riingo
riingo allows you to access the Tiingo API for stock prices,
cryptocurrencies, and intraday feeds from the IEX (Investors Exchange).
This can serve as an alternate source of data to Yahoo Finance.
Installation
Install the stable version from CRAN with:
install.packages("riingo")Install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("DavisVaughan/riingo")API Token
The first thing you must do is create an account and set an API token. I recommend using the two functions below to help create your account and find the token.
riingo_browse_signup()
riingo_browse_token() # This requires that you are signed in on the site once you sign upOnce you have signed up and have an API token, I recommmend setting the
token as an environment variable, RIINGO_TOKEN in an .Renviron file.
The easiest way to do this is with usethis.
usethis::edit_r_environ()
# Then add a line in the environment file that looks like:
RIINGO_TOKEN = token_hereDo not put the token in quotes, and restart R after you have set it.
See the documentation ?riingo_get_token() for more information.
Basic example
library(riingo)Let’s grab some data with riingo. The default parameters attempt to
get 1 year’s worth of data.
riingo_prices("AAPL")
#> # A tibble: 252 x 14
#> ticker date close high low open volume adjClose
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 AAPL 2017-04-11 00:00:00 142. 143. 140. 143. 30379376 139.
#> 2 AAPL 2017-04-12 00:00:00 142. 142. 141. 142. 20350000 140.
#> 3 AAPL 2017-04-13 00:00:00 141. 142. 141. 142. 17822880 139.
#> 4 AAPL 2017-04-17 00:00:00 142. 142. 141. 141. 16582094 140.
#> 5 AAPL 2017-04-18 00:00:00 141. 142. 141. 141. 14697544 139.
#> 6 AAPL 2017-04-19 00:00:00 141. 142. 140. 142. 17328375 138.
#> 7 AAPL 2017-04-20 00:00:00 142. 143. 141. 141. 23319562 140.
#> 8 AAPL 2017-04-21 00:00:00 142. 143. 142. 142. 17320928 140.
#> 9 AAPL 2017-04-24 00:00:00 144. 144. 143. 144. 17116599 141.
#> 10 AAPL 2017-04-25 00:00:00 145. 145. 144. 144. 18216472 142.
#> # ... with 242 more rows, and 6 more variables: adjHigh <dbl>,
#> # adjLow <dbl>, adjOpen <dbl>, adjVolume <int>, divCash <dbl>,
#> # splitFactor <dbl>But of course you can try and get as much as is available…
riingo_prices("AAPL", start_date = "1950-01-01")
#> # A tibble: 9,412 x 14
#> ticker date close high low open volume adjClose
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 AAPL 1980-12-12 00:00:00 28.8 28.9 28.8 28.8 2093900 0.419
#> 2 AAPL 1980-12-15 00:00:00 27.2 27.4 27.2 27.4 785200 0.398
#> 3 AAPL 1980-12-16 00:00:00 25.2 25.4 25.2 25.4 472000 0.368
#> 4 AAPL 1980-12-17 00:00:00 25.9 26.0 25.9 25.9 385900 0.377
#> 5 AAPL 1980-12-18 00:00:00 26.6 26.8 26.6 26.6 327900 0.389
#> 6 AAPL 1980-12-19 00:00:00 28.2 28.4 28.2 28.2 217100 0.412
#> 7 AAPL 1980-12-22 00:00:00 29.6 29.8 29.6 29.6 166800 0.432
#> 8 AAPL 1980-12-23 00:00:00 30.9 31.0 30.9 30.9 209600 0.451
#> 9 AAPL 1980-12-24 00:00:00 32.5 32.6 32.5 32.5 214300 0.474
#> 10 AAPL 1980-12-26 00:00:00 35.5 35.6 35.5 35.5 248100 0.518
#> # ... with 9,402 more rows, and 6 more variables: adjHigh <dbl>,
#> # adjLow <dbl>, adjOpen <dbl>, adjVolume <int>, divCash <dbl>,
#> # splitFactor <dbl>And multiple tickers work as well.
