Widget HTML Atas

Home / Download Stock Data Using R

Download Stock Data Using R

Post a Comment
  • Importing and Managing Financial Data in R
    • Chapter 1: Introduction and downloading data
      • 1.1 Introducing getSymbols()
      • 1.2 Data sources
      • 1.3 Make getSymbols() return the data it retrieves
      • 1.4 Introducing Quandl()
      • 1.5 Return data type
      • 1.6 Find stock ticker from Google Finance
      • 1.7 Download exchange rate data from Oanda
      • 1.8 Find and download US Civilian Unemployment Rate data from FRED
    • Chapter 2: Extracting and transforming data
      • 2.1 Extract one column from one instrument
      • 2.2 Extract multiple columns from one instrument
      • 2.3 Use getPrice to extract other columns
      • 2.4 Use Quandl to download weekly returns data
      • 2.5 Combine objects from an environment using do.call and eapply
      • 2.6 Use quantmod to download multiple instruments and extract the close column
  • Quiz
    • Retrieve the data for the period between 2010-12-3 and 2013-12-31, using the packages and functions you learned in this course. And plot them using the plot function.
    • 1. Apple stock price (adjusted close)
    • 2. New Hampshire gross domestic product
    • 3. exchange rate, US Dollar per South Korean Won (for the past 180 days)

Importing and Managing Financial Data in R

Chapter 1: Introduction and downloading data

Load packages

              # Load packages library(quantmod)

1.1 Introducing getSymbols()

                                  # 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 2007-01-03/2017-07-14 containing: ##   Data: num [1:2652, 1:6] 39.5 39.3 39.9 39.9 40 ... ##  - 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: "2017-07-15 12:27:20"  # 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   39.488   40.033  38.634  39.28798  167689500        43.24 ## 2007-01-04   39.342   40.169  39.206  40.03301  136853500        44.06 ## 2007-01-05   39.933   39.933  39.506  39.84221  138958800        43.85 ## 2007-01-08   39.879   40.088  39.651  39.86947  106401600        43.88 ## 2007-01-09   39.988   40.242  39.642  40.06936  121577500        44.10 ## 2007-01-10   39.942   40.578  39.815  40.54183  121070100        44.62

1.2 Data sources

                # 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 2007-01-03/2017-07-14 containing: ##   Data: num [1:2651, 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: "2017-07-15 12:27:21"  # 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: "2017-07-15 12:27:21"

1.3 Make getSymbols() return the data it retrieves

                # Load the quantmod package library(quantmod)  # Assign SPY data to object named 'spy' 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 2007-01-03/2017-07-14 containing: ##   Data: num [1:2652, 1:6] 114 114 114 113 114 ... ##  - 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: "2017-07-15 12:27:22"  # Assign JNJ data to object named 'jnj' using env argument jnj <- getSymbols(Symbols = "JNJ", env = NULL)  # Look at the structure of the 'jnj' object str(jnj) ## An 'xts' object on 2007-01-03/2017-07-14 containing: ##   Data: num [1:2652, 1:6] 48 47.8 48.5 48.3 48.3 ... ##  - 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: "2017-07-15 12:27:22"

1.4 Introducing 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"

1.5 Return data type

                # 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: 4

1.6 Find stock ticker from Google Finance

                # 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   34548400

1.7 Download exchange rate data from Oanda

                # 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 2017-01-17/2017-07-14 containing: ##   Data: num [1:179, 1] 1.6 1.62 1.63 1.64 1.65 ... ##  - 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: "2017-07-15 12:27:24"  # Try to load data from 190 days ago getSymbols(Symbols = currency_pair, from = Sys.Date() - 190, to = Sys.Date(), src = "oanda") ## [1] "GBPCAD"

1.8 Find and download US Civilian Unemployment Rate data from FRED

                # Create a series_name object series_name <- "UNRATE"  # 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)

Chapter 2: Extracting and transforming data

              # Provide your api_key  Quandl.api_key('v55sdwPxRDW1BeHgtzz2')  #Import GDP data from FRED gdp <- Quandl(code = "FRED/GDP")

