This is a modification of the original crypto package by
jesse vent. It is
entirely set up to use means from the tidyverse and
provides tibbles with all data available via the web-api of
coinmarketcap.com. It
does not require an API key but in turn only provides information that
is also available through the website of coinmarketcap.com.
It allows the user to retrieve
crypto_listings() a list of all coins that were
historically listed on CMC (main dataset to avoid delisting bias)
according to the CMC
API documentationcrypto_list() a list of all coins that are listed as
either being active, delisted or untracked
according to the CMC
API documentationcrypto_info() a list of all information available for
all available coins according to the CMC
API documentationcrypto_history() the most powerful
function of this package that allows to download the entire available
history for all coins covered by CMC according to the CMC
API documentationcrypto_global_quotes() a dataset of historical global
crypto currency market metrics to the CMC
API documentationfiat_list() a mapping of all fiat currencies (plus
precious metals) available via the CMC
WEB APIexchange_list() a list of all exchanges available as
either being active, delisted or untracked
according to the CMC
API documentationexchange_info() a list of all information available for
all given exchanges according to the CMC
API documentationSlight change in api output broke crypto_info() (new
additional column). Fixed.
Slight change in api output broke crypto_info().
Fixed.
After a major change in the api structure of coinmarketcap.com, the package had to be rewritten. As a result, many functions had to be rewritten, because data was not available any more in a similar format or with similar accuracy. Unfortunately, this will potentially break many users implementations. Here is a detailed list of changes:
crypto_list() has been modified and delivers the same
data as before.exchange_list() has been modified and delivers the same
data as before.fiat_list() has been modified and no longer delivers
all available currencies and precious metals (therefore only USD and
Bitcoin are available any more).crypto_listings() needed to be modified, as multiple
base currencies are not available any more. Also some of the fields
downloaded from CMC might have changed. It still retrieves the latest
listings, the new listings as well as historical listings. The fields
returned have somewhat slightly changed. Also, no sorting is available
any more, so if you want to download the top x CCs by market cap, you
have to download all CCs and then sort them in R.crypto_info() has been modified, as the data structure
has changed. The fields returned have somewhat slightly changed.crypto_history() has been modified. It still retrieves
all the OHLC history of all the coins, but is slower due to an increased
number of necessary api calls. The number of available intervals is
strongly limited, but hourly and daily data is still available.
Currently only USD and BTC are available as quote currencies through
this library.crypto_global_quotes() has been modified. It still
produces a clear picture of the global market, but the data structure
has somewhat slightly changed.Since version 1.4.6 I have added the possibility to “sort” the
historical crypto_listings() in _asc_ending or _desc_ending
order (“sort_dir”) to allow for the possibility to download only the top
x crypto currencies using “limit” based on the requested sort (not
available for “new” sorting). Also corrected some problems when sourcing
lists that now do not have the “last_historical_data” field available
any more.
Since version 1.4.5 I have added a new function
crypto_global_quotes() which retrieves global aggregate
market statistics for CMC. There also were some bugs fixed.
Since version 1.4.4 a new function crypto_listings() was
introduced that retrieves new/latest/historical listings and listing
information at CMC. Additionally some aspects of the other functions
have been reworked. We noticed that finalWait = TRUE does
not seem to be necessary at the moment, as well as sleep
can be set to ‘0’ seconds. If you experience strange behavior this might
be due to the the api sending back strange (old) results. In this case
let sleep = 60 (the default) and
finalWait = TRUE (the default).
Since version 1.4.0 the package has been reworked to retrieve as many
assets as possible with one api call, as there is a new “feature”
introduced by CMC to send back the initially requested data for each api
call within 60 seconds. So one needs to wait 60s before calling the api
again. Additionally, since version v1.4.3 the package allows for a data
interval larger than daily (e.g. ‘2d’ or ‘7d’ or
‘weekly’)
You can install crypto2 from CRAN with
install.packages("crypto2")or directly from github with:
# install.packages("devtools")
devtools::install_github("sstoeckl/crypto2")The package provides API free and efficient access to all information
from https://coinmarketcap.com that is also available through
their website. It uses a variety of modification and web-scraping tools
from the tidyverse (especially purrr).
