I learned how to use the getSymbols() function from the quantmod package.
# Load the quantmod package
library(quantmod)
# Import QQQ data from Yahoo! Finance
getSymbols(Symbols = "QQQ", auto_assign = TRUE)
## [1] "QQQ"
# Look at the structure of the object getSymbols created
str(QQQ)
## An 'xts' object on 2007-01-03/2017-06-30 containing:
## Data: num [1:2643, 1:6] 47.8 47.7 48.4 48.3 48.4 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:6] "QQQ.Open" "QQQ.High" "QQQ.Low" "QQQ.Close" ...
## Indexed by objects of class: [Date] TZ: UTC
## xts Attributes:
## List of 2
## $ src : chr "yahoo"
## $ updated: POSIXct[1:1], format: "2017-07-01 13:34:00"
# Look at the first few rows of QQQ
head(QQQ)
## QQQ.Open QQQ.High QQQ.Low QQQ.Close QQQ.Volume QQQ.Adjusted
## 2007-01-03 47.832 48.492 46.797 43.24 167689500 39.28798
## 2007-01-04 47.656 48.657 47.491 44.06 136853500 40.03301
## 2007-01-05 48.371 48.371 47.854 43.85 138958800 39.84221
## 2007-01-08 48.305 48.558 48.030 43.88 106401600 39.86947
## 2007-01-09 48.437 48.745 48.019 44.10 121577500 40.06936
## 2007-01-10 48.382 49.152 48.228 44.62 121070100 40.54183I used getSymbols() imports data from FRED.
# Import QQQ data from Google Finance
getSymbols(Symbols = "QQQ", src = "google")
## [1] "QQQ"
# Look at the structure of QQQ
str(QQQ)
## An 'xts' object on 2007-01-03/2017-06-30 containing:
## Data: num [1:2642, 1:5] 43.5 43.3 43.9 43.9 44 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:5] "QQQ.Open" "QQQ.High" "QQQ.Low" "QQQ.Close" ...
## Indexed by objects of class: [Date] TZ: UTC
## xts Attributes:
## List of 2
## $ src : chr "google"
## $ updated: POSIXct[1:1], format: "2017-07-01 13:34:02"
# Import GDP data from FRED
getSymbols(Symbols = "GDP", src = "FRED")
## [1] "GDP"
# Look at the structure of GDP
str(GDP)
## An 'xts' object on 1947-01-01/2017-01-01 containing:
## Data: num [1:281, 1] 243 246 250 260 266 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr "GDP"
## Indexed by objects of class: [Date] TZ: UTC
## xts Attributes:
## List of 2
## $ src : chr "FRED"
## $ updated: POSIXct[1:1], format: "2017-07-01 13:34:03"I used getSymbols() and I assigned the data myself.
# Load the quantmod package
library(quantmod)
# Assign SPY data to 'spy' object using auto.assign argument
spy <- getSymbols(Symbols = "SPY", auto.assign = FALSE)
# Look at the structure of the 'spy' object
str(spy)
## An 'xts' object on 2007-01-03/2017-06-30 containing:
## Data: num [1:2643, 1:6] 177 176 176 175 176 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:6] "SPY.Open" "SPY.High" "SPY.Low" "SPY.Close" ...
## Indexed by objects of class: [Date] TZ: UTC
## xts Attributes:
## List of 2
## $ src : chr "yahoo"
## $ updated: POSIXct[1:1], format: "2017-07-01 13:34:04"
# Assign JNJ data to 'jnj' object using env argument
jnj <- getSymbols(Symbols = "JNJ", env = NULL) #env = NULL does the same as auto.assign = FALSE
# Look at the structure of the 'jnj' object
str(jnj)
## An 'xts' object on 2007-01-03/2017-06-30 containing:
## Data: num [1:2643, 1:6] 91.2 90.8 92.1 91.7 91.8 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:6] "JNJ.Open" "JNJ.High" "JNJ.Low" "JNJ.Close" ...
