Fetching the Datasets of qrmdata

1 Stock data

Note: The .csv files can be found in ./misc in the package’s source directory.

## Radioshack
## Defaulted early 2015 => Yahoo Finance only has
## https://www.google.com/finance/historical?q=OTCMKTS%3ARSHCQ&ei=N_CAVvnUKsjjsAHJ3YvwDg
## but no adjusted close price (and values differ slightly)
## => Use the data from Yahoo Finance as of 2015-01-21 here (collected by Alex J. McNeil)
RSHCQ <- read.table("data_RSHCQ.csv", sep=",", header=TRUE, row.names=1) # get data
dates <- as.Date(row.names(RSHCQ), format="%Y-%m-%d") # get dates
RSHCQ <- xts(RSHCQ, dates)[,"Adj.Close"] # create an xts object
colnames(RSHCQ) <- "RSHCQ"
save(RSHCQ, file="RSHCQ.rda", compress="xz")

2 Stock index data

## S&P 500
SP500 <- get_data("^GSPC", to=end)
save(SP500, file="SP500.rda", compress="xz")

## Dow Jones
DJ <- get_data("^DJI", to=end)
save(DJ, file="DJ.rda", compress="xz")

## NASDAQ 100
NASDAQ <- get_data("^NDX", to=end)
save(NASDAQ, file="NASDAQ.rda", compress="xz")

## FTSE 100
FTSE <- get_data("^FTSE", to=end)
save(FTSE, file="FTSE.rda", compress="xz")

## SMI (Swiss Market Index)
SMI <- get_data("^SSMI", to=end)
save(SMI, file="SMI.rda", compress="xz")

## Euro Stoxx 50 (stock index of Eurozone stocks)
EURSTOXX <- get_data("^STOXX50E", to=end)
save(EURSTOXX, file="EURSTOXX.rda", compress="xz")

## CAC (Cotation Assistee en Continu)
CAC <- get_data("^FCHI", to=end)
save(CAC, file="CAC.rda", compress="xz")

## DAX (Deutscher Aktienindex)
DAX <- get_data("^GDAXI", to=end)
save(DAX, file="DAX.rda", compress="xz")

## CSI (China Securities Index)
CSI <- get_data("000300.SS", to=end)
save(CSI, file="CSI.rda", compress="xz")

## HSI (Hang Seng Index; open to overseas investors)
HSI <- get_data("^HSI", to=end)
save(HSI, file="HSI.rda", compress="xz")

## SSEC (Shanghai Stock Exchange Composite Index; restricted to domestic investors)
SSEC <- get_data("000001.SS", to=end)
save(SSEC, file="SSEC.rda", compress="xz")

## NIKKEI
NIKKEI <- get_data("^N225", to=end)
save(NIKKEI, file="NIKKEI.rda", compress="xz")

3 Constituents data

3.1 S&P 500

## S&P 500 constituents
## See https://www.cboe.com/products/snp500.aspx (updated 2015-10-12)
SP500_const_info <- read.table("data_SP500_const_tab.csv", sep=",")
colnames(SP500_const_info) <- c("Ticker", "Sector", "Subsector")
SP500_const <- get_data(SP500_const_info[,1], to=end) # get data for these tickers (takes a while)
SP500_const <- round(SP500_const, digits = 2) # round to fit in a single R package
save(SP500_const, SP500_const_info, file="SP500_const.rda", compress="xz")

if(FALSE) {
    image(x=index(SP500_const), y=seq_len(ncol(SP500_const)), z=is.na(SP500_const),
          col=c("white", "black"), xlab="Time t", ylab="Stock j",
          main="Plot of NAs among the S&P 500 constituents")
    mtext("Black: NA; White: Data available", side=4, line=1, adj=0)
}

3.2 Other indexes

## Dow Jones constituents
## See https://finance.yahoo.com/q/cp?s=%5EDJI (drawn on 2015-12-10)
DJ_const_info <- read.table("data_DJ_const_tab.csv")
colnames(DJ_const_info) <- "Ticker"
DJ_const <- get_data(DJ_const_info[,1], to=end)
save(DJ_const, file="DJ_const.rda", compress="xz") # Note: No info besides tickers available => don't save *_const_info

## FTSE 100 constituents
## See https://uk.finance.yahoo.com/q/cp?s=%5EFTSE (drawn on 2015-12-10)
## Note: ncol(FTSE_const)=98 only
FTSE_const_info <- read.table("data_FTSE_const_tab.csv")
colnames(FTSE_const_info) <- "Ticker"
FTSE_const <- get_data(FTSE_const_info[,1], to=end)
save(FTSE_const, file="FTSE_const.rda", compress="xz") # Note: No info besides tickers available => don't save *_const_info

