7.1 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)## AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume
## 2019-03-05 175.94 176.00 174.54 175.53 19737400
## 2019-03-06 174.67 175.49 173.94 174.52 20810400
## 2019-03-07 173.87 174.44 172.02 172.50 24760900
## 2019-03-08 170.32 173.07 169.50 172.91 23999400
## 2019-03-11 175.49 179.12 175.35 178.90 32011000
## 2019-03-12 180.00 182.67 179.37 180.91 32467600
## AAPL.Adjusted
## 2019-03-05 175.53
## 2019-03-06 174.52
## 2019-03-07 172.50
## 2019-03-08 172.91
## 2019-03-11 178.90
## 2019-03-12 180.91To 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 <- 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 91244100
## 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
## 2019-01-02 157.2456
## 2019-01-03 141.5828
## 2019-01-04 147.6268
## 2019-01-07 147.2983
## 2019-01-08 150.1062
## 2019-01-09 152.6553Alternatively, 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 7.982585
## 2007-01-04 8.159763
## 2007-01-05 8.101658
## 2007-01-08 8.141665
## 2007-01-09 8.817995
## 2007-01-10 9.239983We 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)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 46.17351
## 2007-01-03 41163203 46.90325
## 2007-01-04 37286533 45.24471
## 2007-01-05 24502496 46.10715
## 2007-01-08 15584163 44.91301
## 2007-01-09 17861491 43.98424Besides 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.
WeekAveVoClYa<- apply.weekly(Vo(AAPL),mean)
# weekly average volume