Majeed Simaan March 27th, 2018
In this vignette, I provide a brief introduction to the quantmod package to download stock prices, option prices, and different indices data.
To get started, make sure you have installed the quantmod in R - you can do so using the following command:
install.packages("quantmod")After successfully installing the package, you can load the package
library(quantmod)To get started, assume that you are interested in downloading the stock prices for Apple ("AAPL"). To do so, you can easily get the data using the following command
getSymbols("AAPL",from = "1990-01-01")## [1] "AAPL"
Note that getSymbols takes two inputs above. The first one is the ticker of the stock, whereas the second is the start date. In case there is no data prior to the inputted date, the function will return the maximum date. Also, note that R stores the stock data in a new item named AAPL. For instance,
head(AAPL)## AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume
## 1990-01-02 1.258929 1.339286 1.250000 1.330357 45799600
## 1990-01-03 1.357143 1.357143 1.339286 1.339286 51998800
## 1990-01-04 1.366071 1.383929 1.330357 1.343750 55378400
## 1990-01-05 1.348214 1.366071 1.321429 1.348214 30828000
## 1990-01-08 1.339286 1.357143 1.321429 1.357143 25393200
## 1990-01-09 1.357143 1.357143 1.321429 1.343750 21534800
## AAPL.Adjusted
## 1990-01-02 0.122946
## 1990-01-03 0.123771
## 1990-01-04 0.124184
## 1990-01-05 0.124597
## 1990-01-08 0.125422
## 1990-01-09 0.124184
If one wishes to focus on the adjusted prices, which I will do in this article, one can do so by referring to the corresponding column number, i.e. AAPL[,6]. For instance, the following command plots the adjusted prices over time
plot(AAPL[,6])
Now, let's assume we are interested in downloading data for multiple tickers stacked in vector tics, where
tics <- c("AAPL","GE","TWTR","BAC","RAD","PFE")One approach is to run a loop and get the data for each ticker, and then try to figure out a way to merge the prices altogether. Instead, I would like to show you the following trick using Functional Programming:
P.list <- lapply(tics, function(tic) get(getSymbols(tic, from = "1980-01-01")) )Note that the P.list is a list object containing the data for each ticker. In total, it contains 6 elements (corresponding to the number of tickers), while the each element has the following length
sapply(P.list, nrow)## [1] 9499 9739 1199 9687 8484 9739
Obviously, TWTR has the least number of dates in the time series. Merging the adjusted prices altogether is straightforward
P.adj <- lapply(P.list, function(p) p[,6] ) # pulls the sixth column of each ticker
P <- Reduce(merge,P.adj) # merge altogether
names(P) <- tics # adjust the names with respect to the tickers
head(P,2) # returns the oldest two observations## AAPL GE TWTR BAC RAD PFE
## 1980-01-02 NA 0.005311 NA NA NA 0.000712
## 1980-01-03 NA 0.005379 NA NA NA 0.000712
tail(P,2) # returns the most recent two observations## AAPL GE TWTR BAC RAD PFE
## 2018-08-10 207.53 12.77 32.01 31.19 1.48 40.93
## 2018-08-13 208.87 12.45 32.80 30.48 1.40 40.81
Finally, to get the returns, I use the lag operator, such that
R <- P/lag(P) - 1 # or log(P/lag(P))Note that since each ticker has a different length, the combined returns have a lot of missing values. One can either drop the missing values using the na.omit function or utilize all data to derive the relevant estimates. In the latter case, one can compute the mean vector and the covariance matrix using all available information as follows
M <- apply(R,2,function(x) mean(x,na.rm = T) )
S <- var(R,use = "pairwise")Finally, you can save the data into a csv file. But, make sure that data is saved as a data.frame rather than an xts object, i.e.
write.csv(data.frame(R),file = "R.csv")The quantmod package also allows users to download options data from Yahoo, however, this is more tricky. The main function I will use is the getOptionChain from the quantmod package. Like getSymbols, getOptionChain requires ticker as the main input. However, the function does not return any historical options data. The second argument one can add is the expiration date. If one knows the exact expiration date, then it is straightforward to implement. If not, one can either search for the expiration dates from Yahoo Finance or explore expiration as I will illustrate below.