As this provides access not only to active coins but also to those that have now been delisted and also those that are categorized as untracked, including historical pricing information, this package provides a valid basis for any Asset Pricing Studies based on crypto currencies that require survivorship-bias-free information. In addition to that, the package maintainer is currently working on also providing delisting returns (similarly to CRSP for stocks) to also eliminate the delisting bias.
First we load the crypto2-package and download the set
of active coins from https://coinmarketcap.com (additionally one could load
delisted coins with only_Active=FALSE as well as untracked
coins with add_untracked=TRUE).
library(crypto2)
library(dplyr)
#>
#> Attache Paket: 'dplyr'
#> Die folgenden Objekte sind maskiert von 'package:stats':
#>
#> filter, lag
#> Die folgenden Objekte sind maskiert von 'package:base':
#>
#> intersect, setdiff, setequal, union
# List all active coins
coins <- crypto_list(only_active=TRUE)Next we download information on the first three coins from that list.
# retrieve information for all (the first 3) of those coins
coin_info <- crypto_info(coins, limit=3, finalWait=FALSE)
#> ❯ Scraping crypto info
#>
#> ❯ Processing crypto info
#>
# and give the first two lines of information per coin
coin_info
#> # A tibble: 3 × 34
#> id name symbol slug category description date_added status notice
#> <int> <chr> <chr> <chr> <chr> <chr> <date> <chr> <chr>
#> 1 1 Bitcoin BTC bitcoin coin "## What Is … 2010-07-13 active ""
#> 2 2 Litecoin LTC litecoin coin "## What Is … 2013-04-28 active ""
#> 3 3 Namecoin NMC namecoin coin "Namecoin (N… 2013-04-28 active ""
#> # ℹ 25 more variables: alert_type <int>, alert_link <chr>,
#> # latest_update_time <dttm>, watch_list_ranking <int>, date_launched <date>,
#> # is_audited <lgl>, display_tv <int>, is_infinite_max_supply <int>,
#> # tv_coin_symbol <chr>, use_faq <lgl>, holders_flag <lgl>,
#> # ratings_flag <lgl>, analysis_flag <lgl>, socials_flag <lgl>,
#> # has_extra_info_flag <lgl>, upcoming <named list>, annotation_flag <lgl>,
#> # tags <list>, crypto_rating <list>, urls <list>, faq_description <list>, …In a next step we show the logos of the three coins as provided by https://coinmarketcap.com.
In addition we show tags provided by https://coinmarketcap.com.
coin_info %>% select(slug,tags) %>% tidyr::unnest(tags) %>% group_by(slug) %>% slice(1,n())
#> # A tibble: 6 × 2
#> # Groups: slug [3]
#> slug tags$slug $name $category
#> <chr> <chr> <chr> <chr>
#> 1 bitcoin mineable "Mineable" OTHERS
#> 2 bitcoin ftx-bankruptcy-estate "FTX Bankruptcy Estate " CATEGORY
#> 3 litecoin mineable "Mineable" OTHERS
#> 4 litecoin medium-of-exchange "Medium of Exchange" INDUSTRY
#> 5 namecoin mineable "Mineable" OTHERS
#> 6 namecoin platform "Platform" CATEGORYAdditionally: Here are some urls pertaining to these coins as provided by https://coinmarketcap.com.
coin_info %>% pull(urls) %>% .[[1]] |> unlist()
#> urls.website urls.technical_doc
#> "https://bitcoin.org/" "https://bitcoin.org/bitcoin.pdf"
#> urls.explorer1 urls.explorer2
#> "https://blockchain.info/" "https://live.blockcypher.com/btc/"
#> urls.explorer3 urls.explorer4
#> "https://blockchair.com/bitcoin" "https://explorer.viabtc.com/btc"
#> urls.explorer5 urls.source_code
#> "https://www.okx.com/web3/explorer/btc" "https://github.com/bitcoin/bitcoin"
#> urls.message_board urls.reddit
#> "https://bitcointalk.org" "https://reddit.com/r/bitcoin"In a next step we download time series data for these coins.
