Function to calculate daily % change from yesterday close and apply it to multiple tickers

user_9985437 0
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With the help of community I've created the code to download multiple tickers from a .csv file list and create a daily range function to all of them. Now I would like to create a similar function to calculate the daily percentage change (%) of each ticker and apply to all of them.

Here is my approach:

Load my tickers list from a .csv file, create a list with all of them.

library(quantmod)
Tickers <- read.csv("nasdaq_tickers_list.csv", stringsAsFactors = FALSE)
getSymbols(Tickers$Tickers,from="2018-01-01", src="yahoo" )
stock_data = sapply(.GlobalEnv, is.xts)
all_stocks <- do.call(list, mget(names(stock_data)[stock_data]))

Percentage function?

Percentage <- function(x) {
stock_name <- stringi::stri_extract(names(x)[1], regex = "^[A-Z]+")
stock_name <- paste0(stock_name, ".percentage")
column_names <- c(names(x), stock_name)
x$percentage <- quantmod::Close(today) - quantmod::Close(yesterday)/100 
x <- setNames(x, column_names)
return(x)
}

calculate percentages and add them to the data

all_stocks <- lapply(all_stocks, percentage_change)

Any help on how to create the %function?

Thanks a lot.

quantmod percentage xts r
2 Answers

Creating the function like below does the trick

percentage_change <- function(x) {
  stock_name <- stringi::stri_extract(names(x)[1], regex = "^[A-Z]+")
  stock_name <- paste0(stock_name, ".%change")
  column_names <- c(names(x), stock_name)
  x$change <- (quantmod::Cl(x) - quantmod::Lag(quantmod::Cl(x)))/quantmod::Lag(quantmod::Cl(x)) * 100
  x <- setNames(x, column_names)
  return(x)
}

all_stocks <- lapply(all_stocks, percentage_change)

head(all_stocks$MSFT)
           MSFT.Open MSFT.High MSFT.Low MSFT.Close MSFT.Volume MSFT.Adjusted MSFT.%change
2007-01-03     29.91     30.25    29.40      29.86    76935100      22.67236           NA
2007-01-04     29.70     29.97    29.44      29.81    45774500      22.63439   -0.1674548
2007-01-05     29.63     29.75    29.45      29.64    44607200      22.50531   -0.5702784
2007-01-08     29.65     30.10    29.53      29.93    50220200      22.72550    0.9784110
2007-01-09     30.00     30.18    29.73      29.96    44636600      22.74828    0.1002305
2007-01-10     29.80     29.89    29.43      29.66    55017400      22.52049   -1.0013318

You could also use the following, but then the header is daily.returns for every stock in your list.

all_stocks  <- lapply(all_stocks , function(x) merge(x, dailyReturn(x, leading = FALSE) * 100))

head(all_stocks$MSFT)
           MSFT.Open MSFT.High MSFT.Low MSFT.Close MSFT.Volume MSFT.Adjusted daily.returns
2007-01-03     29.91     30.25    29.40      29.86    76935100      22.67236            NA
2007-01-04     29.70     29.97    29.44      29.81    45774500      22.63439    -0.1674548
2007-01-05     29.63     29.75    29.45      29.64    44607200      22.50531    -0.5702784
2007-01-08     29.65     30.10    29.53      29.93    50220200      22.72550     0.9784110
2007-01-09     30.00     30.18    29.73      29.96    44636600      22.74828     0.1002305
2007-01-10     29.80     29.89    29.43      29.66    55017400      22.52049    -1.0013318
user_49851760

An alternative using data.table, you can try:

library(quantmod)
setSymbolLookup(QQQ='yahoo',SPY='google')
getSymbols(c('QQQ', 'SPY'),from="2018-01-01", src ="yahoo" )
stock_data = sapply(.GlobalEnv, is.xts)
all_stocks <- do.call(list, mget(names(stock_data)[stock_data])) 
library(data.table)
lapply(all_stocks,
       function(x) as.data.table(x)[, paste0(gsub("\\..*","",names(x)[1]), '.prctg') := 
                                      dailyReturn(x, leading = FALSE)*100])
$`QQQ`
          index QQQ.Open QQQ.High QQQ.Low QQQ.Close QQQ.Volume QQQ.Adjusted  QQQ.prctg
  1: 2018-01-02   156.56   158.53  156.17    158.49   32573300     157.8953         NA
  2: 2018-01-03   158.64   160.17  158.61    160.03   29383600     159.4296  0.9716663
  3: 2018-01-04   160.58   160.79  160.08    160.31   24776100     159.7085  0.1749666
  4: 2018-01-05   161.07   162.03  160.77    161.92   26992300     161.3125  1.0043042
  5: 2018-01-08   161.92   162.63  161.86    162.55   23159100     161.9401  0.3890841
 ---                                                                                  
174: 2018-09-10   182.15   182.25  180.73    181.72   26132000     181.7200  0.3368119
175: 2018-09-11   180.99   183.42  180.52    183.12   30116500     183.1200  0.7704127
176: 2018-09-12   182.85   182.98  181.01    182.58   36204000     182.5800 -0.2948848
177: 2018-09-13   183.72   184.88  183.64    184.53   31133200     184.5300  1.0680233
178: 2018-09-14   184.66   184.95  183.26    183.99   31215200     183.9900 -0.2926321

$SPY
          index SPY.Open SPY.High SPY.Low SPY.Close SPY.Volume SPY.Adjusted  SPY.prctg
  1: 2018-01-02   267.84   268.81  267.40    268.77   86655700     266.5012         NA
  2: 2018-01-03   268.96   270.64  268.96    270.47   90070400     268.1868 0.63251556
  3: 2018-01-04   271.20   272.16  270.54    271.61   80636400     269.3172 0.42148260
  4: 2018-01-05   272.51   273.56  271.95    273.42   83524000     271.1119 0.66640702
  5: 2018-01-08   273.31   274.10  272.98    273.92   57319200     271.6077 0.18286884
 ---                                                                                  
174: 2018-09-10   288.74   289.04  287.88    288.10   50210900     288.1000 0.17385257
175: 2018-09-11   287.37   289.55  286.98    289.05   50530500     289.0500 0.32974036
176: 2018-09-12   289.06   289.80  288.23    289.12   59810800     289.1200 0.02421969
177: 2018-09-13   290.32   291.04  290.00    290.83   51034200     290.8300 0.59144716
178: 2018-09-14   291.06   291.27  290.00    290.88   54962300     290.8800 0.01719836
user_43633150
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