## Indexed by objects of class: [Date] TZ: UTC
## xts Attributes:
## List of 2
## $ src : chr "yahoo"
## $ updated: POSIXct[1:1], format: "2017-07-01 13:34:04"I learned how to use Quandl()
# Load the Quandl package
library(Quandl)
# Import GDP data from FRED
gdp <- Quandl(code = "FRED/GDP")
# Look at the structure of the object returned by Quandl
str(gdp)
## 'data.frame': 281 obs. of 2 variables:
## $ Date : Date, format: "2017-01-01" "2016-10-01" ...
## $ Value: num 19027 18869 18675 18450 18282 ...
## - attr(*, "freq")= chr "quarterly"I Learned how to use the type argument to Quandl() to make it return an xts and a zoo object.
# Import GDP data from FRED as xts
gdp_xts <- Quandl(code = "FRED/GDP", type = "xts")
# Look at the structure of gdp_xts
str(gdp_xts)
## An 'xts' object on 1947 Q1/2017 Q1 containing:
## Data: num [1:281, 1] 243 246 250 260 266 ...
## Indexed by objects of class: [yearqtr] TZ:
## xts Attributes:
## NULL
# Import GDP data from FRED as zoo
gdp_zoo <- Quandl(code = "FRED/GDP", type = "zoo")
# Look at the structure of gdp_zoo
str(gdp_zoo)
## 'zooreg' series from 1947 Q1 to 2017 Q1
## Data: num [1:281] 243 246 250 260 266 ...
## Index: Class 'yearqtr' num [1:281] 1947 1947 1948 1948 1948 ...
## Frequency: 4I learned how to gather data from an internet source.
# Create an object containing the Pfizer ticker symbol
symbol = "PFE"
# Use getSymbols to import the data
getSymbols(Symbols = symbol, src = "google")
## [1] "PFE"
# Look at the first few rows of data
head(PFE)
## PFE.Open PFE.High PFE.Low PFE.Close PFE.Volume
## 2007-01-03 26.15 26.42 25.98 26.29 40645100
## 2007-01-04 26.38 26.57 26.29 26.38 32258100
## 2007-01-05 26.55 26.63 26.17 26.30 31355800
## 2007-01-08 26.29 26.42 25.89 26.16 43224400
## 2007-01-09 26.24 26.34 26.04 26.17 31321400
## 2007-01-10 26.10 26.26 26.00 26.20 34548400I used data from Oanda.com
# There seems to be an error in IBM's system itself. It doesn't import data from Oanda.com
# Create a currency_pair object
currency_pair <- "GBP/CAD"
# Load British Pound to Canadian Dollar exchange rate data
getSymbols(Symbols = currency_pair, src = "oanda")
## [1] "GBPCAD"
# Examine object using str()
str(GBPCAD)
## An 'xts' object on 2017-01-03/2017-06-30 containing:
## Data: num [1:179, 1] 1.65 1.64 1.64 1.64 1.63 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr "GBP.CAD"
## Indexed by objects of class: [Date] TZ: UTC
## xts Attributes:
## List of 2
## $ src : chr "oanda"
## $ updated: POSIXct[1:1], format: "2017-07-01 13:34:06"
# Try to load data from 190 days ago
getSymbols(Symbols = currency_pair, from = Sys.Date() - 190, to = Sys.Date(), src = "oanda")
## [1] "GBPCAD"Both getSymbols() andQuandl() provide access to the FRED database.
# Create a series_name object
series_name <- "UNRATE" # To find the series name, go to the FRED website and search for "United States civilian unemployment rate".
