How to loop through objects in the global environment - R

问题

I have looked far and wide for a solution to this issue, but I cannot seem to figure it out. I do not have much experience working with xts objects in R.

I have 40 xts objects (ETF data) and I want to run the quantmod function WeeklyReturn on each of them individually.

I have tried to refer to them by using the ls() function:

lapply(ls(), weeklyReturn)

I have also tried the object() function

lapply(object(), weeklyReturn)

I have also tried using as.xts() in my call to coerce the ls() objects to be used as xts but to no avail.

How can I run this function on every xts object in the environment?

Thank you,

回答1:

It would be better to load all of your xts objects into a list or create them in a way that returns them in a list to begin with. Then you could do results = lapply(xts.list, weeklyReturn).

To work with objects in the global environment, you could test for whether the object is an xts object and then run weeklyReturn on it if it is. Something like this:

results = lapply(setNames(ls(), ls()), function(i) {
  x = get(i)
  if(is.xts(x)) {
    weeklyReturn(x)
  }
})

results = results[!sapply(results, is.null)]

Or you could select only the xts objects to begin with:

results = sapply(ls()[sapply(ls(), function(i) is.xts(get(i)))],
       function(i) weeklyReturn(get(i)), simplify=FALSE, USE.NAMES=TRUE)

lapply(ls(), weeklyReturn) doesn't work, because ls() returns the object names as strings. The get function takes a string as an argument and returns the object with that name.

回答2:

An alternate solution using the tidyquant package. Note that this is data frame based so I will not be working with xts objects. I use two core functions to scale the analysis. First, tq_get() is used to go from a vector of ETF symbols to getting the prices. Second, tq_transmute() is used to apply the weeklyReturn function to the adjusted prices.

library(tidyquant)

etf_vec <- c("SPY", "QEFA", "TOTL", "GLD")

# Use tq_get to get prices
etf_prices <- tq_get(etf_vec, get = "stock.prices", from = "2017-01-01", to = "2017-05-31")
etf_prices
#> # A tibble: 408 x 8
#>    symbol       date    open    high     low  close   volume adjusted
#>     <chr>     <date>   <dbl>   <dbl>   <dbl>  <dbl>    <dbl>    <dbl>
#>  1    SPY 2017-01-03 227.121 227.919 225.951 225.24 91366500 223.1760
#>  2    SPY 2017-01-04 227.707 228.847 227.696 226.58 78744400 224.5037
#>  3    SPY 2017-01-05 228.363 228.675 227.565 226.40 78379000 224.3254
#>  4    SPY 2017-01-06 228.625 229.856 227.989 227.21 71559900 225.1280
#>  5    SPY 2017-01-09 229.009 229.170 228.514 226.46 46265300 224.3848
#>  6    SPY 2017-01-10 228.575 229.554 228.100 226.46 63771900 224.3848
#>  7    SPY 2017-01-11 228.453 229.200 227.676 227.10 74650000 225.0190
#>  8    SPY 2017-01-12 228.595 228.847 227.040 226.53 72113200 224.4542
#>  9    SPY 2017-01-13 228.827 229.503 228.786 227.05 62717900 224.9694
#> 10    SPY 2017-01-17 228.403 228.877 227.888 226.25 61240800 224.1767
#> # ... with 398 more rows

# Use tq_transmute to apply weeklyReturn to multiple groups
etf_returns_w <- etf_prices %>%
    group_by(symbol) %>%
    tq_transmute(select = adjusted, mutate_fun = weeklyReturn)
etf_returns_w
#> # A tibble: 88 x 3
#> # Groups:   symbol [4]
#>    symbol       date weekly.returns
#>     <chr>     <date>          <dbl>
#>  1    SPY 2017-01-06   0.0087462358
#>  2    SPY 2017-01-13  -0.0007042173
#>  3    SPY 2017-01-20  -0.0013653367
#>  4    SPY 2017-01-27   0.0098350474
#>  5    SPY 2017-02-03   0.0016159256
#>  6    SPY 2017-02-10   0.0094619381
#>  7    SPY 2017-02-17   0.0154636969
#>  8    SPY 2017-02-24   0.0070186222
#>  9    SPY 2017-03-03   0.0070964211
#> 10    SPY 2017-03-10  -0.0030618336
#> # ... with 78 more rows

来源：https://stackoverflow.com/questions/44707472/how-to-loop-through-objects-in-the-global-environment-r

标签

lapply

xts

quantmod