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 up

Once 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_here

Do 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 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>

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: 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 rows

See 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           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 00:00:00

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: 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>

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 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>

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: 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.

Related projects

• tiingo-python - A Python client for interacting with the Tiingo API.

• quantmod - One of the data sources quantmod can pull from is Tiingo.