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 the development version from GitHub with:
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.
Let’s grab some data with riingo. The default parameters attempt to get 1 year’s worth of data.
riingo_prices("AAPL")
#> # A tibble: 253 x 14
#> ticker date close high low open volume adjClose
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 AAPL 2017-07-05 00:00:00 144. 145. 143. 144. 2.08e7 142.
#> 2 AAPL 2017-07-06 00:00:00 143. 144. 142. 143. 2.34e7 141.
#> 3 AAPL 2017-07-07 00:00:00 144. 145. 143. 143. 1.85e7 142.
#> 4 AAPL 2017-07-10 00:00:00 145. 146. 143. 144. 2.10e7 143.
#> 5 AAPL 2017-07-11 00:00:00 146. 146. 144. 145. 1.83e7 143.
#> 6 AAPL 2017-07-12 00:00:00 146. 146. 145. 146. 2.36e7 143.
#> 7 AAPL 2017-07-13 00:00:00 148. 148. 145. 146. 2.49e7 145.
#> 8 AAPL 2017-07-14 00:00:00 149. 149. 147. 148. 2.00e7 147.
#> 9 AAPL 2017-07-17 00:00:00 150. 151. 149. 149. 2.32e7 147.
#> 10 AAPL 2017-07-18 00:00:00 150. 150. 149. 149. 1.77e7 148.
#> # ... with 243 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: 9,471 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 2.09e6 0.418
#> 2 AAPL 1980-12-15 00:00:00 27.2 27.4 27.2 27.4 7.85e5 0.396
#> 3 AAPL 1980-12-16 00:00:00 25.2 25.4 25.2 25.4 4.72e5 0.367
#> 4 AAPL 1980-12-17 00:00:00 25.9 26 25.9 25.9 3.86e5 0.376
#> 5 AAPL 1980-12-18 00:00:00 26.6 26.8 26.6 26.6 3.28e5 0.387
#> 6 AAPL 1980-12-19 00:00:00 28.2 28.4 28.2 28.2 2.17e5 0.411
#> 7 AAPL 1980-12-22 00:00:00 29.6 29.8 29.6 29.6 1.67e5 0.431
#> 8 AAPL 1980-12-23 00:00:00 30.9 31 30.9 30.9 2.10e5 0.449
#> 9 AAPL 1980-12-24 00:00:00 32.5 32.6 32.5 32.5 2.14e5 0.472
#> 10 AAPL 1980-12-26 00:00:00 35.5 35.6 35.5 35.5 2.48e5 0.516
#> # ... with 9,461 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 2.45e8 1.37
#> 2 AAPL 2001-02-28 00:00:00 18.2 21.9 18 20.7 1.25e8 1.16
#> 3 AAPL 2001-03-30 00:00:00 22.1 23.8 17.2 17.8 1.93e8 1.40
#> 4 AAPL 2001-04-30 00:00:00 25.5 27.1 18.8 22.1 1.99e8 1.62
#> 5 AAPL 2001-05-31 00:00:00 20.0 26.7 19.3 25.4 1.33e8 1.27
#> 6 AAPL 2001-06-29 00:00:00 23.2 25.1 19.4 20.1 1.36e8 1.48
#> 7 AAPL 2001-07-31 00:00:00 18.8 25.2 17.8 23.6 1.55e8 1.19
#> 8 AAPL 2001-08-31 00:00:00 18.6 19.9 17.3 19.0 9.16e7 1.18
#> 9 AAPL 2001-09-28 00:00:00 15.5 19.1 14.7 18.5 9.88e7 0.985
#> 10 AAPL 2001-10-31 00:00:00 17.6 19.4 14.8 15.5 1.35e8 1.12
#> # ... with 88 more rows, and 6 more variables: adjHigh <dbl>,
#> # 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: 2,000 x 6
#> ticker date open high low close
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 AAPL 2018-06-29 15:45:00 186. 186. 186. 186.
#> 2 AAPL 2018-06-29 15:46:00 186. 186. 186. 186.
#> 3 AAPL 2018-06-29 15:47:00 186. 186. 186. 186.
#> 4 AAPL 2018-06-29 15:48:00 186. 186. 186. 186.
#> 5 AAPL 2018-06-29 15:49:00 186. 186. 186. 186.
#> 6 AAPL 2018-06-29 15:50:00 186. 186. 186. 186.
#> 7 AAPL 2018-06-29 15:51:00 186. 186. 186. 186.
#> 8 AAPL 2018-06-29 15:52:00 186. 186. 186. 186.
#> 9 AAPL 2018-06-29 15:53:00 186. 186. 186. 186.
#> 10 AAPL 2018-06-29 15:54:00 186. 186. 186. 186.
#> # ... with 1,990 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 description endDate
#> <chr> <chr> <dttm> <chr> <dttm>
#> 1 AAPL Appl… 1980-12-12 00:00:00 Apple Inc.… 2018-07-05 00:00:00
#> 2 QQQ POWE… 1999-03-10 00:00:00 "PowerShar… 2018-07-05 00:00:00
#> # ... with 1 more variable: exchangeCode <chr>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: 67,006 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 2018-01-02 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 66,996 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 lastSize lastsaleTimeStamp bidPrice high
#> <chr> <dbl> <lgl> <dttm> <lgl> <dbl>
#> 1 AAPL 185. NA 2018-07-05 20:00:00 NA 186.
#> 2 QQQ 173. NA 2018-07-05 20:00:00 NA 173.
#> # ... with 11 more variables: quoteTimestamp <dttm>, tngoLast <dbl>,
#> # mid <lgl>, bidSize <lgl>, prevClose <dbl>, timestamp <dttm>,
#> # askSize <lgl>, askPrice <lgl>, open <dbl>, volume <int>, low <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: 570 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 560 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 2018-06-30 00:00:00 6206. 6206. 6202.
#> 2 btcusd btc usd 2018-06-30 00:01:00 6205. 6206. 6198.
#> 3 btcusd btc usd 2018-06-30 00:02:00 6201. 6201. 6196.
#> 4 btcusd btc usd 2018-06-30 00:03:00 6196. 6202. 6192.
#> 5 btcusd btc usd 2018-06-30 00:04:00 6202. 6209. 6189.
#> 6 btcusd btc usd 2018-06-30 00:05:00 6202. 6214. 6201.
#> 7 btcusd btc usd 2018-06-30 00:06:00 6214. 6225. 6213.
#> 8 btcusd btc usd 2018-06-30 00:07:00 6227. 6231. 6222.
#> 9 btcusd btc usd 2018-06-30 00:08:00 6218. 6218. 6212.
#> 10 btcusd btc usd 2018-06-30 00:09:00 6213. 6216. 6208.
#> # ... 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.
tiingo-python - A Python client for interacting with the Tiingo API.
quantmod - One of the data sources quantmod can pull from is Tiingo.