Stock indicator calculations in TTR ( R package): Best way to align the output to the left?

Question

I am using the TTR package to generate stock indicators. However, the indicator functions add NA (where applicable -- e.g. CMO, SMA, CMF, etc.) to the beginning of the series instead of the end. Is there a way to align the output to the left so the NA values are added to the end of the series as opposed to the beginning?

For example:

library(TTR)    
x = 1:10
# TTR's simple moving average
SMA(x,n=2)
[1]  NA 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5

zoo package has an align option to pad the series with NAs at the end:

library(zoo)
rollmean(x,2,na.pad=TRUE,align='left')
[1] 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5  NA

Is there a way to specify something like this in TTR as I need to generate indicators beyond moving averages? I guess I can create a wrapper around these functions and manually shift the resulting values but not sure if there is a better way to do it.

Also, since TTR is heavily used to add indicators to stock prices, I am wondering why the padding is at the beginning as opposed to the end especially since most historical prices as sorted in descending order(by date)? In the above example, if x[1] is the price of a stock today and x[10] the price 10-days ago, shouldn't the moving average (span = 2) for today the average of today + yesterday? As much as I would like to add NAs at the end, I would also like to make sure I am not misinterpreting how these indicators are used.

Thanks, -e

Answer 1

I couldn't figure out an option in the function call to shift the series in a different direction. However, now I understand why TTR shifts the series downwards. Historical stock prices obtained via quantmod's getSymbols() returns them sorted by date in ascending order. When I manually download the quotes from Yahoo! or use ystockquote.py, the order is descending. I just re-sorted my data by date and used the TTR library as-is.

There were certain vectors that I wanted to be shifted up (padded with NAs) and I just used this code:

miss_len = length(x[is.na(x])
x = x[!is.na(x)]
length(x) = length(x) + miss_len