## SMI constituents
## See https://uk.finance.yahoo.com/q/cp?s=%5ESSMI (drawn on 2015-12-10)
## Note: 6/20 stocks missing
if(FALSE) {
    SMI_const_info <- read.table("data_SMI_const_tab.csv")
    colnames(SMI_const_info) <- "Ticker"
    SMI_const <- get_data(SMI_const_info[,1], to=end)
    save(SMI_const, file="SMI_const.rda", compress="xz") # Note: No info besides tickers available => don't save *_const_info
}

## Euro Stoxx 50 constituents
## See https://uk.finance.yahoo.com/q/cp?s=%5ESTOXX50E (drawn on 2015-12-10)
## Note: 2 stocks missing
EURSTX_const_info <- read.table("data_EURSTX_const_tab.csv")
colnames(EURSTX_const_info) <- "Ticker"
EURSTX_const <- get_data(EURSTX_const_info[,1], to=end)
save(EURSTX_const, file="EURSTX_const.rda", compress="xz") # Note: No info besides tickers available => don't save *_const_info

## HSI constituents
## See https://uk.finance.yahoo.com/q/cp?s=%5EHSI (drawn on 2015-12-10)
HSI_const_info <- read.table("data_HSI_const_tab.csv")
colnames(HSI_const_info) <- "Ticker"
HSI_const <- get_data(HSI_const_info[,1], to=end)
save(HSI_const, file="HSI_const.rda", compress="xz") # Note: No info besides tickers available => don't save *_const_info

## Note: NIKKEI constituents not available

4 FX data

## Check that 1/"1 unit cur A in cur B" ~= "cur B in cur A"
if(FALSE) {
    GBP_USD <- get_data("GBP/USD", to=end, src="oanda") # 1 GBP in USD
    USD_GBP <- get_data("USD/GBP", to=end, src="oanda") # 1 USD in GBP
    ## Check that 1/GBP_USD ~= USD_GBP
    plot(USD_GBP)
    lines(1/GBP_USD, col="blue")
    summary(USD_GBP-1/GBP_USD) # as good as equal
}

## Choose a reasonable 'from' date
## FX involving EUR are only available >= 1998-12-14
from <- "2000-01-01"

4.1 */USD

## CAD/USD
CAD_USD <- get_data("CAD/USD", from=from, to=end, src="oanda")
head(CAD_USD)
save(CAD_USD, file="CAD_USD.rda", compress="xz")

## GBP/USD
GBP_USD <- get_data("GBP/USD", from=from, to=end, src="oanda")
head(GBP_USD)
save(GBP_USD, file="GBP_USD.rda", compress="xz")

## EUR/USD
EUR_USD <- get_data("EUR/USD", from=from, to=end, src="oanda")
head(EUR_USD)
save(EUR_USD, file="EUR_USD.rda", compress="xz")

## CHF/USD
CHF_USD <- get_data("CHF/USD", from=from, to=end, src="oanda")
head(CHF_USD)
save(CHF_USD, file="CHF_USD.rda", compress="xz")

## JPY/USD
## Note: We use JPY/USD = 1/(USD/JPY) here since JPY/USD only
##       has four digits which is to little here
JPY_USD <- 1/get_data("USD/JPY", from=from, to=end, src="oanda")
head(JPY_USD)
save(JPY_USD, file="JPY_USD.rda", compress="xz")

## CNY/USD
CNY_USD <- get_data("CNY/USD", from=from, to=end, src="oanda")
head(CNY_USD)
save(CNY_USD, file="CNY_USD.rda", compress="xz")

4.2 */GBP

## CAD/GBP
CAD_GBP <- get_data("CAD/GBP", from=from, to=end, src="oanda")
head(CAD_GBP)
save(CAD_GBP, file="CAD_GBP.rda", compress="xz")

## GBP/GBP
USD_GBP <- get_data("USD/GBP", from=from, to=end, src="oanda")
head(USD_GBP)
save(USD_GBP, file="USD_GBP.rda", compress="xz")

## EUR/GBP
EUR_GBP <- get_data("EUR/GBP", from=from, to=end, src="oanda")
head(EUR_GBP)
save(EUR_GBP, file="EUR_GBP.rda", compress="xz")

## CHF/GBP
CHF_GBP <- get_data("CHF/GBP", from=from, to=end, src="oanda")
head(CHF_GBP)
save(CHF_GBP, file="CHF_GBP.rda", compress="xz")

