time series in R, unwanted variable class changes - r

Question

I'm trying to program the Coppock Curve in R and finding time series exceedingly difficult to work with in R. The S&P 500 data can be downloaded from finance.yahoo.com. Just bring in the date and the adjusted close.
sp500 = read.csv(file="/.../sp500.csv",header=TRUE)
attach(zoo)
sp500.z = zoo(sp500)
lag11 = lag(sp500.z$SP500, -11, na.pad=TRUE)
lag14 = lag(sp500.z$SP500, -14, na.pad=TRUE)
sp500.z = cbind(sp500.z, lag11, lag14)
str(sp500.z)
sp500.z[1:25,]
data = (sp500.z)
data[1:25,] ### everything looks good up to here
str(data)
data = as.data.frame(data) ### problem arises here, everything becomes factor even if it wasn't before, so I try to convert, but it doesn't work
data$SP500 = as.numeric(data$SP500)
data$lag11 = as.numeric(data$lag11)
data$lag14 = as.numeric(data$lag14)
data$date = as.Date(data$date)
In order to do further data manipulation I need to convert to a data frame, because you cannot attach a zoo matrix or perform dataset$variable operations on it. When I convert to data frame the lag11 and lag14 variables turn into index numbers. The data frame conversion makes everything a factor, and when the variable types are corrected the problem occurs.
The Coppock Curve is calculated as a 10-month weighted moving average of the sum of the 14-month rate of change and the 11-month rate of change for the index.
Coppock Curve = 10-month weighted MA(of 14-month ROC + 11-month ROC)
Where the ROC is:
ROC = [(Close - Close n periods ago) / (Close n periods ago)] * 100
where n is 11 and 14. The weights on the ROC terms go backwards in time from 10/55 for period t, 9/55 for t-1,..., 1/55 for t-9.

Answer 1

You do not need to convert to a data.frame. While you cannot use $ on a matrix, you can use it on zoo and xts objects. And you really shouldn't be using attach, especially if this is something you plan to put into a reusable script.
What you want to do is very easy with xts/zoo, quantmod, and TTR.
library(quantmod) # also loads TTR, xts, and zoo
# download data from Yahoo Finance
sp500 <- getSymbols("^GSPC", auto.assign=FALSE)
# convert to monthly
sp500m <- to.monthly(sp500)
# add lags (via $<-, like you claimed couldn't be done)
sp500m$lag11 <- ROC(Ad(sp500m), n=11, type="discrete")
sp500m$lag14 <- ROC(Ad(sp500m), n=14, type="discrete")
# calculate Coppock Curve
sp500m$Coppock <- WMA(sp500m$lag11 + sp500m$lag14, n=10, wts=(9:1)/55)

Answer 2

One option would be not to put the data into zoo format and then use the lag() function in dplyr instead. So:
library(dplyr)
sp500 = read.csv(file="/.../sp500.csv",header=TRUE)
sp500.v2 <- sp500 %>%
mutate(lag11 = lag(SP500, 11),
lag14 = lag(SP500, 14))
Are these data grouped somehow, like maybe by ticker symbol? If so, you could accommodate that like this:
sp500.v2 <- sp500 %>%
group_by([grouping variable name, no quotes]) %>%
mutate(lag11 = lag(SP500, 11),
lag14 = lag(SP500, 14))
And if the data aren't pre-sorted by date or you want to make sure that's done before you lag, you can use arrange() like so:
sp500.v2 <- sp500 %>%
group_by([grouping variable name, no quotes]) %>%
arrange([date variable]) %>%
mutate(lag11 = lag(SP500, 11),
lag14 = lag(SP500, 14))