2.1 Extract one column from one instrument

                # Load data DC <- read.csv("~/resources/rstudio/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

2.2 Extract multiple columns from one instrument

                # 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

2.3 Use getPrice to extract other columns

                # 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$")  # 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

2.4 Use Quandl to download weekly returns data

                # 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")  # 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

2.5 Combine objects from an environment using do.call and eapply

                # Creat a new environment data_env <- new.env()  # Load data into the environment getSymbols(Symbols = c("QQQ"), env = data_env, auto.assign = TRUE) ## [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   39.488   40.033  38.634  39.28798  167689500        43.24 ## 2007-01-04   39.342   40.169  39.206  40.03301  136853500        44.06 ## 2007-01-05   39.933   39.933  39.506  39.84221  138958800        43.85 ## 2007-01-08   39.879   40.088  39.651  39.86947  106401600        43.88 ## 2007-01-09   39.988   40.242  39.642  40.06936  121577500        44.10 ## 2007-01-10   39.942   40.578  39.815  40.54183  121070100        44.62  # 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   39.488   40.033  38.634  39.28798  167689500        43.24 ## 2007-01-04   39.342   40.169  39.206  40.03301  136853500        44.06 ## 2007-01-05   39.933   39.933  39.506  39.84221  138958800        43.85 ## 2007-01-08   39.879   40.088  39.651  39.86947  106401600        43.88 ## 2007-01-09   39.988   40.242  39.642  40.06936  121577500        44.10 ## 2007-01-10   39.942   40.578  39.815  40.54183  121070100        44.62  # 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   39.488   40.033  38.634  39.28798 ## 2007-01-04   39.342   40.169  39.206  40.03301 ## 2007-01-05   39.933   39.933  39.506  39.84221 ## 2007-01-08   39.879   40.088  39.651  39.86947 ## 2007-01-09   39.988   40.242  39.642  40.06936 ## 2007-01-10   39.942   40.578  39.815  40.54183

2.6 Use quantmod to download multiple instruments and extract the close column

                # 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   10.81246  76.98791   23.20394 ## 2007-01-04   11.05245  77.81103   23.16508 ## 2007-01-05   10.97374  77.10663   23.03297 ## 2007-01-08   11.02793  78.27800   23.25834 ## 2007-01-09   11.94403  79.20407   23.28165 ## 2007-01-10   12.51562  78.27012   23.04851

Quiz

Retrieve the data for the period between 2010-12-3 and 2013-12-31, using the packages and functions you learned in this course. And plot them using the plot function.

1. Apple stock price (adjusted close)

              apple_stock <- getSymbols("AAPL", from = "2010-12-31", to = "2013-12-31", auto.assign = FALSE)  head(apple_stock) ##            AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume ## 2010-12-31    41.669    41.738   41.458   41.61895    48377000 ## 2011-01-03    42.016    42.612   41.913   42.52343   111284600 ## 2011-01-04    42.894    42.901   42.340   42.74536    77270200 ## 2011-01-05    42.521    43.139   42.514   43.09501    63879900 ## 2011-01-06    43.188    43.256   42.953   43.06018    75107200 ## 2011-01-07    43.094    43.398   42.824   43.36855    77982800 ##            AAPL.Adjusted ## 2010-12-31      46.08000 ## 2011-01-03      47.08143 ## 2011-01-04      47.32715 ## 2011-01-05      47.71429 ## 2011-01-06      47.67571 ## 2011-01-07      48.01714  plot(apple_stock)

2. New Hampshire gross domestic product

              nh_gdp <- getSymbols("NHNGSP", src = "FRED", from = "2010-12-31", to = "2013-12-31", auto.assign = FALSE)  head(nh_gdp) ##            NHNGSP ## 1997-01-01  38400 ## 1998-01-01  40652 ## 1999-01-01  42027 ## 2000-01-01  45519 ## 2001-01-01  46967 ## 2002-01-01  49091  plot(nh_gdp)

3. exchange rate, US Dollar per South Korean Won (for the past 180 days)

                              exchange_rate <- getSymbols("USD/KRW", src = "oanda", auto.assign = FALSE)  head(exchange_rate) ##             USD.KRW ## 2017-01-17 1171.440 ## 2017-01-18 1168.635 ## 2017-01-19 1177.580 ## 2017-01-20 1174.195 ## 2017-01-21 1176.055 ## 2017-01-22 1175.230  plot(exchange_rate)

Source: https://rstudio-pubs-static.s3.amazonaws.com/288984_5d7850e6801447c29762b3162e8460be.html

Posted by: lift-today.blogspot.com