# Load the data using getSymbols
getSymbols(Symbols = series_name, src = "FRED")
## [1] "UNRATE"
# Create a quandl_code object
quandl_code <- "FRED/UNRATE"
# Load the data using Quandl
unemploy_rate <- Quandl(code = quandl_code)I explored quantmod functions
# Load data
DC <- read.csv("~/resources/rstudio/New Folder With Items 2/DC.csv")
DC$Date <- as.POSIXct(DC$Date, format("%Y-%m-%d %H:%M:%S"))
DC <- xts(DC[, 2:6], order.by = DC$Date)
str(DC)
## An 'xts' object on 2016-01-16 01:00:00/2016-01-17 23:00:00 containing:
## Data: num [1:47, 1:5] 20.8 20.8 20.8 20.8 20.8 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:5] "DC.Open" "DC.High" "DC.Low" "DC.Close" ...
## Indexed by objects of class: [POSIXct,POSIXt] TZ: %Y-%m-%d %H:%M:%S
## xts Attributes:
## NULL
# Look at the head of DC
head(DC)
## DC.Open DC.High DC.Low DC.Close DC.Volume
## 2016-01-16 01:00:00 20.845 20.850 20.835 20.845 157
## 2016-01-16 02:00:00 20.845 20.850 20.835 20.845 214
## 2016-01-16 03:00:00 20.845 20.850 20.835 20.845 103
## 2016-01-16 04:00:00 20.845 20.855 20.835 20.845 180
## 2016-01-16 05:00:00 20.845 20.845 20.845 20.845 211
## 2016-01-16 06:00:00 20.845 20.845 20.840 20.845 35
# Extract the close column
dc_close <- Cl(DC)
# Look at the head of dc_close
head(dc_close)
## DC.Close
## 2016-01-16 01:00:00 20.845
## 2016-01-16 02:00:00 20.845
## 2016-01-16 03:00:00 20.845
## 2016-01-16 04:00:00 20.845
## 2016-01-16 05:00:00 20.845
## 2016-01-16 06:00:00 20.845
# Extract the volume column
dc_volume <- Vo(DC)
# Look at the head of dc_volume
head(dc_volume)
## DC.Volume
## 2016-01-16 01:00:00 157
## 2016-01-16 02:00:00 214
## 2016-01-16 03:00:00 103
## 2016-01-16 04:00:00 180
## 2016-01-16 05:00:00 211
## 2016-01-16 06:00:00 35# Extract the high, low, and close columns
dc_hlc <- HLC(DC)
# Look at the head of dc_hlc
head(dc_hlc)
## DC.High DC.Low DC.Close
## 2016-01-16 01:00:00 20.850 20.835 20.845
## 2016-01-16 02:00:00 20.850 20.835 20.845
## 2016-01-16 03:00:00 20.850 20.835 20.845
## 2016-01-16 04:00:00 20.855 20.835 20.845
## 2016-01-16 05:00:00 20.845 20.845 20.845
## 2016-01-16 06:00:00 20.845 20.840 20.845
# Extract the open, high, low, close, and volume columns
dc_ohlcv <- OHLCV(DC)
# Look at the head of dc_ohlcv
head(dc_ohlcv)
## DC.Open DC.High DC.Low DC.Close DC.Volume
## 2016-01-16 01:00:00 20.845 20.850 20.835 20.845 157
## 2016-01-16 02:00:00 20.845 20.850 20.835 20.845 214
## 2016-01-16 03:00:00 20.845 20.850 20.835 20.845 103
## 2016-01-16 04:00:00 20.845 20.855 20.835 20.845 180
## 2016-01-16 05:00:00 20.845 20.845 20.845 20.845 211
## 2016-01-16 06:00:00 20.845 20.845 20.840 20.845 35# Download CME data for CL and BZ as an xts object
oil_data <- Quandl(code = c("CME/CLH2016", "CME/BZH2016"), type = "xts")
# Look at the column names of the oil_data object
colnames(oil_data)
