用于计算昨天收盘时的每日变化百分比并将其应用于多个代码的功能

Question

在社区的帮助下，我创建了从.csv文件列表中下载多个代码的代码，并为所有这些代码创建了每日范围函数。现在我想创建一个类似的函数来计算每个股票代码的每日百分比变化（％）并应用于所有这些。

这是我的方法：

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)

有关如何创建％函数的任何帮助？

非常感谢。

Answer 1

创建像下面这样的功能就可以了

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

您也可以使用以下内容，但标题是每日。对于列表中的每个股票，都会返回。

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

Answer 2

使用data.table的替代方案，您可以尝试：

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