# retrieve historical data for all (the first 3) of them
coin_hist <- crypto_history(coins, limit=3, start_date="20210101", end_date="20210105", finalWait=FALSE)
#> ❯ Scraping historical crypto data
#>
#> ❯ Processing historical crypto data
#>
# and give the first two times of information per coin
coin_hist %>% group_by(slug) %>% slice(1:2)
#> # A tibble: 6 × 17
#> # Groups: slug [3]
#> id slug name symbol timestamp ref_cur_id ref_cur_name
#> <int> <chr> <chr> <chr> <dttm> <chr> <chr>
#> 1 1 bitcoin Bitcoin BTC 2021-01-01 23:59:59 2781 USD
#> 2 1 bitcoin Bitcoin BTC 2021-01-02 23:59:59 2781 USD
#> 3 2 litecoin Litecoin LTC 2021-01-01 23:59:59 2781 USD
#> 4 2 litecoin Litecoin LTC 2021-01-02 23:59:59 2781 USD
#> 5 3 namecoin Namecoin NMC 2021-01-01 23:59:59 2781 USD
#> 6 3 namecoin Namecoin NMC 2021-01-02 23:59:59 2781 USD
#> # ℹ 10 more variables: time_open <dttm>, time_close <dttm>, time_high <dttm>,
#> # time_low <dttm>, open <dbl>, high <dbl>, low <dbl>, close <dbl>,
#> # volume <dbl>, market_cap <dbl>Similarly, we could download data on an hourly basis.
# retrieve historical data for all (the first 3) of them
coin_hist_m <- crypto_history(coins, limit=3, start_date="20210101", end_date="20210102", interval ="1h", finalWait=FALSE)
#> ❯ Scraping historical crypto data
#>
#> ❯ Processing historical crypto data
#>
# and give the first two times of information per coin
coin_hist_m %>% group_by(slug) %>% slice(1:2)
#> # A tibble: 6 × 17
#> # Groups: slug [3]
#> id slug name symbol timestamp ref_cur_id ref_cur_name
#> <int> <chr> <chr> <chr> <dttm> <chr> <chr>
#> 1 1 bitcoin Bitcoin BTC 2021-01-01 00:59:59 2781 USD
#> 2 1 bitcoin Bitcoin BTC 2021-01-01 01:59:59 2781 USD
#> 3 2 litecoin Litecoin LTC 2021-01-01 00:59:59 2781 USD
#> 4 2 litecoin Litecoin LTC 2021-01-01 01:59:59 2781 USD
#> 5 3 namecoin Namecoin NMC 2021-01-01 00:59:59 2781 USD
#> 6 3 namecoin Namecoin NMC 2021-01-01 01:59:59 2781 USD
#> # ℹ 10 more variables: time_open <dttm>, time_close <dttm>, time_high <dttm>,
#> # time_low <dttm>, open <dbl>, high <dbl>, low <dbl>, close <dbl>,
#> # volume <dbl>, market_cap <dbl>Alternatively, we could determine the price of these coins in other
currencies. A list of such currencies is available as
fiat_list()
fiats <- fiat_list()
fiats
#> # A tibble: 1 × 4
#> id name sign symbol
#> <int> <chr> <chr> <chr>
#> 1 2781 United States Dollar $ USDSo we download the time series again depicting prices in terms of
Bitcoin and Euro (note that multiple currencies can be given to
convert, separated by “,”).