To get started, let's pull some options data for Apple:
opt <- getOptionChain("AAPL",Exp = "2018")
class(opt)## [1] "list"
names(opt)## [1] "Aug.17.2018" "Aug.24.2018" "Aug.31.2018" "Sep.07.2018" "Sep.14.2018"
## [6] "Sep.21.2018" "Sep.28.2018" "Oct.19.2018" "Nov.16.2018" "Dec.21.2018"
As you can see, the names of the lists are assigned with respect to the expiration date for the year 2018. Let's assume that we are interested in options expiring in six months ahead. In this case, "Sep.21.2018" is the most relevant one.
opt6 <- opt[["Sep.21.2018"]]
class(opt6)## [1] "list"
names(opt6)## [1] "calls" "puts"
Note that the opt6 is also a list, where each item in the list corresponds to the call and put options data, such that
sapply(opt6, class)## calls puts
## "data.frame" "data.frame"
To put altogether, I perform the following commands
opt6 <- lapply(1:length(opt6), function(i) data.frame(opt6[[i]],Type = names(opt6)[i], Expiration = "Sep.21.2018" , stringsAsFactors = F) )
opt6 <- Reduce(rbind,opt6)The first line adds one column corresponding to the type of the option and another for the expiration date. The second line combines the two lists altogether by rows as one data.frame, i.e.
class(opt6)## [1] "data.frame"
Finally, I would like to adjust the expiration date format and add today's date to the data:
opt6$Expiration <- as.Date(opt6$Expiration,format='%B.%d.%Y')
opt6$Date <- Sys.Date()
head(opt6)## Strike Last Chg Bid Ask Vol OI Type
## AAPL180921C00070000 70 139.57 0.00 137.40 138.20 105 0 calls
## AAPL180921C00075000 75 115.07 115.07 115.50 116.50 0 0 calls
## AAPL180921C00080000 80 129.60 0.00 127.45 128.20 74 20 calls
## AAPL180921C00085000 85 122.50 0.00 122.50 123.20 2 0 calls
## AAPL180921C00090000 90 118.90 0.00 117.50 118.20 48 2 calls
## AAPL180921C00095000 95 113.85 0.00 112.50 113.25 40 1 calls
## Expiration Date
## AAPL180921C00070000 2018-09-21 2018-08-14
## AAPL180921C00075000 2018-09-21 2018-08-14
## AAPL180921C00080000 2018-09-21 2018-08-14
## AAPL180921C00085000 2018-09-21 2018-08-14
## AAPL180921C00090000 2018-09-21 2018-08-14
## AAPL180921C00095000 2018-09-21 2018-08-14
The above illustration focuses on one stock and one expiration date. To generalize, let's consider a function that takes one ticker as the main input and returns a data.frame object that covers all options data today for all expiration dates.
get_opt <- function(tic) {
OPT <- numeric()
opt.i <- try(getOptionChain(tic,Exp = NULL), silent = T )
if(inherits(opt.i, "try-error"))
return(NULL)
else {
# drop any missing
opt.i <- opt.i[!sapply(opt.i, function(x) any(sapply(x, is.null )) )]
exp.time <- names(opt.i)
opt.iy <- numeric()
for(exp_t in exp.time) {
opt.it <- opt.i[[exp_t]]
opt.it <- lapply(1:length(opt.it), function(i)
data.frame(opt.it[[i]],Type = names(opt.it)[i],
Expiration = exp_t , stringsAsFactors = F) )
opt.it <- Reduce(rbind,opt.it)
opt.it$Expiration <- as.Date(opt.it$Expiration,format='%B.%d.%Y')
opt.it$Date <- Sys.Date()
opt.iy <- rbind(opt.iy,opt.it)
}
OPT <- rbind(OPT,opt.iy)
OPT$Ticker <- tic
rownames(OPT) <- NULL
}
return(OPT)
} Let's run the get_opt function on all tickers we have in tics:
opt.list <- numeric()
for(i in 1:length(tics)) {
get.i <- try(get_opt(tics[i]),silent = T)
if(inherits(get.i, "try-error"))
opt.list[i] <- list(NULL)
else
opt.list[i] <- list(get.i)
}
names(opt.list) <- tics
sapply(opt.list,nrow) # number of observations in each data## AAPL GE TWTR BAC RAD PFE
## 1178 414 886 609 116 431
The above tries to extract all options data for a set of tickers. In case there is a failure, the list assigns an empty object. As a final step, we can combine all items in the opt.list by rows as we did earlier
opt.ds <- Reduce(rbind,opt.list)
write.csv(opt.ds,file = "OPT.csv",row.names = FALSE)To download other indices outside Yahoo Finance, especially economic indicators, I recommend the Federal Reserve Economic Data FRED as the best open source to establish so. Let's say that you already know the tickers (codes) names you interested in, then getting these indices is as simple as getting stock data from Yahoo Finance. Nonetheless, I recommend browsing through the FRED website to figure out which code you are looking for. For instance, the corresponding codes for the LIBOR and 3-months Treasury maturity rates are given by "USD1MTD156N" and "DGS3MO", respectively.
indices <- c("USD1MTD156N","DGS3MO")
ind.list <- lapply(indices, function(tic) get(getSymbols(tic, from = "1980-01-01", src = "FRED")) )
IND <- Reduce(merge,ind.list)Different from the stock returns, I changed the src to FRED, whereas the default is set to src = "yahoo".
plot(IND)
Finally, don't forget to save the IND into a csv by transforming into a data.frame:
write.csv(data.frame(IND),file = "IND.csv")