Motivation
One of the great things of working in finance is that financial
datasets from capital markets are freely available from sources such as
Yahoo Finance. This is an excelent feature for building up to date
content for classes and conducting academic research.
In the past I have used function GetSymbols from the CRAN package quantmod in order
to download end of day trade data for several stocks in the financial
market. The problem in using GetSymbols is that it does not aggregate or
clean the financial data for several tickers. In the usage of
GetSymbols, each stock will have its own xts
object with
different column names and this makes it harder to store data from
several tickers in a single dataframe.
Package BatchGetSymbols is my solution to this problem. Based on a
list of tickers and a time period, BatchGetSymbols will download price
data from yahoo finance and organize it so that you don’t need to worry
about cleaning it yourself.
A simple example
As a simple exercise, let’s download data for three stocks, facebook
(FB), 3M (MMM), PETR4.SA (PETROBRAS) and abcdef, a ticker I just made
up. We will use the last 60 days as the time period. This example will
show the simple interface of the package and how it handles invalid
tickers.
if (!require(BatchGetSymbols)) install.packages('BatchGetSymbols')
## Loading required package: BatchGetSymbols
## Loading required package: rvest
## Loading required package: dplyr
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
##
library(BatchGetSymbols)
# set dates
first.date <- Sys.Date() - 60
last.date <- Sys.Date()
freq.data <- 'daily'
# set tickers
tickers <- c('FB','MMM','PETR4.SA','abcdef')
l.out <- BatchGetSymbols(tickers = tickers,
first.date = first.date,
last.date = last.date,
freq.data = freq.data,
cache.folder = file.path(tempdir(),
'BGS_Cache') ) # cache in tempdir()
## Warning: `BatchGetSymbols()` was deprecated in BatchGetSymbols 2.6.4.
## Please use `yfR::yf_get()` instead.
## 2022-05-01: Package BatchGetSymbols will soon be replaced by yfR.
## More details about the change is available at github <<www.github.com/msperlin/yfR>
## You can install yfR by executing:
##
## remotes::install_github('msperlin/yfR')
##
## Running BatchGetSymbols for:
##
## tickers =FB, MMM, PETR4.SA, abcdef
## Downloading data for benchmark ticker
## ^GSPC | yahoo (1|1) | Not Cached | Saving cache
## FB | yahoo (1|4) | Not Cached | Saving cache - Got 100% of valid prices | Feels good!
## MMM | yahoo (2|4) | Not Cached | Saving cache - Got 100% of valid prices | Good stuff!
## PETR4.SA | yahoo (3|4) | Not Cached | Saving cache - Got 98% of valid prices | Good job!
## abcdef | yahoo (4|4) | Not Cached - Error in download..
After downloading the data, we can check the success of the process
for each ticker. Notice that the last ticker does not exist in yahoo
finance and therefore results in an error. All information regarding the
download process is provided in the dataframe df.control:
## # A tibble: 3 × 6
## ticker src download.status total.obs perc.benchmark.dates threshold.decis…
## <chr> <chr> <chr> <int> <dbl> <chr>
## 1 FB yahoo OK 42 1 KEEP
## 2 MMM yahoo OK 42 1 KEEP
## 3 PETR4.SA yahoo OK 41 0.976 KEEP
Moreover, we can plot the daily closing prices using ggplot2:
library(ggplot2)
p <- ggplot(l.out$df.tickers, aes(x = ref.date, y = price.close))
p <- p + geom_line()
p <- p + facet_wrap(~ticker, scales = 'free_y')
print(p)
![](data:image/png;base64,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)
Downloading data for all tickers in the SP500 index
The package was designed for large scale download of financial data.
An example is downloading all stocks in the current composition of the
SP500 stock index. The package also includes a function that downloads
the current composition of the SP500 index from the internet. By using
this function along with BatchGetSymbols, we can easily import
end-of-day data for all assets in the index.
In the following code we download data for the SP500 stocks for the
last year. The code is not executed in this vignette given its time
duration, but you can just copy and paste on its own R script in order
to check the results. In my computer it takes around 5 minutes to
download the whole dataset.
library(BatchGetSymbols)
first.date <- Sys.Date()-365
last.date <- Sys.Date()
df.SP500 <- GetSP500Stocks()
tickers <- df.SP500$Tickers
l.out <- BatchGetSymbols(tickers = tickers,
first.date = first.date,
last.date = last.date)
print(l.out$df.control)
print(l.out$df.tickers)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.