CODEWITHSUNDEEP
r
Hakki
Hakki

Reputation: 1472

returns to prices R

Here is sample data:

  set.seed(13)
x1 <- runif(10, -0.05, 0.05)
x2 <- runif(10, -0.05, 0.05)
x3 <- runif(10, -0.05, 0.05)
x4 <- runif(10, -0.05, 0.05)
df <- as.data.frame(cbind(x1, x2, x3, x4))

Lets think that these are Returns and I would like to convert these to prices with a starting value of 100. There is a answer for turning one vector of returns to prices here: How to convert returns to prices? I have tried following:

index <- as.data.frame(Reduce(function(x,y) {x * exp(y)}, df, init=100, accumulate=T))

but that wont work for data frame. I also tried apply function, but couldn't get anything reasonable out of it.

Upvotes: 1

Views: 1081

Answers (2)

Joshua Ulrich
Joshua Ulrich

Reputation: 176688

Use cumsum, which works on data frames.

R> index <- exp(cumsum(df)) * 100
          x1       x2       x3        x4
1  102.12550 101.6243 96.43574  99.23404
2   99.56554 105.5431 96.88956  98.29784
3   98.47272 109.7467 98.62877 102.50103
4   94.53007 110.4766 98.90613 105.71522
5   99.00045 111.5149 94.90222 106.13989
6   94.27516 110.0142 96.04782 102.05241
7   94.97819 108.4567 91.65382 101.58857
8   97.52289 109.4531 91.30083  97.13752
9  101.23305 113.5271 89.76203  99.68356
10  96.69209 115.5952 90.96857  95.62000

Upvotes: 1

Therkel
Therkel

Reputation: 1438

Expand the answer to your data frame by running it column-wise.

index <- sapply(colnames(df), function(col){
    Reduce(function(x,y){x * exp(y)}, 
           df[[col]], init=100, accumulate=T)
})

index
#x1       x2        x3        x4
#[1,] 100.00000 100.0000 100.00000 100.00000
#[2,] 102.12550 101.6243  96.43574  99.23404
#[3,]  99.56554 105.5431  96.88956  98.29784
#[4,]  98.47272 109.7467  98.62877 102.50103
#[5,]  94.53007 110.4766  98.90613 105.71522
#[6,]  99.00045 111.5149  94.90222 106.13989
#[7,]  94.27516 110.0142  96.04782 102.05241
#[8,]  94.97819 108.4567  91.65382 101.58857
#[9,]  97.52289 109.4531  91.30083  97.13752
#[10,] 101.23305 113.5271  89.76203  99.68356
#[11,]  96.69209 115.5952  90.96857  95.62000

Upvotes: 1

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