How can I fix both the open and close data using quantmod's periodReturns?

Question

I am retrieving data from yahoo using the getSymbols to get then monthly returns. But I want to subset the data, I want to have monthly returns from January 2016 until May 2017. I am running this:

library(quantmod)
list <- c("AAL","AAPL","ADBE","AMZN", "FB", "GOOGL", "MSFT", "NFLX", "TSLA", "VOD")

df <- data.frame()

##get monthly returns from january 2016 until today

for (stock in list){

 data <- monthlyReturn(to.monthly(getSymbols(stock, auto.assign = FALSE)),from='2016-01-01', to='2017-05-31' )

      colnames(data)[1] <- paste('r_', stock, sep = "")

      df <- cbind(df, data)

}

df <- as.data.frame(df)
attach(df)

And it is not working as I get values from the entire dataset. Instead if I use this:

for (stock in list){

 data <- monthlyReturn(to.monthly(getSymbols(stock, auto.assign = FALSE)),subset = "2016::")

 colnames(data)[1] <- paste('r_', stock, sep = "")

  df <- cbind(df, data)

}

df <- as.data.frame(df)

I get monthly data from January 2016 until today, but that's not what I want as I want to fix the close date too. Any ideas on how to solve that?

Answer 1

Is this what you would like to get:

lst <- c("AAL","AAPL","ADBE","AMZN", "FB", "GOOGL", "MSFT", "NFLX", "TSLA", "VOD")

getSymbols(lst, from = "2016-01-01", to = "2017-05-31")
P <- NULL
seltickers <- NULL

for(stock in lst) {     
  tmp = monthlyReturn(Cl(to.monthly(eval(parse(text = stock))))) ## Ad from quantmod
  if(is.null(P)){ timeP = time(tmp) }
  if(any(time(tmp)!=timeP)) next
  else P = cbind(P, as.numeric(tmp))
  seltickers = c(seltickers, stock)
  P = xts(P, order.by = timeP)
  colnames(P) = seltickers
}

head(P)

                   AAL         AAPL        ADBE         AMZN          FB
Jan 2016  0.0000000000  0.000000000  0.00000000  0.000000000  0.00000000
Feb 2016  0.0515516260 -0.006677563 -0.04465382 -0.058739319 -0.04714376
Mar 2016  0.0002438537  0.127210629  0.10158545  0.074422634  0.06715301
Apr 2016 -0.1541087371 -0.139921096  0.00447759  0.111094283  0.03049960
May 2016 -0.0801383419  0.065286997  0.05572065  0.095817020  0.01046093
Jun 2016 -0.1128173300 -0.042659753 -0.03699608 -0.009919871 -0.03812810
               GOOGL        MSFT        NFLX         TSLA           AAL
Jan 2016  0.00000000  0.00000000  0.00000000  0.000000000  0.0000000000
Feb 2016 -0.05796284 -0.07642038  0.01709504  0.003817971  0.0515516260
Mar 2016  0.06369044  0.08549526  0.09442242  0.197155277  0.0002438537
Apr 2016 -0.07211957 -0.09704872 -0.11933878  0.047830399 -0.1541087371
May 2016  0.05787700  0.06276321  0.13928692 -0.072811096 -0.0801383419
Jun 2016 -0.06051939 -0.03452834 -0.10812125 -0.049052534 -0.1128173300
                 AAPL        ADBE         AMZN          FB       GOOGL
Jan 2016  0.000000000  0.00000000  0.000000000  0.00000000  0.00000000
Feb 2016 -0.006677563 -0.04465382 -0.058739319 -0.04714376 -0.05796284
Mar 2016  0.127210629  0.10158545  0.074422634  0.06715301  0.06369044
Apr 2016 -0.139921096  0.00447759  0.111094283  0.03049960 -0.07211957
May 2016  0.065286997  0.05572065  0.095817020  0.01046093  0.05787700
Jun 2016 -0.042659753 -0.03699608 -0.009919871 -0.03812810 -0.06051939
                MSFT        NFLX         TSLA           AAL         AAPL
Jan 2016  0.00000000  0.00000000  0.000000000  0.0000000000  0.000000000
Feb 2016 -0.07642038  0.01709504  0.003817971  0.0515516260 -0.006677563
Mar 2016  0.08549526  0.09442242  0.197155277  0.0002438537  0.127210629
Apr 2016 -0.09704872 -0.11933878  0.047830399 -0.1541087371 -0.139921096
May 2016  0.06276321  0.13928692 -0.072811096 -0.0801383419  0.065286997
Jun 2016 -0.03452834 -0.10812125 -0.049052534 -0.1128173300 -0.042659753
                ADBE         AMZN          FB       GOOGL        MSFT
Jan 2016  0.00000000  0.000000000  0.00000000  0.00000000  0.00000000
Feb 2016 -0.04465382 -0.058739319 -0.04714376 -0.05796284 -0.07642038
Mar 2016  0.10158545  0.074422634  0.06715301  0.06369044  0.08549526
Apr 2016  0.00447759  0.111094283  0.03049960 -0.07211957 -0.09704872
May 2016  0.05572065  0.095817020  0.01046093  0.05787700  0.06276321
Jun 2016 -0.03699608 -0.009919871 -0.03812810 -0.06051939 -0.03452834
                NFLX         TSLA
Jan 2016  0.00000000  0.000000000
Feb 2016  0.01709504  0.003817971
Mar 2016  0.09442242  0.197155277
Apr 2016 -0.11933878  0.047830399
May 2016  0.13928692 -0.072811096
Jun 2016 -0.10812125 -0.049052534

These are monthly returns on Close price. I am not sure if you want returns on Open price as well.