4.2 Downloading data
Use getSymbols to get data from yahoo or Google (default is yahoo)
getSymbols("AAPL")Let see what is inside the data.
head(AAPL,n=3)## AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume
## 2003-01-02 0.5083427 0.5281666 0.5079888 0.5239188 45357200
## 2003-01-03 0.5239188 0.5285210 0.5164849 0.5274589 36863400
## 2003-01-06 0.5320605 0.5444505 0.5267507 0.5274589 97633200
## AAPL.Adjusted
## 2003-01-02 0.919821
## 2003-01-03 0.926036
## 2003-01-06 0.926036This is callsed OHLCVA price data. We have opening, high, low, open, close, volume and adjusted closing prices. High and low are the highest and lowest prices of the trading day. Open and close are the opening and the closing prices of the trading day. Volume is the number of shares transacted on the trading day. Adjusted closing price is the closing price that adjusts for event after market closes such as stock splits and dividend.
To extract columns, we use Op, Hi, Lo, Cl, Vo and Ad
Open <- Op(AAPL) #Open Price
High <- Hi(AAPL) # High price
Low <- Lo(AAPL) # Low price
Close<- Cl(AAPL) #Close Price
Volume <- Vo(AAPL) #Volume
AdjClose <- Ad(AAPL) # Adjusted closeIf you wish to only import at a certain date e.g.,. 2000-01-01 to 2015-09-25, we can restrict the set the data to download.
getSymbols("AAPL", from='2000-01-01',to='2015-09-25')## [1] "AAPL"Alternatively, we can load the whole series and restrict using xts function last()
getSymbols("AAPL")## [1] "AAPL"AAPL <- xts::last(AAPL,'1 year')
head(AAPL)## AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume
## 2019-01-02 154.89 158.85 154.23 157.92 37039700
## 2019-01-03 143.98 145.72 142.00 142.19 91312200
## 2019-01-04 144.53 148.55 143.80 148.26 58607100
## 2019-01-07 148.70 148.83 145.90 147.93 54777800
## 2019-01-08 149.56 151.82 148.52 150.75 41025300
## 2019-01-09 151.29 154.53 149.63 153.31 45099100
## AAPL.Adjusted
## 2019-01-02 155.5824
## 2019-01-03 140.0852
## 2019-01-04 146.0654
## 2019-01-07 145.7403
## 2019-01-08 148.5185
## 2019-01-09 151.0406Alternatively, we can take the first 3 years by using first():
getSymbols("AAPL")## [1] "AAPL"AAPL <- first(AAPL,'3 years')
head(AAPL)## AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume
## 2007-01-03 12.32714 12.36857 11.70000 11.97143 309579900
## 2007-01-04 12.00714 12.27857 11.97429 12.23714 211815100
## 2007-01-05 12.25286 12.31428 12.05714 12.15000 208685400
## 2007-01-08 12.28000 12.36143 12.18286 12.21000 199276700
## 2007-01-09 12.35000 13.28286 12.16429 13.22429 837324600
## 2007-01-10 13.53571 13.97143 13.35000 13.85714 738220000
## AAPL.Adjusted
## 2007-01-03 10.41635
## 2007-01-04 10.64755
## 2007-01-05 10.57173
## 2007-01-08 10.62393
## 2007-01-09 11.50647
## 2007-01-10 12.05711We can also import three stocks at the same time by using a vector.
getSymbols(c("AAPL","GOOG"))## [1] "AAPL" "GOOG"Alternatively, we can assign a vector of stocks and import based on the vector.
stocklist <- c("AAPL","GOOG")
getSymbols(stocklist)The package can also import non-US stocks.
getSymbols("0941.hk")
#If error, try instead the following:
#getSymbols("0941.HK",src="yahoo", auto.assign=FALSE)If there is an error, try the following instead:
getSymbols("0941.HK",src="yahoo", auto.assign=FALSE)Now let us see if the data is correct:
head(`0941.HK`)## 0941.HK.Open 0941.HK.High 0941.HK.Low 0941.HK.Close
## 2007-01-02 67.25 70.00 67.10 69.6
## 2007-01-03 69.90 71.80 69.50 70.7
## 2007-01-04 70.60 70.95 67.30 68.2
## 2007-01-05 67.50 69.50 66.30 69.5
## 2007-01-08 67.50 68.40 67.45 67.7
## 2007-01-09 68.00 68.60 65.55 66.3
## 0941.HK.Volume 0941.HK.Adjusted
## 2007-01-02 35293388 44.25044
## 2007-01-03 41163203 44.94978
## 2007-01-04 37286533 43.36033
## 2007-01-05 24502496 44.18684
## 2007-01-08 15584163 43.04243
## 2007-01-09 17861491 42.15236Besides prices, very often we are interested in the trading volume. Moreover, we would like to find volume over time: weekly, monthly, quarterly and yearly. We can use apply and sum to calculate the rolling sum of volume to each distinct period.
WeekVoYa<- apply.weekly(Vo(AAPL),sum)
# sum from Monday to Friday
MonthVoYa <- apply.monthly(Vo(AAPL),sum)
# sum to month
QuarterVoYa <- apply.quarterly(Vo(AAPL),sum)
# sum to quarter
YearVoYa <- apply.yearly(Vo(AAPL),sum)
# sum to yearIn some case, we are interested in average than the sum. Then we can use apply and mean. Below we calculate the weekly average volume of AAPL.
WeekAveVoClYa<- apply.weekly(Vo(AAPL),mean)