## JPY/GBP
## Note: We use JPY/GBP = 1/(GBP/JPY) here since JPY/GBP only
##       has four digits which is to little here
JPY_GBP <- 1/get_data("GBP/JPY", from=from, to=end, src="oanda")
head(JPY_GBP)
save(JPY_GBP, file="JPY_GBP.rda", compress="xz")

## CNY/GBP
CNY_GBP <- get_data("CNY/GBP", from=from, to=end, src="oanda")
head(CNY_GBP)
save(CNY_GBP, file="CNY_GBP.rda", compress="xz")

5 Interest rate data

5.1 Canada

## Get data from http://www.bankofcanada.ca/rates/interest-rates/bond-yield-curves/
ZCB_CA <- read.csv("data_yield_curves_CA.csv", header=TRUE, na.strings=" na", row.names=1)
head(ZCB_CA) # quick look
ZCB_CA <- ZCB_CA[,-ncol(ZCB_CA)] # remove last (empty) column
## Pick out rows with *all* available data only
ZCB_CA <- ZCB_CA[apply(!is.na(ZCB_CA), 1, all),]
## Create Date object
dates <- as.Date(row.names(ZCB_CA), format="%Y-%m-%d")
## Create xts object
ZCB_CA <- xts(ZCB_CA, dates)
## Fix column names
clnms <- colnames(ZCB_CA)
clnms <- gsub("YR", "y", gsub("ZC", "", clnms)) # remove leading and trailing characters
clnms <- sapply(clnms, function(x) { # insert "."
    if(nchar(x) == 4) { # insert "." after first char
        gsub('^(.{1})(.*)$', '\\1.\\2', x)
    } else { # insert "." after first char
        gsub('^(.{2})(.*)$', '\\1.\\2', x)
    }
})
colnames(ZCB_CA) <- clnms
head(ZCB_CA)
## Save
save(ZCB_CA, file="ZCB_CA.rda", compress="xz")

## Plot
if(FALSE)
    plot.zoo(ZCB_CA, screens=1, col=adjustcolor("black", alpha.f=0.05))

5.2 US

## Get data from https://www.quandl.com/data/FED/SVENY-US-Treasury-Zero-Coupon-Yield-Curve
ZCB_US <- get_data("FED/SVENY", to=end, src="quandl")
head(ZCB_US) # quick look
## Pick out rows with *all* available data only
ZCB_US <- ZCB_US[apply(!is.na(ZCB_US), 1, all),]
## Fix column names
clnms <- colnames(ZCB_US)
clnms <- paste0(gsub("SVENY", "", clnms),"y") # remove leading characters and append "y"
clnms[1:9] <- substr(clnms[1:9], 2, 3) # remove leading "0"
colnames(ZCB_US) <- clnms
head(ZCB_US)
## Save
save(ZCB_US, file="ZCB_US.rda", compress="xz")

## Plot
if(FALSE)
    plot.zoo(ZCB_US, screens=1, col=adjustcolor("black", alpha.f=0.05))

6 Volatility data

## VIX
VIX <- get_data("^VIX", to=end)
save(VIX, file="VIX.rda", compress="xz")

7 Commodity data

## Oil
## Note: - Brent Crude is the benchmark (https://en.wikipedia.org/wiki/Brent_Crude)
##       - See https://www.quandl.com/data/FRED?keyword=dcoil for the tickers
##         => https://www.quandl.com/data/FRED/DCOILBRENTEU-Crude-Oil-Prices-Brent-Europe
##       - FRED = Federal Reserve Economic Data => FRED data can also be directly
##         obtained via getSymbols (and thus get_data).
##       - get_data("DCOILBRENTEU", to=end, src="FRED") would have also worked
OIL_Brent <- get_data("FRED/DCOILBRENTEU", to=end, src="quandl") # Brent Crude price in USD per barrel (USD/bbl)
colnames(OIL_Brent) <- "OIL_Brent"
stopifnot(!is.na(OIL_Brent))
save(OIL_Brent, file="OIL_Brent.rda", compress="xz")

## Gold
## See https://www.quandl.com/data/WGC/GOLD_DAILY_USD-Gold-Prices-Daily-Currency-USD (WGC = World Gold Council)
GOLD <- get_data("WGC/GOLD_DAILY_USD", to=end, src="quandl")
colnames(GOLD) <- "GOLD"
stopifnot(!is.na(GOLD))
save(GOLD, file="GOLD.rda", compress="xz")

Fetching the Datasets of qrmdata

Marius Hofert

2016-05-29

1 Stock data

2 Stock index data

3 Constituents data

3.1 S&P 500

3.2 Other indexes

4 FX data

4.1 */USD

4.2 */GBP

5 Interest rate data

5.1 Canada

5.2 US

6 Volatility data

7 Commodity data