user2819398 user2819398 - 1 month ago 5
R Question

rowwise iteration in r on a data table

library(quantmod)
library(PerformanceAnalytics)
getSymbols("YHOO",src="google")
stock_dat=data.table(PerformanceAnalytics::
CalculateReturns(Cl(YHOO)[1:10],'discrete'))
stock_dat[,Price:=0]
stock_dat[1,2]=Cl(YHOO)[1]
stock_dat[,D:=(1+YHOO.Close)*shift(Price,1)]


The above code generates the below result:


stock_dat
YHOO.Close Price D
1: NA 25.61 NA
2: 0.048418586 0.00 26.85
3: 0.033147114 0.00 0.00
4: 0.006488825 0.00 0.00
5: -0.012177650 0.00 0.00
6: 0.040609137 0.00 0.00
7: 0.017421603 0.00 0.00
8: 0.008561644 0.00 0.00
9: -0.005432937 0.00 0.00
10: -0.008193923 0.00 0.00



The YHOO.Close is assumed to be a simulated returns and i need to back out the prices from that. And i am using the first price as the base. The above code needs to ideally use the price in D from row 3.

nrowsDF <- nrow(stock_dat)

for(i in 2:nrowsDF){
stock_dat[i,2]=(1+stock_dat[i,1,with=FALSE])*stock_dat[i-1,2,with=FALSE]
}


The above code solves the problem. But am looking for a more efficent way to do this, as i have to repeat this for over 5000 simulated return series

The below is the answer i actually need

stock_dat
YHOO.Close Price
1: NA 25.61
2: 0.048418586 26.85
3: 0.033147114 27.74
4: 0.006488825 27.92
5: -0.012177650 27.58
6: 0.040609137 28.70
7: 0.017421603 29.20
8: 0.008561644 29.45
9: -0.005432937 29.29
10: -0.008193923 29.05

Answer

You can use the cumulative product like this:

DT <- fread("      YHOO.Close Price     D
                     NA 25.61    NA
            0.048418586  0.00 26.85
            0.033147114  0.00  0.00
            0.006488825  0.00  0.00
           -0.012177650  0.00  0.00
            0.040609137  0.00  0.00
            0.017421603  0.00  0.00
            0.008561644  0.00  0.00
           -0.005432937  0.00  0.00
           -0.008193923  0.00  0.00")

DT[, res := Price[1] * c(1, cumprod(1 + YHOO.Close[-1]))]
#      YHOO.Close Price     D   res
# 1:           NA 25.61    NA 25.61
# 2:  0.048418586  0.00 26.85 26.85
# 3:  0.033147114  0.00  0.00 27.74
# 4:  0.006488825  0.00  0.00 27.92
# 5: -0.012177650  0.00  0.00 27.58
# 6:  0.040609137  0.00  0.00 28.70
# 7:  0.017421603  0.00  0.00 29.20
# 8:  0.008561644  0.00  0.00 29.45
# 9: -0.005432937  0.00  0.00 29.29
#10: -0.008193923  0.00  0.00 29.05
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