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.
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")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.
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 adjHigh
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 AAPL 2019-07-18 00:00:00 206. 206. 204. 204 1.86e7 203. 203.
#> 2 AAPL 2019-07-19 00:00:00 203. 206. 202. 206. 2.09e7 200. 204.
#> 3 AAPL 2019-07-22 00:00:00 207. 207. 204. 204. 2.23e7 205. 205.
#> 4 AAPL 2019-07-23 00:00:00 209. 209. 207. 208. 1.84e7 206. 206.
#> 5 AAPL 2019-07-24 00:00:00 209. 209. 207. 208. 1.50e7 206. 207.
#> 6 AAPL 2019-07-25 00:00:00 207. 209. 207. 209. 1.39e7 205. 207.
#> 7 AAPL 2019-07-26 00:00:00 208. 210. 207. 207. 1.76e7 205. 207.
#> 8 AAPL 2019-07-29 00:00:00 210. 211. 208. 208. 2.17e7 207. 208.
#> 9 AAPL 2019-07-30 00:00:00 209. 210. 207. 209. 3.39e7 206. 208.
#> 10 AAPL 2019-07-31 00:00:00 213. 221. 211. 216. 6.93e7 211. 219.
#> # … with 242 more rows, and 5 more variables: 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: 9,982 x 14
#> ticker date close high low open volume adjClose adjHigh
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 AAPL 1980-12-12 00:00:00 28.8 28.9 28.8 28.8 2.09e6 0.407 0.408
#> 2 AAPL 1980-12-15 00:00:00 27.2 27.4 27.2 27.4 7.85e5 0.386 0.387
#> 3 AAPL 1980-12-16 00:00:00 25.2 25.4 25.2 25.4 4.72e5 0.357 0.359
#> 4 AAPL 1980-12-17 00:00:00 25.9 26 25.9 25.9 3.86e5 0.366 0.368
#> 5 AAPL 1980-12-18 00:00:00 26.6 26.8 26.6 26.6 3.28e5 0.377 0.378
#> 6 AAPL 1980-12-19 00:00:00 28.2 28.4 28.2 28.2 2.17e5 0.400 0.402
#> 7 AAPL 1980-12-22 00:00:00 29.6 29.8 29.6 29.6 1.67e5 0.419 0.421
#> 8 AAPL 1980-12-23 00:00:00 30.9 31 30.9 30.9 2.10e5 0.437 0.439
#> 9 AAPL 1980-12-24 00:00:00 32.5 32.6 32.5 32.5 2.14e5 0.460 0.462
#> 10 AAPL 1980-12-26 00:00:00 35.5 35.6 35.5 35.5 2.48e5 0.502 0.504
#> # … with 9,972 more rows, and 5 more variables: 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 adjHigh
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 AAPL 2001-01-31 00:00:00 21.6 22.5 14.4 14.9 2.45e8 1.34 1.39
#> 2 AAPL 2001-02-28 00:00:00 18.2 21.9 18 20.7 1.25e8 1.13 1.36
#> 3 AAPL 2001-03-30 00:00:00 22.1 23.8 17.2 17.8 1.93e8 1.36 1.47
#> 4 AAPL 2001-04-30 00:00:00 25.5 27.1 18.8 22.1 1.99e8 1.58 1.68
#> 5 AAPL 2001-05-31 00:00:00 20.0 26.7 19.3 25.4 1.33e8 1.23 1.65
#> 6 AAPL 2001-06-29 00:00:00 23.2 25.1 19.4 20.1 1.36e8 1.44 1.55
#> 7 AAPL 2001-07-31 00:00:00 18.8 25.2 17.8 23.6 1.55e8 1.16 1.56
#> 8 AAPL 2001-08-31 00:00:00 18.6 19.9 17.3 19.0 9.16e7 1.15 1.23
#> 9 AAPL 2001-09-28 00:00:00 15.5 19.1 14.7 18.5 9.88e7 0.959 1.18
#> 10 AAPL 2001-10-31 00:00:00 17.6 19.4 14.8 15.5 1.35e8 1.09 1.20
#> # … with 88 more rows, and 5 more variables: adjLow <dbl>, adjOpen <dbl>,
#> # adjVolume <dbl>, divCash <dbl>, splitFactor <dbl>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: 10,000 x 6
#> ticker date close high low open
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 AAPL 2020-06-12 13:55:00 346. 346. 346. 346.
#> 2 AAPL 2020-06-12 13:56:00 347. 347. 346. 346.
#> 3 AAPL 2020-06-12 13:57:00 346. 347. 346. 347.
#> 4 AAPL 2020-06-12 13:58:00 346. 346. 346. 346.
#> 5 AAPL 2020-06-12 13:59:00 346. 347. 346. 346.
#> 6 AAPL 2020-06-12 14:00:00 346. 347. 346. 347.
#> 7 AAPL 2020-06-12 14:01:00 347. 347. 346. 346.
