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?
I've been really struggeling with this issue and I have the feeling that there is a data issue beneath it. To demonstrate this I'm computing cumulative returns using two approaches. The results show some differences, that I can't really explain - therfore, you might take a look at these first.
First, I running your code:
library(quantmod)
library(tidyverse
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
}
Then I am computing the cumulative returns manually, by dividing the current value by the starting value (ie the price in row #1) and subtracting 1. In addition, I'm cumsum
'ing the two stocks' returns separately.
df$aapl_man <- df$AAPL.Adjusted / df$AAPL.Adjusted[1] - 1
df$fb_man <- df$FB.Adjusted / df$FB.Adjusted[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))
df <- mutate_at(df, vars(contains("_prc")), cumsum)
Now, I am plotting the cumsum
returns (in blue) with the manually computed returns (in red).
df %>%
ggplot(aes(x = idu)) +
geom_line(aes(y = AAPL.Adjusted_prc), colour = "blue") +
geom_line(aes(y = aapl_man), colour = "red") +
ggtitle("Apple")
df %>%
ggplot(aes(x = idu)) +
geom_line(aes(y = FB.Adjusted_prc), colour = "blue") +
geom_line(aes(y = fb_man), colour = "red") +
ggtitle("Facebook")
In particular for Facebook, we see quite some difference between the two approaches. I'm sorry that I couldn't solve your problem, but I hope that this will lead you towards the solution.
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