# R: Calculating cumulative return of a portfolio

I've downloaded adjusted closing prices from Yahoo using the quantmod-package, and used that to create a portfolio consisting of 50% AAPL- and 50% FB-stocks.

When I plot the cumulative performance of my portfolio, I get a performance that is (suspiciously) high as it is above 100%:

library(ggplot2)
library(quantmod)

cmp <- "AAPL"
getSymbols(Symbols = cmp)
tail(AAPL$AAPL.Adjusted) cmp <- "FB" getSymbols(Symbols = cmp) tail(FB$FB.Adjusted)

df <- data.frame("AAPL" = tail(AAPL$$AAPL.Adjusted, 1000), "FB" = tail(FB$$FB.Adjusted, 1000))

for(i in 2:nrow(df)){
df$$AAPL.Adjusted_prc[i] <- df$$AAPL.Adjusted[i]/df$$AAPL.Adjusted[i-1]-1 df$$FB.Adjusted_prc[i] <- df$$FB.Adjusted[i]/df$$FB.Adjusted[i-1]-1
}

df <- df[-1,]
df$$portfolio <- (df$$AAPL.Adjusted_prc + df$$FB.Adjusted_prc)*0.5 df$$performance <- cumprod(df$$portfolio+1)-1 df$$idu <- as.Date(row.names(df))

ggplot(data = df, aes(x = idu, y = performance)) + geom_line()


A cumulative performance above 100% seems very unrealistic to me. This lead me to think that maybe it is necessary to adjust/scale the downloaded data from quantmod before using it?

Have you checked the performance of the particular stocks?

library("quantmod")
library("PMwR")

cmp <- "AAPL"
aapl <- getSymbols(Symbols = cmp, auto.assign = FALSE)$AAPL.Adjusted cmp <- "FB" fb <- getSymbols(Symbols = cmp, auto.assign = FALSE)$FB.Adjusted

returns(window(merge(aapl, fb), start = as.Date("2015-1-1")),
period = "itd")
## AAPL.Adjusted:  73.2%  [02 Jan 2015 -- 04 Mar 2019]
##   FB.Adjusted: 113.3%  [02 Jan 2015 -- 04 Mar 2019]


So this seems quite realistic (and you may verify this performance via other sources as well). However, you should properly merge the time-series on their timestamps. Also, the portfolio performance you compute assumes that you rebalance to equal weights every period (i.e. day).