# retrieve historical data for all (the first 3) of them
coin_hist2 <- crypto_history(coins, convert="USD", limit=3, start_date="20210101", end_date="20210105", finalWait=FALSE)
#> ❯ Scraping historical crypto data
#>
#> ❯ Processing historical crypto data
#>
# and give the first two times of information per coin
coin_hist2 %>% group_by(slug,ref_cur_name) %>% slice(1:2)
#> # A tibble: 6 × 17
#> # Groups: slug, ref_cur_name [3]
#> id slug name symbol timestamp ref_cur_id ref_cur_name
#> <int> <chr> <chr> <chr> <dttm> <chr> <chr>
#> 1 1 bitcoin Bitcoin BTC 2021-01-01 23:59:59 2781 USD
#> 2 1 bitcoin Bitcoin BTC 2021-01-02 23:59:59 2781 USD
#> 3 2 litecoin Litecoin LTC 2021-01-01 23:59:59 2781 USD
#> 4 2 litecoin Litecoin LTC 2021-01-02 23:59:59 2781 USD
#> 5 3 namecoin Namecoin NMC 2021-01-01 23:59:59 2781 USD
#> 6 3 namecoin Namecoin NMC 2021-01-02 23:59:59 2781 USD
#> # ℹ 10 more variables: time_open <dttm>, time_close <dttm>, time_high <dttm>,
#> # time_low <dttm>, open <dbl>, high <dbl>, low <dbl>, close <dbl>,
#> # volume <dbl>, market_cap <dbl>As a new features in version 1.4.4. we introduced the possibility to
download historical listings and listing information (add
quote = TRUE).
latest_listings <- crypto_listings(which="latest", limit=10, quote=TRUE, finalWait=FALSE)
latest_listings
#> # A tibble: 5,000 × 30
#> id name symbol slug cmc_rank market_pair_count circulating_supply
#> <int> <chr> <chr> <chr> <int> <int> <dbl>
#> 1 1 Bitcoin BTC bitc… 1 11665 19748503
#> 2 2 Litecoin LTC lite… 19 1226 74929219.
#> 3 3 Namecoin NMC name… 1101 7 14736400
#> 4 5 Peercoin PPC peer… 960 41 29110837.
#> 5 8 Feathercoin FTC feat… 1696 12 236600238
#> 6 16 WorldCoin W… WDC worl… 3516 5 0
#> 7 18 Digitalcoin DGC digi… 4616 2 0
#> 8 25 Goldcoin GLC gold… 1949 12 43681422.
#> 9 35 Phoenixcoin PXC phoe… 1815 4 91379993.
#> 10 42 Primecoin XPM prim… 1630 3 50753528.
#> # ℹ 4,990 more rows
#> # ℹ 23 more variables: self_reported_circulating_supply <dbl>,
#> # total_supply <dbl>, max_supply <dbl>, is_active <int>, last_updated <date>,
#> # date_added <chr>, ref_currency <chr>, price <dbl>, volume24h <dbl>,
#> # market_cap <dbl>, percent_change1h <dbl>, percent_change24h <dbl>,
#> # percent_change7d <dbl>, percent_change30d <dbl>, percent_change60d <dbl>,
#> # percent_change90d <dbl>, fully_dillutted_market_cap <dbl>, …An additional feature that was added in version 1.4.5 retrieves global aggregate market statistics for CMC.
all_quotes <- crypto_global_quotes(which="historical", quote=TRUE)
#> ❯ Scraping historical global data
#>
#> ❯ Processing historical crypto data
#>
all_quotes
#> # A tibble: 4,143 × 17
#> timestamp btc_dominance eth_dominance score USD_total_market_cap
#> <date> <dbl> <dbl> <dbl> <dbl>
#> 1 2013-04-29 94.2 0 1367193600000 1583440000
#> 2 2013-04-30 94.4 0 1367280000000 1686950016
#> 3 2013-05-01 94.4 0 1367366400000 1637389952
#> 4 2013-05-02 94.1 0 1367452800000 1333880064
#> 5 2013-05-03 94.2 0 1367539200000 1275410048
#> 6 2013-05-04 93.9 0 1367625600000 1169469952
#> 7 2013-05-05 94.0 0 1367712000000 1335379968
#> 8 2013-05-06 94.1 0 1367798400000 1370880000
#> 9 2013-05-07 94.4 0 1367884800000 1313900032
#> 10 2013-05-08 94.4 0 1367971200000 1320509952
#> # ℹ 4,133 more rows