#> 8 AAPL 2020-06-12 14:02:00 347. 347. 347. 347.
#> 9 AAPL 2020-06-12 14:03:00 347. 347. 347. 347.
#> 10 AAPL 2020-06-12 14:04:00 347. 347. 347. 347.
#> # … with 9,990 more rowsSee the documentation for all of the restrictions.
Meta data about each ticker is available through
riingo_meta().
riingo_meta(c("AAPL", "QQQ"))
#> # A tibble: 2 x 6
#> ticker name startDate exchangeCode description endDate
#> <chr> <chr> <dttm> <chr> <chr> <dttm>
#> 1 AAPL Apple… 1980-12-12 00:00:00 NASDAQ "Apple Inc… 2020-07-16 00:00:00
#> 2 QQQ POWER… 1999-03-10 00:00:00 NASDAQ "PowerShar… 2020-07-16 00:00:00You 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: 85,714 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 000003 SHE Stock CNY NA
#> 4 000004 SHE Stock CNY 2007-08-31 00:00:00
#> 5 000005 SHE Stock CNY 2001-01-02 00:00:00
#> 6 000006 SHE Stock CNY 2018-01-01 00:00:00
#> 7 000007 SHE Stock CNY 2007-08-31 00:00:00
#> 8 000008 SHE Stock CNY 2000-01-03 00:00:00
#> 9 000009 SHE Stock CNY 2000-01-03 00:00:00
#> 10 000010 SHE Stock CNY 2007-08-31 00:00:00
#> # … with 85,704 more rows, and 1 more variable: endDate <dttm>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 bidPrice quoteTimestamp low volume timestamp
#> <chr> <dbl> <dbl> <dttm> <dbl> <int> <dttm>
#> 1 AAPL 385. 377 2020-07-17 13:39:00 383. 201592 2020-07-17 13:39:00
#> 2 QQQ 259. 259. 2020-07-17 13:39:16 257. 131168 2020-07-17 13:39:16
#> # … with 10 more variables: tngoLast <dbl>, lastsaleTimeStamp <dttm>,
#> # lastSize <int>, askSize <int>, bidSize <int>, askPrice <dbl>, high <dbl>,
#> # open <dbl>, prevClose <dbl>, mid <dbl>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: 731 x 11
#> ticker baseCurrency quoteCurrency date open high low close
#> <chr> <chr> <chr> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 btceur btc eur 2019-07-18 00:00:00 8638. 9568. 8282. 9444.
#> 2 btceur btc eur 2019-07-19 00:00:00 9453. 9558. 9012. 9383.
#> 3 btceur btc eur 2019-07-20 00:00:00 9377. 9901. 9246. 9578.
#> 4 btceur btc eur 2019-07-21 00:00:00 9580. 9650. 9188. 9433.
#> 5 btceur btc eur 2019-07-22 00:00:00 9449. 9539. 8986. 9221.
#> 6 btceur btc eur 2019-07-23 00:00:00 9215. 9221. 8812. 8838.
#> 7 btceur btc eur 2019-07-24 00:00:00 8843. 8915. 8550. 8769.
#> 8 btceur btc eur 2019-07-25 00:00:00 8770. 9144. 8738. 8876.
#> 9 btceur btc eur 2019-07-26 00:00:00 8872. 8913. 8674. 8844.
#> 10 btceur btc eur 2019-07-27 00:00:00 8843. 9177. 8373. 8512.
#> # … with 721 more rows, and 3 more variables: 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: 4,530 x 11
#> ticker baseCurrency quoteCurrency date open high low close
#> <chr> <chr> <chr> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 btcusd btc usd 2020-07-12 00:00:00 9234. 9237. 9234. 9236.
#> 2 btcusd btc usd 2020-07-12 00:01:00 9236. 9237. 9236. 9237.
#> 3 btcusd btc usd 2020-07-12 00:02:00 9237. 9237. 9231. 9233.
#> 4 btcusd btc usd 2020-07-12 00:03:00 9233. 9240. 9233. 9238.
#> 5 btcusd btc usd 2020-07-12 00:04:00 9238. 9240. 9238. 9239.
#> 6 btcusd btc usd 2020-07-12 00:05:00 9240. 9240. 9239. 9240.
#> 7 btcusd btc usd 2020-07-12 00:06:00 9240. 9240. 9239. 9240.
#> 8 btcusd btc usd 2020-07-12 00:07:00 9240. 9242. 9238. 9240.
#> 9 btcusd btc usd 2020-07-12 00:08:00 9240. 9242. 9239. 9241.
#> 10 btcusd btc usd 2020-07-12 00:09:00 9241. 9246. 9240. 9245.
#> # … with 4,520 more rows, and 3 more variables: 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.
tiingo-python - A Python client for interacting with the Tiingo API.
quantmod -
One of the data sources quantmod can pull from is
Tiingo.