I'm not a programmer by any means and have been trying to learn R to code various trading strategies. I'm trying to calculate the relative performance of a list of stocks versus the S&P 500 and save it to a matrix. It appears that what I've written only goes through the first symbol and then stops. Below is the code that I've come up with. I appreciate any help, input and advice on how to proceed. Thank you.
library(quantmod)
library(PerformanceAnalytics)
Sys.setenv(TZ = "UTC")
symbols <- c('IBM', 'GE', '^GSPC')
getSymbols(symbols, src = "yahoo", from = "2010-12-31", to = Sys.Date())
symadj <- cbind(IBM[,6], GE[,6])
sp5adj <- GSPC[,6]
# Calculate Relative Performance vs S&P and save data
for (i in length(symadj)) {
rp <- matrix(symadj[,1]/sp5adj, nrow = 1070, ncol = 3)
print(tail(rp))
}
_You are not looping over an array but over a single number:
for (i in length(symadj))
Try (see the seq added, watch the parenthesis. Plus, be careful with length, the iteration is over ncol - i.e. the columns):
for (i in seq(1,ncol(rp),1))
_Also, you are going always through the same column:
rp <- matrix(symadj[,1]/sp5adj, nrow = 1070, ncol = 3)
_A thing I skipped: you should build your matrix before the loop:
rp <- matrix(0,nrow=1071,ncol=2)
And then assign without overwritting your previous matrix - you have already build it (plus, look at the i where the 1 was, now you are iterating)
rp[,i] <- symadj[,i]/sp5adj #This inside the loop
_Your for loop should end up looking something like this:
rp <- matrix(0,nrow=1071,ncol=2)
for (i in seq(1,ncol(rp),1)) {
rp[,i] <- symadj[,i]/sp5adj #This inside the loop
print(tail(rp))
}
\!/ Now there are 1071 days in that period, so the matrix should have one more row - that's why the 1071.
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)$mean
Note sure how to solve this. Thank you very much.
I have 24 variables called empl_1 -empl_24 (e.g. empl_2; empl_3..)
I would like to write a loop in R that takes this values 1-24 and puts them in the respective places so the corresponding variables are either called or created with i = 1-24. The sample below shows what I would like to have within the loop (e.g. ye1- ye24; ipw_atet_1 - ipw_atet_14 and so on.
ye1_ipw <- empl$empl_1[insample==1]
ipw_atet_1 <- treatweight(y=ye1_ipw, d=treat_ipw, x=x1_ipw, ATET =TRUE, trim=0.05, boot = 2)
ipw_atet_1
ipw_atet_1$se
ye2_ipw <- empl$empl_2[insample==1]
ipw_atet_2 <- treatweight(y=ye2_ipw, d=treat_ipw, x=x1_ipw, ATET =TRUE, trim=0.05, boot = 2)
ipw_atet_2
ipw_atet_2$se
ye3_ipw <- empl$empl_3[insample==1]
ipw_atet_3 <- treatweight(y=ye3_ipw, d=treat_ipw, x=x1_ipw, ATET =TRUE, trim=0.05, boot = 2)
ipw_atet_3
ipw_atet_3$se
coming from a Stata environment I tried
for (i in seq_anlong(empl_list)){
ye[i]_ipw <- empl$empl_[i][insample==1]
ipw_atet_[i]<-treatweight(y=ye[i]_ipw, d=treat_ipw, x=x1_ipw, ATET=TRUE, trim=0.05, boot =2
}
However this does not work at all. Do you have any idea how to approach this problem by writing a nice loop? Thank you so much for your help =)
You can try with lapply :
result <- lapply(empl[paste0('empl_', 1:24)], function(x)
treatweight(y = x[insample==1], d = treat_ipw,
x = x1_ipw, ATET = TRUE, trim = 0.05, boot = 2))
result would be a list output storing the data of all the 24 variables in same object which is easier to manage and process instead of having different vectors.
I have 1000 csv files in my working directory and each file has a location Id, rainfall and temperature. The structure of one file is shown below:
set.seed(123)
my.dat <- data.frame(Id = rep(1, each = 365),
rain = runif(365, min = 0, max = 20),
tmean = sample(20:40, 365, replace = T))
I wrote an Rcpp function that is also stored in my working directory. This function takes in rainfall and temperature data and calculates some derived variables var1 andvar2. I want to read each location's weather data and apply the function and save the corresponding output using foreach package.
location.vec <- 1:1000
myClusters <- makeCluster(6)
registerDoParallel(myClusters)
foreach(i = 1:length(location.vec),
.packages = c('Rcpp', 'dplyr', 'data.table'),
.noexport = c('myRcppFunc'),
.verbose = T) %dopar%
{
Rcpp::sourceCpp('myRcppFunc.cpp')
idRef <- location.vec[i]
# read the weather data
temp_weather <- fread(paste0('weather_',idRef,'.csv'))
# apply my Rcpp function
temp_weather[, c("var1","var2") := myRcppFunc(rain, tmean)]
# save my output
fwrite(temp_weather, 'paste0('weather_',idRef_modified,'.csv')')
}
stopCluster(myClusters)
This loop seems to have a weird behaviour. Sometimes it just gets stuck on iteration 10, sometimes on 40 etc everytime I run it and then I have to kill the job.
My doubt is this driven by the fact that multiple process are trying to access the Rcpp function at the same time which is leading to this issue? How can I fix it? Can I read in the Rcpp function in the foreach argument so that I don't have to keep loading it? Any other advise?