#> # ℹ 12 more variables: USD_total_volume24h <dbl>,
#> # USD_total_volume24h_reported <dbl>, USD_altcoin_volume24h <dbl>,
#> # USD_altcoin_volume24h_reported <dbl>, USD_altcoin_market_cap <dbl>,
#> # USD_original_score <chr>, active_cryptocurrencies <int>,
#> # active_market_pairs <int>, active_exchanges <int>,
#> # total_cryptocurrencies <int>, total_exchanges <int>, origin_id <chr>We can use those quotes to plot information on the aggregate market capitalization:
all_quotes %>% select(timestamp, USD_total_market_cap, USD_altcoin_market_cap) %>%
tidyr::pivot_longer(cols = 2:3, names_to = "Market Cap", values_to = "bn. USD") %>%
tidyr::separate(`Market Cap`,into = c("Currency","Type","Market","Cap")) %>%
dplyr::mutate(`bn. USD`=`bn. USD`/1000000000) %>%
ggplot2::ggplot(ggplot2::aes(x=timestamp,y=`bn. USD`,color=Type)) + ggplot2::geom_line() +
ggplot2::labs(title="Market capitalization in bn USD", subtitle="CoinMarketCap.com")
Last and least, one can get information on exchanges. For this
download a list of active/inactive/untracked exchanges using
exchange_list():
exchanges <- exchange_list(only_active=TRUE)
exchanges
#> # A tibble: 790 × 6
#> id name slug is_active first_historical_data last_historical_data
#> <int> <chr> <chr> <int> <date> <date>
#> 1 16 Poloniex polo… 1 2018-04-26 2024-09-02
#> 2 21 BTCC btcc 1 2018-04-26 2024-09-02
#> 3 24 Kraken krak… 1 2018-04-26 2024-09-02
#> 4 34 Bittylicious bitt… 1 2018-04-26 2024-09-02
#> 5 36 CEX.IO cex-… 1 2018-04-26 2024-09-02
#> 6 37 Bitfinex bitf… 1 2018-04-26 2024-09-02
#> 7 42 HitBTC hitb… 1 2018-04-26 2024-09-02
#> 8 50 EXMO exmo 1 2018-04-26 2024-09-02
#> 9 61 Okcoin okco… 1 2018-04-26 2024-09-02
#> 10 68 Indodax indo… 1 2018-04-26 2024-09-02
#> # ℹ 780 more rowsand then download information on “binance” and “bittrex”:
ex_info <- exchange_info(exchanges %>% filter(slug %in% c('binance','kraken')), finalWait=FALSE)
#> ❯ Scraping crypto info
#>
#> ❯ Processing exchange info
#>
ex_info
#> # A tibble: 2 × 19
#> id name slug logo description date_launched notice is_hidden status
#> <int> <chr> <chr> <chr> <chr> <date> <chr> <int> <chr>
#> 1 24 Kraken kraken https… "## What I… 2011-07-28 "" 0 active
#> 2 270 Binance binance https… "## What I… 2017-07-14 "" 0 active
#> # ℹ 10 more variables: type <chr>, maker_fee <dbl>, taker_fee <dbl>,
#> # platform_id <int>, dex_status <int>, wallet_source_status <int>,
#> # tags <lgl>, countries <lgl>, fiats <list>, urls <list>Then we can access information on the fee structure,
ex_info %>% select(contains("fee"))
#> # A tibble: 2 × 2
#> maker_fee taker_fee
#> <dbl> <dbl>
#> 1 0.02 0.05
#> 2 0.02 0.04or the fiat currencies allowed:
ex_info %>% select(slug,fiats) %>% tidyr::unnest(fiats)
#> # A tibble: 18 × 2
#> slug fiats
#> <chr> <chr>
#> 1 kraken "USD"
#> 2 kraken "EUR"
#> 3 kraken "GBP"
#> 4 kraken "CAD"
#> 5 kraken "JPY"
#> 6 kraken "CHF"
#> 7 kraken "AUD"
#> 8 binance "EUR"
#> 9 binance " GBP"
#> 10 binance " BRL"
#> 11 binance " AUD"
#> 12 binance " UAH"
#> 13 binance " RUB"
#> 14 binance " TRY"
#> 15 binance " ZAR"
#> 16 binance " PLN"
#> 17 binance " NGN"
#> 18 binance " RON"This project is licensed under the MIT License - see the <license.md> file for details</license.md>
crypto-package
that inspired this package.