riingo_prices(c("AAPL", "IBM"), start_date = "2001-01-01", end_date = "2005-01-01", resample_frequency = "monthly")
#> # A tibble: 98 x 14
#> ticker date close high low open volume adjClose
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 AAPL 2001-01-31 00:00:00 21.6 22.5 14.4 14.9 244811800 1.38
#> 2 AAPL 2001-02-28 00:00:00 18.2 21.9 18.0 20.7 125424400 1.16
#> 3 AAPL 2001-03-30 00:00:00 22.1 23.8 17.2 17.8 192840400 1.41
#> 4 AAPL 2001-04-30 00:00:00 25.5 27.1 18.8 22.1 199267700 1.63
#> 5 AAPL 2001-05-31 00:00:00 20.0 26.7 19.3 25.4 133364900 1.27
#> 6 AAPL 2001-06-29 00:00:00 23.2 25.1 19.4 20.1 136397600 1.48
#> 7 AAPL 2001-07-31 00:00:00 18.8 25.2 17.8 23.6 154555400 1.20
#> 8 AAPL 2001-08-31 00:00:00 18.6 19.9 17.3 19.0 91595700 1.18
#> 9 AAPL 2001-09-28 00:00:00 15.5 19.1 14.7 18.5 98777200 0.989
#> 10 AAPL 2001-10-31 00:00:00 17.6 19.4 14.8 15.5 134718700 1.12
#> # ... with 88 more rows, and 6 more variables: adjHigh <dbl>,
#> # adjLow <dbl>, adjOpen <dbl>, adjVolume <dbl>, divCash <dbl>,
#> # splitFactor <dbl>Intraday data
You can get limited intraday data with riingo_iex_prices(). This
gives you access to Tiingo’s direct feed to the IEX.
riingo_iex_prices("AAPL", resample_frequency = "1min")
#> # A tibble: 2,000 x 6
#> ticker date open high low close
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 AAPL 2018-04-05 16:26:00 173. 173. 173. 173.
#> 2 AAPL 2018-04-05 16:27:00 173. 173. 173. 173.
#> 3 AAPL 2018-04-05 16:28:00 173. 173. 173. 173.
#> 4 AAPL 2018-04-05 16:29:00 173. 173. 173. 173.
#> 5 AAPL 2018-04-05 16:30:00 173. 173. 173. 173.
#> 6 AAPL 2018-04-05 16:31:00 174. 174. 173. 173.
#> 7 AAPL 2018-04-05 16:32:00 173. 173. 173. 173.
#> 8 AAPL 2018-04-05 16:33:00 173. 173. 173. 173.
#> 9 AAPL 2018-04-05 16:34:00 173. 173. 173. 173.
#> 10 AAPL 2018-04-05 16:35:00 173. 173. 173. 173.
#> # ... with 1,990 more rowsSee the documentation for all of the restrictions.
Meta data
Meta data about each ticker is available through riingo_meta().
riingo_meta(c("AAPL", "QQQ"))
#> # A tibble: 2 x 6
#> ticker name startDate description endDate
#> <chr> <chr> <dttm> <chr> <dttm>
#> 1 AAPL Apple… 1980-12-12 00:00:00 Apple Inc. (Apple… 2018-04-11 00:00:00
#> 2 QQQ POWER… 1999-03-10 00:00:00 "PowerShares QQQ™… 2018-04-11 00:00:00
#> # ... with 1 more variable: exchangeCode <chr>Available tickers
You can check if a ticker is supported on Tiingo with
is_supported_ticker() and you can get a tibble of all supported
tickers with supported_tickers()
is_supported_ticker("AAPL")
#> [1] TRUE
tickers <- supported_tickers()
tickers
#> # A tibble: 64,992 x 6
#> ticker exchange assetType priceCurrency startDate
#> <chr> <chr> <chr> <chr> <dttm>
#> 1 000001 SHE Stock CNY 2007-08-30 00:00:00
#> 2 000002 SHE Stock CNY 2000-01-04 00:00:00
#> 3 000004 SHE Stock CNY 2007-08-31 00:00:00
#> 4 000005 SHE Stock CNY 2001-01-02 00:00:00
#> 5 000006 SHE Stock CNY 2007-08-31 00:00:00
#> 6 000007 SHE Stock CNY 2007-08-31 00:00:00
#> 7 000008 SHE Stock CNY 2000-01-03 00:00:00
#> 8 000009 SHE Stock CNY 2000-01-03 00:00:00
#> 9 000010 SHE Stock CNY 2007-08-31 00:00:00
#> 10 000011 SHE Stock CNY 2018-01-02 00:00:00
#> # ... with 64,982 more rows, and 1 more variable: endDate <dttm>Quote data
Another benefit of getting a feed from IEX is real time quote data. This includes TOP (top of book) bid and ask prices, along with most recent sale prices.