## [1] "CME.CLH2016 - Open" "CME.CLH2016 - High"
## [3] "CME.CLH2016 - Low" "CME.CLH2016 - Last"
## [5] "CME.CLH2016 - Change" "CME.CLH2016 - Settle"
## [7] "CME.CLH2016 - Volume" "CME.CLH2016 - Open Interest"
## [9] "CME.BZH2016 - Open" "CME.BZH2016 - High"
## [11] "CME.BZH2016 - Low" "CME.BZH2016 - Last"
## [13] "CME.BZH2016 - Change" "CME.BZH2016 - Settle"
## [15] "CME.BZH2016 - Volume" "CME.BZH2016 - Open Interest"
# Extract the Open price for CLH2016
cl_open <- getPrice(oil_data, symbol = "CLH2016", prefer = "Open$") #$ is to prevent you from also retrieving 'Open Interest'
# Look at January, 2016 using xts' ISO-8601 subsetting
cl_open["2016-01"]
## CME.CLH2016 - Open
## 2016-01-04 38.75
## 2016-01-05 38.07
## 2016-01-06 37.39
## 2016-01-07 35.35
## 2016-01-08 34.50
## 2016-01-11 34.11
## 2016-01-12 32.24
## 2016-01-13 31.60
## 2016-01-14 31.47
## 2016-01-15 32.11
## 2016-01-19 30.17
## 2016-01-20 29.47
## 2016-01-21 28.35
## 2016-01-22 29.84
## 2016-01-25 32.05
## 2016-01-26 29.81
## 2016-01-27 30.55
## 2016-01-28 32.19
## 2016-01-29 33.70# CL and BZ Quandl codes
quandl_codes <- c("CME/CLH2016","CME/BZH2016")
# Download quarterly CL and BZ prices
qtr_price <- Quandl(quandl_codes, collapse = "quarterly", type = "xts") #collapse gives the last observation of the period
# View the high prices for both series
Hi(qtr_price)
## CME.CLH2016 - High CME.BZH2016 - High
## 2010 Q4 92.94 NA
## 2011 Q1 101.43 NA
## 2011 Q2 100.19 NA
## 2011 Q3 91.59 NA
## 2011 Q4 0.00 0.00
## 2012 Q1 0.00 0.00
## 2012 Q2 0.00 0.00
## 2012 Q3 0.00 0.00
## 2012 Q4 0.00 0.00
## 2013 Q1 0.00 0.00
## 2013 Q2 0.00 0.00
## 2013 Q3 0.00 0.00
## 2013 Q4 0.00 0.00
## 2014 Q1 86.00 0.00
## 2014 Q2 93.05 NA
## 2014 Q3 0.00 NA
## 2014 Q4 61.03 69.45
## 2015 Q1 57.17 64.45
## 2015 Q2 61.63 66.72
## 2015 Q3 47.95 51.37
## 2015 Q4 38.87 38.34
## 2016 Q1 32.05 35.00
# Download quarterly CL and BZ returns
qtr_return <- Quandl(quandl_codes, collapse = "quarterly", type = "xts", transform = "rdiff")
# View the settle price returns for both series
getPrice(qtr_return, prefer = "Settle")
## CME.CLH2016 - Settle CME.BZH2016 - Settle
## 2011 Q1 0.091349258 NA
## 2011 Q2 -0.012225180 NA
## 2011 Q3 -0.085836910 NA
## 2011 Q4 -0.012337591 NA
## 2012 Q1 0.036038028 0.040508511
## 2012 Q2 -0.074797268 -0.040381031
## 2012 Q3 0.016030446 0.029779888
## 2012 Q4 0.008286039 0.006496228
## 2013 Q1 -0.011595182 0.001561524
## 2013 Q2 -0.045899772 -0.041679659
## 2013 Q3 0.025784887 0.027440347
## 2013 Q4 -0.011521005 0.038847250
## 2014 Q1 0.014480810 0.008535718
## 2014 Q2 0.079377974 0.052191436
## 2014 Q3 -0.069454897 -0.070669348
## 2014 Q4 -0.295436164 -0.306543019
## 2015 Q1 -0.073958675 -0.073551263
## 2015 Q2 0.088188419 0.071692061
## 2015 Q3 -0.229129373 -0.237503741
## 2015 Q4 -0.194215748 -0.260647694
## 2016 Q1 -0.175268535 -0.079108044# Creat a new environment
data_env <- new.env()