Thanks
I am trying to do some quantitative modeling in R. I'm not getting an error message, but the results are not what I actually need.
I am a newbie, but here is my complete code sample.
`library(quantmod)
#Building the data frame and xts to show dividends, splits and technical indicators
getSymbols(c("AMZN"))
Playground <- data.frame(AMZN)
Playground$date <- as.Date(row.names(Playground))
Playground$wday <- as.POSIXlt(Playground$date)$wday #day of the week
Playground$yday <- as.POSIXlt(Playground$date)$mday #day of the month
Playground$mon <- as.POSIXlt(Playground$date)$mon #month of the year
Playground$RSI <- RSI(Playground$AMZN.Adjusted, n = 5, maType="EMA") #can add Moving Average Type with maType =
Playground$MACD <- MACD(AMZN, nFast = 12, nSlow = 26, nSig = 9)
Playground$Div <- getDividends('AMZN', from = "2007-01-01", to = Sys.Date(), src = "google", auto.assign = FALSE)
Playground$Split <- getSplits('AMZN', from = "2007-01-01", to = Sys.Date(), src = "google", auto.assign = FALSE)
Playground$BuySignal <- ifelse(Playground$RSI < 30 & Playground$MACD < 0, "Buy", "Hold")
All is well up until this point when I start using some logical conditions to come up with decision points.
Playground$boughts <- ifelse(Playground$BuySignal == "Buy", lag(Playground$boughts) + 1000, lag(Playground$boughts))
It will execute but the result will be nothing but NA. I suppose this is because you are trying to add NA to a number, but I'm not 100% sure. How do you tell the computer I want you to keep a running tally of how much you have bought?
Thanks so much for the help.
So we want ot buy 1000 shares every time a buy signal is generated?
Your problem stems from MACD idicator. It actually generates two columns, macd and signal. You have to decide which one you want to keep.
Playground$MACD <- MACD(AMZN, nFast = 12, nSlow = 26, nSig = 9)$signal
This should solve the problem at hand.
Also, please check the reference for ifelse. The class of return value can be tricky at times, and so the approach suggested by Floo0 is preferable.
Also, I'd advocate using 1 and 0 instead of buy and sell to show weather you are holding . It makes the math much easier.
And I'd strongly suggest reading some beginner tutorial on backtesting with PerformanceAnalytics. They make the going much much easier.
BTW, you missed this line in the code:
Playground$boughts<- 0
Hope it helps.
EDIT: And I forgot to mention the obvious. discard the first few rows where MACD will be NA
Something like:
Playground<- Playground[-c(1:26),]
Whenever you want to do an ifelse like
if ... Do something, else stay the same: Do not use ifelse
Try this instead
ind <- which(Playground$BuySignal == "Buy")
Playground$boughts[ind] <- lag(Playground$boughts) + 1000
I am stuck with the following code.
For reference the code it is taken from the following website (http://gekkoquant.com/2013/01/21/statistical-arbitrage-trading-a-cointegrated-pair/), I am also compiling the code through R Studio.
library("quantmod")
startDate = as.Date("2013-01-01")
symbolLst<-c("WPL.AX","BHP.AX")
symbolData <- new.env()
getSymbols(symbolLst, env = symbolData, src = "yahoo", from = startDate)
stockPair <- list(
a =coredata(Cl(eval(parse(text=paste("symbolData$\"",symbolLst[1],"\"",sep="")))))
,b = coredata(Cl(eval(parse(text=paste("symbolData$\"",symbolLst[2],"\"",sep="")))))
,hedgeRatio = 0.70 ,name=title)
spread <- stockPair$a - stockPair$hedgeRatio*stockPair$b
I am getting the following error.
Error in stockPair$a - stockPair$hedgeRatio * stockPair$b :
non-conformable arrays
The reason these particular series don't match is because "WPL.AX" has an extra value (date:19-05-2014 - the matrix lengths are different) compared to "BHP". How can I solve this issue when loading data?
I have also tested other stock pairs such as "ANZ","WBC" with the source = "google" which produces two of the same length arrays.
> length(stockPair$a)
[1] 360
> length(stockPair$b)
[1] 359
Add code such as this prior to the stockPair computation, to trim each xts set to the intersection of dates:
common_dates <- as.Date(Reduce(intersect, eapply(symbolData, index)))
symbolData <- eapply(symbolData, `[`, i=common_dates)
Your code works fine if you don't convert your xts object to matrix via coredata. Then Ops.xts will ensure that only the rows with the same index will be subtracted. And fortune(106) applies.
fortunes::fortune(106)
# If the answer is parse() you should usually rethink the question.
# -- Thomas Lumley
# R-help (February 2005)
stockPair <- list(
a = Cl(symbolData[[symbolLst[1]]])
,b = Cl(symbolData[[symbolLst[2]]])
,hedgeRatio = 0.70
,name = "title")
spread <- stockPair$a - stockPair$hedgeRatio*stockPair$b
Here's an alternative approach:
# merge stocks into a single xts object
stockPair <- do.call(merge, eapply(symbolData, Cl))
# ensure stockPair columns are in the same order as symbolLst, since
# eapply may loop over the environment in an order you don't expect
stockPair <- stockPair[,pmatch(symbolLst, colnames(stockPair))]
colnames(stockPair) <- c("a","b")
# add hedgeRatio and name as xts attributes
xtsAttributes(stockPair) <- list(hedgeRatio=0.7, name="title")
spread <- stockPair$a - attr(stockPair,'hedgeRatio')*stockPair$b