It is normal for some fields to return NA when outside of trading
hours.
riingo_iex_quote(c("AAPL", "QQQ"))
#> # A tibble: 2 x 17
#> ticker last askSize lastsaleTimeStamp bidPrice volume
#> <chr> <dbl> <int> <dttm> <dbl> <int>
#> 1 AAPL 172. 150 2018-04-11 16:41:05 172. 0
#> 2 QQQ 160. 400 2018-04-11 16:46:28 160. 0
#> # ... with 11 more variables: quoteTimestamp <dttm>, tngoLast <dbl>,
#> # mid <dbl>, bidSize <int>, prevClose <dbl>, timestamp <dttm>,
#> # high <dbl>, lastSize <int>, askPrice <dbl>, open <dbl>, low <dbl>Crypto data
Cryptocurrency data can be accessed with riingo_crypto_*() functions.
By default, 1 year’s worth is pulled if available. Some tickers go back
much further than others.
riingo_crypto_prices(c("btcusd", "btceur"))
#> # A tibble: 485 x 11
#> ticker baseCurrency quoteCurrency date open high
#> <chr> <chr> <chr> <dttm> <dbl> <dbl>
#> 1 btceur btc eur 2017-12-14 00:00:00 13873. 14354.
#> 2 btceur btc eur 2017-12-15 00:00:00 14076. 15195.
#> 3 btceur btc eur 2017-12-16 00:00:00 15073. 16881.
#> 4 btceur btc eur 2017-12-17 00:00:00 16573. 16933.
#> 5 btceur btc eur 2017-12-18 00:00:00 16010. 16439.
#> 6 btceur btc eur 2017-12-19 00:00:00 16051. 16329.
#> 7 btceur btc eur 2017-12-20 00:00:00 14994. 15408.
#> 8 btceur btc eur 2017-12-21 00:00:00 13855. 14912.
#> 9 btceur btc eur 2017-12-22 00:00:00 13314. 13562.
#> 10 btceur btc eur 2017-12-23 00:00:00 12702. 14465.
#> # ... with 475 more rows, and 5 more variables: low <dbl>, close <dbl>,
#> # volume <dbl>, volumeNotional <dbl>, tradesDone <dbl>Intraday data is available as well. The intraday ranges are not well documented, so it is a little hard to know what you can pull. From what I have discovered, you can pull a few days at a time, with the max date of intraday data being about ~4 months back (When the date was April 5, 2018, I could pull intraday data back to December 15, 2017, but only 5000 minutes at a time).
riingo_crypto_prices("btcusd", start_date = Sys.Date() - 5, end_date = Sys.Date(), resample_frequency = "1min")
#> # A tibble: 5,001 x 11
#> ticker baseCurrency quoteCurrency date open high low
#> <chr> <chr> <chr> <dttm> <dbl> <dbl> <dbl>
#> 1 btcusd btc usd 2018-04-06 00:00:00 6771. 6782. 6770.
#> 2 btcusd btc usd 2018-04-06 00:01:00 6772. 6777. 6763.
#> 3 btcusd btc usd 2018-04-06 00:02:00 6775. 6778. 6766.
#> 4 btcusd btc usd 2018-04-06 00:03:00 6774. 6776. 6770.
#> 5 btcusd btc usd 2018-04-06 00:04:00 6774. 6785. 6772.
#> 6 btcusd btc usd 2018-04-06 00:05:00 6782. 6787. 6771.
#> 7 btcusd btc usd 2018-04-06 00:06:00 6775. 6788. 6772.
#> 8 btcusd btc usd 2018-04-06 00:07:00 6790. 6794. 6787.
#> 9 btcusd btc usd 2018-04-06 00:08:00 6793. 6797. 6790.
#> 10 btcusd btc usd 2018-04-06 00:09:00 6790. 6795. 6782.
#> # ... with 4,991 more rows, and 4 more variables: close <dbl>,
#> # volume <dbl>, volumeNotional <dbl>, tradesDone <dbl>Also available are meta data with riingo_crypto_meta(), and TOP (top
of book) quote data with riingo_crypto_quote().
Lastly, you can extract raw (unaggregated) data feeds from multiple
exchanges by using the raw = TRUE argument in the price and quote
crypto function.