Rollapply backwards time series in R - r

Question

I need to fill backwards the historical prices knowing the returns (in real situation they are simulated).
So far I have this code:
library(quantmod)
getSymbols("AAPL")
df = AAPL["2014-01-01/2015-01-01", "AAPL.Close"]
df_ret = diff(log(df),1)
# imagine the half of the past prices are missing
df["2014-01-01/2014-07-01"] = NA
df_tot = cbind(df, df_ret)
fillBackwards = function(data, range_to_fill){
index_array = index(data[range_to_fill,])
data_out = data
for (i in (length(index_array)-1):1){
inx = index_array[i]
inx_0 = index_array[i+1]
data_out[inx,1] = exp(-(data_out[inx_0,2]))*(data_out[inx_0,1])
}
return (data_out)
}
df_filled = fillBackwards(df_tot,"2014-01-01/2014-07-02")
sum(AAPL["2014-01-01/2015-01-01", "AAPL.Close"] - df_filled[,1]) # zero up to computation error, i.e. identical
This works perfect, but a bit slow. Could you please suggest something using build-in rollapply()
# i want something like this
df_filled = rollapply(df_tot["2014-07-02/2014-01-01",], by=-1, function(x) {....})

Answer 1

You don't need rollapply, or a loop. You can use cumprod on the returns. Here's a version of fillBackwards that uses cumprod:
fillBackwards <- function(data, range_to_fill) {
data_range <- data[range_to_fill,]
returns <- rev(coredata(data_range[-1L, 2L]))
last_price <- drop(coredata(last(data_range[, 1L])))
new_prices <- rev(last_price * cumprod(exp(-returns)))
data[range_to_fill, 1L] <- c(last_price, new_prices)
return(data)
}