# Load data into the environment
getSymbols(Symbols = c("QQQ"), env = data_env, auto.assign = TRUE) #This will not generate the exact same restult as is in datacamp but the point is to show how to combine objects
## [1] "QQQ"
# Call head on each object in data_env using eapply
data_list <- eapply(data_env, head)
head(data_list)
## $QQQ
## QQQ.Open QQQ.High QQQ.Low QQQ.Close QQQ.Volume QQQ.Adjusted
## 2007-01-03 47.832 48.492 46.797 43.24 167689500 39.28798
## 2007-01-04 47.656 48.657 47.491 44.06 136853500 40.03301
## 2007-01-05 48.371 48.371 47.854 43.85 138958800 39.84221
## 2007-01-08 48.305 48.558 48.030 43.88 106401600 39.86947
## 2007-01-09 48.437 48.745 48.019 44.10 121577500 40.06936
## 2007-01-10 48.382 49.152 48.228 44.62 121070100 40.54183
# Merge all the list elements into one xts object
data_merged <- do.call(merge, data_list)
head(data_merged)
## QQQ.Open QQQ.High QQQ.Low QQQ.Close QQQ.Volume QQQ.Adjusted
## 2007-01-03 47.832 48.492 46.797 43.24 167689500 39.28798
## 2007-01-04 47.656 48.657 47.491 44.06 136853500 40.03301
## 2007-01-05 48.371 48.371 47.854 43.85 138958800 39.84221
## 2007-01-08 48.305 48.558 48.030 43.88 106401600 39.86947
## 2007-01-09 48.437 48.745 48.019 44.10 121577500 40.06936
## 2007-01-10 48.382 49.152 48.228 44.62 121070100 40.54183
# Ensure the columns are ordered: open, high, low, close
data_ohlc <- OHLC(data_merged)
head(data_ohlc)
## QQQ.Open QQQ.High QQQ.Low QQQ.Close
## 2007-01-03 47.832 48.492 46.797 43.24
## 2007-01-04 47.656 48.657 47.491 44.06
## 2007-01-05 48.371 48.371 47.854 43.85
## 2007-01-08 48.305 48.558 48.030 43.88
## 2007-01-09 48.437 48.745 48.019 44.10
## 2007-01-10 48.382 49.152 48.228 44.62# Symbols
symbols <- c("AAPL", "MSFT", "IBM")
# Create new environment
data_env <- new.env()
# Load symbols into data_env
getSymbols(Symbols = symbols, env = data_env)
## [1] "AAPL" "MSFT" "IBM"
# Extract the close column from each object and merge into one xts object
close_data <- do.call(merge, eapply(data_env, Cl))
# View the head of close_data
head(close_data)
## AAPL.Close IBM.Close MSFT.Close
## 2007-01-03 83.80000 97.27 29.86
## 2007-01-04 85.66000 98.31 29.81
## 2007-01-05 85.04999 97.42 29.64
## 2007-01-08 85.47000 98.90 29.93
## 2007-01-09 92.57000 100.07 29.96
## 2007-01-10 97.00000 98.89 29.66# Set the default to pull data from Google Finance
setDefaults(getSymbols, src = "google")
# Get GOOG data
getSymbols("GOOG")
## [1] "GOOG"
# Verify the data was actually pulled from Google Finance
str(GOOG)
## An 'xts' object on 2007-01-03/2017-06-30 containing:
## Data: num [1:2642, 1:5] 233 234 241 244 242 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:5] "GOOG.Open" "GOOG.High" "GOOG.Low" "GOOG.Close" ...
