将函数应用于 xts quantmod 的子集

Question

我正在尝试按年份获取股票价格的标准差，但每年都得到相同的值。

我尝试使用 dplyr (group_by, summarise) 和一个函数，但其​​中任何一个都没有运气，两者都返回相同的值 67.0。

它可能是传递了整个数据帧而没有对其进行子集化，如何解决这个问题？

library(quantmod)
library(tidyr)
library(dplyr)

#initial parameters
initialDate = as.Date('2010-01-01')
finalDate = Sys.Date()

ybeg = format(initialDate,"%Y")
yend = format(finalDate,"%Y")

ticker = "AAPL"

#getting stock prices
stock = getSymbols.yahoo(ticker, from=initialDate, auto.assign = FALSE)
stock = stock[,4] #working only with closing prices

使用 dplyr：

#Attempt 1 with dplyr - not working, all values by year return the same

stock = stock %>% zoo::fortify.zoo()
stock$Date = stock$Index
separate(stock, Date, c("year","month","day"), sep="-") %>% 
   group_by(year) %>%
   summarise(stdev= sd(stock[,2]))

# A tibble: 11 x 2
#   year  stdev
#   <chr> <dbl>
# 1 2010   67.0
# 2 2011   67.0
#....
#10 2019   67.0
#11 2020   67.0

与功能：

#Attempt 2 with function - not working - returns only one value instead of multiple

#getting stock prices
stock = getSymbols.yahoo(ticker, from=initialDate, auto.assign = FALSE)
stock = stock[,4] #working only with closing prices

#subsetting
years = as.character(seq(ybeg,yend,by=1))
years

calculate_stdev = function(series,years) {
  series[years] #subsetting by years, to be equivalent as stock["2010"], stock["2011"] e.g.
  sd(series[years][,1]) #calculate stdev on closing prices of the current subset
}

yearly.stdev = calculate_stdev(stock,years)

> yearly.stdev
[1] 67.04185

Answer 1

使用apply.yearly()（更通用的period.apply() 的便捷包装）对getSymbols() 返回的xts 对象的年度子集调用函数。

您可以使用Cl() 函数从getSymbols() 返回的对象中提取关闭列。

stock = getSymbols("AAPL", from = "2010-01-01", auto.assign = FALSE)
apply.yearly(Cl(stock), sd)
##            AAPL.Close
## 2010-12-31   5.365208
## 2011-12-30   3.703407
## 2012-12-31   9.568127
## 2013-12-31   6.412542
## 2014-12-31  13.371293
## 2015-12-31   7.683550
## 2016-12-30   7.640743
## 2017-12-29  14.621191
## 2018-12-31  20.593861
## 2019-12-31  34.538978
## 2020-06-19  29.577157

Answer 2

我不知道dplyr，但这是data.table 的方法

library(data.table)

# convert data.frame to data.table
setDT(stock)

# convert your Date column with content like "2020-06-17" from character to Date type
stock[,Date:=as.Date(Date)]

# calculate sd(price) grouped by year, assuming here your price column is named "price"
stock[,sd(price),year(Date)]

Answer 3

不要在 summarise 函数中再次传递数据框的名称。请改用变量名。

separate(stock, Date, c("year","month","day"), sep="-") %>% 
  group_by(year) %>% 
  summarise(stdev = sd(AAPL.Close)) # <-- here
# A tibble: 11 x 2
#   year  stdev
#   <chr> <dbl>
# 1 2010   5.37
# 2 2011   3.70
# 3 2012   9.57
# 4 2013   6.41
# 5 2014  13.4 
# 6 2015   7.68
# 7 2016   7.64
# 8 2017  14.6 
# 9 2018  20.6 
#10 2019  34.5 
#11 2020  28.7