"), c("Package", "Type")) :
cannot open compressed file 'bitops/DESCRIPTION', probable reason 'No such file or directory'
Approach 1:
The error being reported is inability to open a connection. In Windows that is often a firewall problem and is in the Windows R FAQ. The usual first attempt should be to run internet2.dll. From a console session you can use:
setInternet2(TRUE)
NEWS for R version 3.3.1 Patched (2016-09-13 r71247)
(Windows only) Function
setInternet2()
has no effect and will be removed in due
course. The choice between methods
"internal"
and
"wininet"
is now made by the
method
arguments of
url()
and
download.file()
and their defaults can be set
via
options. The out-of-the-box default remains
"wininet"
(as it has been since
R
3.2.2)
You are using version 3.3.1, this is why it is not working anymore.
Approach 2
The error is suggesting that the package requires another package bitops that is not available. That package is not in any of the dependencies but perhaps one of the dependencies requires it in turn(In this case, it is: ROCR).
Try installing:
install.packages("bitops",repos="https://cran.r-project.org/bin/windows/contrib/3.3/bitops_1.0-6.zip",dependencies=TRUE,type="source")
The package DMwR contains packages abind, zoo, xts, quantmod and ROCR as imports. So, additionally to installing 5 packages you must install DMwR package, Install these packages manually.
Install packages in following sequence:
install.packages('abind')
install.packages('zoo')
install.packages('xts')
install.packages('quantmod')
install.packages('ROCR')
install.packages("DMwR")
library("DMwR")
Approach 3:
chooseCRANmirror()
Select CRAN mirror from popup list. Then install packages:
install.packages("bitops")
install.packages("DMwR")
Package ‘DMwR’ was removed from the CRAN repository.
Formerly available versions can be obtained from the archive.
https://CRAN.R-project.org/package=DMwR
You can use the function as written in CRAN package. Copy the following code in a new RScript, run it and save it for future use if you want. Once you run this function, you should be able to use the way you have been trying to use it.
# ===================================================
# Creating a SMOTE training sample for classification problems
#
# If called with learner=NULL (the default) is does not
# learn any model, simply returning the SMOTEd data set
#
# NOTE: It does not handle NAs!
#
# Examples:
# ms <- SMOTE(Species ~ .,iris,'setosa',perc.under=400,perc.over=300,
# learner='svm',gamma=0.001,cost=100)
# newds <- SMOTE(Species ~ .,iris,'setosa',perc.under=300,k=3,perc.over=400)
#
# L. Torgo, Feb 2010
# ---------------------------------------------------
SMOTE <- function(form,data,
perc.over=200,k=5,
perc.under=200,
learner=NULL,...
)
# INPUTS:
# form a model formula
# data the original training set (with the unbalanced distribution)
# minCl the minority class label
# per.over/100 is the number of new cases (smoted cases) generated
# for each rare case. If perc.over < 100 a single case
# is generated uniquely for a randomly selected perc.over
# of the rare cases
# k is the number of neighbours to consider as the pool from where
# the new examples are generated
# perc.under/100 is the number of "normal" cases that are randomly
# selected for each smoted case
# learner the learning system to use.
# ... any learning parameters to pass to learner
{
# the column where the target variable is
tgt <- which(names(data) == as.character(form[[2]]))
minCl <- levels(data[,tgt])[which.min(table(data[,tgt]))]
# get the cases of the minority class
minExs <- which(data[,tgt] == minCl)
# generate synthetic cases from these minExs
if (tgt < ncol(data)) {
cols <- 1:ncol(data)
cols[c(tgt,ncol(data))] <- cols[c(ncol(data),tgt)]
data <- data[,cols]
}
newExs <- smote.exs(data[minExs,],ncol(data),perc.over,k)
if (tgt < ncol(data)) {
newExs <- newExs[,cols]
data <- data[,cols]
}
# get the undersample of the "majority class" examples
selMaj <- sample((1:NROW(data))[-minExs],
as.integer((perc.under/100)*nrow(newExs)),
replace=T)
# the final data set (the undersample+the rare cases+the smoted exs)
newdataset <- rbind(data[selMaj,],data[minExs,],newExs)
# learn a model if required
if (is.null(learner)) return(newdataset)
else do.call(learner,list(form,newdataset,...))
}
# ===================================================
# Obtain a set of smoted examples for a set of rare cases.
# L. Torgo, Feb 2010
# ---------------------------------------------------
smote.exs <- function(data,tgt,N,k)
# INPUTS:
# data are the rare cases (the minority "class" cases)
# tgt is the name of the target variable
# N is the percentage of over-sampling to carry out;
# and k is the number of nearest neighours to use for the generation
# OUTPUTS:
# The result of the function is a (N/100)*T set of generated
# examples with rare values on the target
{
nomatr <- c()
T <- matrix(nrow=dim(data)[1],ncol=dim(data)[2]-1)
for(col in seq.int(dim(T)[2]))
if (class(data[,col]) %in% c('factor','character')) {
T[,col] <- as.integer(data[,col])
nomatr <- c(nomatr,col)
} else T[,col] <- data[,col]
if (N < 100) { # only a percentage of the T cases will be SMOTEd
nT <- NROW(T)
idx <- sample(1:nT,as.integer((N/100)*nT))
T <- T[idx,]
N <- 100
}
p <- dim(T)[2]
nT <- dim(T)[1]
ranges <- apply(T,2,max)-apply(T,2,min)
nexs <- as.integer(N/100) # this is the number of artificial exs generated
# for each member of T
new <- matrix(nrow=nexs*nT,ncol=p) # the new cases
for(i in 1:nT) {
# the k NNs of case T[i,]
xd <- scale(T,T[i,],ranges)
for(a in nomatr) xd[,a] <- xd[,a]==0
dd <- drop(xd^2 %*% rep(1, ncol(xd)))
kNNs <- order(dd)[2:(k+1)]
for(n in 1:nexs) {
# select randomly one of the k NNs
neig <- sample(1:k,1)
ex <- vector(length=ncol(T))
# the attribute values of the generated case
difs <- T[kNNs[neig],]-T[i,]
new[(i-1)*nexs+n,] <- T[i,]+runif(1)*difs
for(a in nomatr)
new[(i-1)*nexs+n,a] <- c(T[kNNs[neig],a],T[i,a])[1+round(runif(1),0)]
}
}
newCases <- data.frame(new)
for(a in nomatr)
newCases[,a] <- factor(newCases[,a],levels=1:nlevels(data[,a]),labels=levels(data[,a]))
newCases[,tgt] <- factor(rep(data[1,tgt],nrow(newCases)),levels=levels(data[,tgt]))
colnames(newCases) <- colnames(data)
newCases
}
It has been removed from the CRAN library. There are instructions on how to retrieve it from the archive.
Either follow the link - https://packagemanager.rstudio.com/client/#/repos/2/packages/DMwR
OR copy-paste the three lines of code mentioned below:
install.packages("devtools")
devtools::install_version('DMwR', '0.4.1')
library("DMwR")
EDIT: this is the error I got while downloading the DMwR package in 2022, but looks like when the question was posted, the error happened because of another reason.
The reason is that the package 'DMwR' was built under R version 3.4.3 So the solution is actually explained in the marked answer in details. Hence, to be short:Just run the script below to get the problem solved!
install.packages('abind')
install.packages('zoo')
install.packages('xts')
install.packages('quantmod')
install.packages('ROCR')
install.packages("DMwR")
library("DMwR")