## Indexed by objects of class: [Date] TZ: UTC
## xts Attributes:
## List of 2
## $ src : chr "google"
## $ updated: POSIXct[1:1], format: "2017-07-01 13:34:11"# Look at getSymbols.yahoo arguments
args(getSymbols.yahoo)
## function (Symbols, env, return.class = "xts", index.class = "Date",
## from = "2007-01-01", to = Sys.Date(), ..., periodicity = "daily")
## NULL
# Set default 'from' value for getSymbols.yahoo
setDefaults(getSymbols.yahoo, from = "2000-01-01")
# Confirm defaults were set correctly
getDefaults("getSymbols.yahoo")
## $from
## [1] "'2000-01-01'"# Load CP data using defaults where default source is yahoo
getSymbols("CP", src = "yahoo") # Canadian railway stock prices
## [1] "CP"
# Look at the first few rows of CP
head(CP)
## CP.Open CP.High CP.Low CP.Close CP.Volume CP.Adjusted
## 2000-01-03 29.069 29.236 27.896 20.8750 358200 7.787133
## 2000-01-04 27.729 29.153 27.729 21.0000 1160200 7.833765
## 2000-01-05 28.147 29.153 28.147 21.5625 852200 8.043595
## 2000-01-06 29.404 30.744 29.236 22.6250 1960400 8.439946
## 2000-01-07 30.493 31.163 29.990 23.0000 1758800 8.579834
## 2000-01-10 30.912 31.331 30.074 23.1875 1273600 8.649777
# Set the source for CP to FRED
setSymbolLookup(CP = list(src = "FRED"))
# Load CP data again
getSymbols("CP")
## [1] "CP"
# Look at the first few rows of CP
head(CP)
## CP.Open CP.High CP.Low CP.Close CP.Volume
## 2007-01-03 53.43 53.68 52.40 52.74 207200
## 2007-01-04 52.42 52.48 51.54 51.62 238700
## 2007-01-05 51.63 51.77 51.39 51.53 180000
## 2007-01-08 51.23 52.14 51.21 51.94 276200
## 2007-01-09 51.44 52.42 51.44 52.25 419300
## 2007-01-10 52.21 52.41 51.92 52.07 206100# Save symbol lookup table
saveSymbolLookup(file = "my_symbol_lookup.rda")
# Set default source for CP to "yahoo"
setSymbolLookup(CP = "yahoo")
# Verify the default source is "yahoo"
getSymbolLookup("CP")
## $CP
## $CP$src
## [1] "yahoo"
# Load symbol lookup table
loadSymbolLookup(file = "my_symbol_lookup.rda")
# Verify the default source is "FRED"
getSymbolLookup("CP")
## $CP
## $CP$src
## [1] "FRED"The answer is Call getSymbols.source directly.
# Load BRK-A data
getSymbols("BRK-A")
## [1] "BRK-A"
# Use backticks and head() to look at the loaded data
head(`BRK-A`)
## BRK-A.Open BRK-A.High BRK-A.Low BRK-A.Close BRK-A.Volume
## 2007-01-03 110400 110600 109000.0 109000.0 670
## 2007-01-04 109400 109400 108250.0 108850.0 460
## 2007-01-05 108800 108800 107000.1 107200.0 420
## 2007-01-08 107600 107800 107300.0 107500.1 340
## 2007-01-09 107510 109600 107510.0 109350.0 400
## 2007-01-10 109350 109550 108500.0 109490.0 370
# Use get() to assign the BRK-A data to an object named BRK.A
BRK.A <- get("BRK-A")# Create BRK.A object
BRK.A <- getSymbols("BRK-A", auto.assign = FALSE)
# Create col_names object with the column names of BRK.A
col_names <- colnames(BRK.A)
# Set BRK.A column names to syntactically valid names
colnames(BRK.A) <- make.names(col_names)# Set name for BRK-A to BRK.A
setSymbolLookup(BRK.A = list(name = "BRK-A"))
# Set name for T (AT&T) to ATT
setSymbolLookup(ATT = list(name = "T"))
# Load BRK.A and ATT data
getSymbols(c("BRK.A", "ATT"))
## [1] "BRK.A" "ATT"