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_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 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 documentationYou can install crypto2 from CRAN with
or directly from github with:
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, 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)
#> > Scraping crypto info
#>
#> > Processing historical crypto data
#>
# and give the first two lines of information per coin
coin_info
#> # A tibble: 3 x 19
#> id name symbol category description slug logo subreddit notice
#> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 Bitco~ BTC coin "## **What Is B~ bitc~ https://~ bitcoin ""
#> 2 2 Litec~ LTC coin "## What Is Lit~ lite~ https://~ litecoin ""
#> 3 3 Namec~ NMC coin "Namecoin (NMC)~ name~ https://~ namecoin ""
#> # ... with 10 more variables: date_added <chr>, twitter_username <chr>,
#> # is_hidden <int>, date_launched <lgl>,
#> # self_reported_circulating_supply <lgl>, self_reported_tags <lgl>,
#> # status <dttm>, tags <list>, urls <list>, platform <lgl>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 x 2
#> # Groups: slug [3]
#> slug tags
#> <chr> <chr>
#> 1 bitcoin mineable
#> 2 bitcoin paradigm-xzy-screener
#> 3 litecoin mineable
#> 4 litecoin binance-chain
#> 5 namecoin mineable
#> 6 namecoin platformAdditionally: Here are some urls pertaining to these coins as provided by https://coinmarketcap.com.
coin_info %>% select(slug,urls) %>% tidyr::unnest(urls) %>% filter(name %in% c("reddit","twitter"))
#> # A tibble: 5 x 3
#> slug name url
#> <chr> <chr> <chr>
#> 1 bitcoin reddit https://reddit.com/r/bitcoin
#> 2 litecoin twitter https://twitter.com/LitecoinProject
#> 3 litecoin reddit https://reddit.com/r/litecoin
#> 4 namecoin twitter https://twitter.com/Namecoin
#> 5 namecoin reddit https://reddit.com/r/namecoinIn 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")
#> > 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 x 16
#> # Groups: slug [3]
#> timestamp slug id name symbol ref_cur open high low
#> <dttm> <chr> <int> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2021-01-01 23:59:59 bitco~ 1 Bitco~ BTC USD 2.90e+4 2.96e+4 2.88e+4
#> 2 2021-01-02 23:59:59 bitco~ 1 Bitco~ BTC USD 2.94e+4 3.32e+4 2.91e+4
#> 3 2021-01-01 23:59:59 litec~ 2 Litec~ LTC USD 1.25e+2 1.33e+2 1.23e+2
#> 4 2021-01-02 23:59:59 litec~ 2 Litec~ LTC USD 1.26e+2 1.40e+2 1.24e+2
#> 5 2021-01-01 23:59:59 namec~ 3 Namec~ NMC USD 4.39e-1 4.63e-1 4.32e-1
#> 6 2021-01-02 23:59:59 namec~ 3 Namec~ NMC USD 4.51e-1 5.10e-1 4.15e-1
#> # ... with 7 more variables: close <dbl>, volume <dbl>, market_cap <dbl>,
#> # time_open <dttm>, time_close <dttm>, time_high <dttm>, time_low <dttm>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: 93 x 4
#> id name sign symbol
#> <int> <chr> <chr> <chr>
#> 1 2781 United States Dollar $ USD
#> 2 2782 Australian Dollar $ AUD
#> 3 2783 Brazilian Real R$ BRL
#> 4 2784 Canadian Dollar $ CAD
#> 5 2785 Swiss Franc Fr CHF
#> 6 2786 Chilean Peso $ CLP
#> 7 2787 Chinese Yuan ¥ CNY
#> 8 2788 Czech Koruna Kc CZK
#> 9 2789 Danish Krone kr DKK
#> 10 2790 Euro € EUR
#> # ... with 83 more rowsSo 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="BTC,EUR", limit=3, start_date="20210101", end_date="20210105")
#> > 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) %>% slice(1:2)
#> # A tibble: 12 x 16
#> # Groups: slug, ref_cur [6]
#> timestamp slug id name symbol ref_cur open high low
#> <dttm> <chr> <int> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2021-01-01 23:59:43 bitco~ 1 Bitc~ BTC BTC 1 e+0 1.00e+0 9.98e-1
#> 2 2021-01-02 23:59:43 bitco~ 1 Bitc~ BTC BTC 1 e+0 1.00e+0 9.99e-1
#> 3 2021-01-01 23:59:06 bitco~ 1 Bitc~ BTC EUR 2.37e+4 2.43e+4 2.36e+4
#> 4 2021-01-02 23:59:06 bitco~ 1 Bitc~ BTC EUR 2.42e+4 2.73e+4 2.40e+4
#> 5 2021-01-01 23:59:43 litec~ 2 Lite~ LTC BTC 4.30e-3 4.56e-3 4.27e-3
#> 6 2021-01-02 23:59:43 litec~ 2 Lite~ LTC BTC 4.30e-3 4.24e-3 4.23e-3
#> 7 2021-01-01 23:59:06 litec~ 2 Lite~ LTC EUR 1.02e+2 1.09e+2 1.01e+2
#> 8 2021-01-02 23:59:06 litec~ 2 Lite~ LTC EUR 1.04e+2 1.16e+2 1.02e+2
#> 9 2021-01-01 23:59:43 namec~ 3 Name~ NMC BTC 1.51e-5 1.58e-5 1.50e-5
#> 10 2021-01-02 23:59:43 namec~ 3 Name~ NMC BTC 1.54e-5 1.57e-5 1.31e-5
#> 11 2021-01-01 23:59:06 namec~ 3 Name~ NMC EUR 3.60e-1 3.80e-1 3.54e-1
#> 12 2021-01-02 23:59:06 namec~ 3 Name~ NMC EUR 3.71e-1 4.21e-1 3.41e-1
#> # ... with 7 more variables: close <dbl>, volume <dbl>, market_cap <dbl>,
#> # time_open <dttm>, time_close <dttm>, time_high <dttm>, time_low <dttm>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: 379 x 6
#> id name slug is_active first_historical_da~ last_historical_d~
#> <int> <chr> <chr> <int> <date> <date>
#> 1 16 Poloniex poloniex 1 2018-04-26 2021-06-23
#> 2 22 Bittrex bittrex 1 2018-04-26 2021-06-23
#> 3 24 Kraken kraken 1 2018-04-26 2021-06-23
#> 4 32 Bleutrade bleutrade 1 2018-04-26 2021-06-23
#> 5 34 Bittylici~ bittylici~ 1 2018-04-26 2021-06-23
#> 6 36 CEX.IO cex-io 1 2018-04-26 2021-06-23
#> 7 37 Bitfinex bitfinex 1 2018-04-26 2021-06-23
#> 8 42 HitBTC hitbtc 1 2018-04-26 2021-06-23
#> 9 50 EXMO exmo 1 2018-04-26 2021-06-23
#> 10 61 Okcoin okcoin 1 2018-04-26 2021-06-23
#> # ... with 369 more rowsand then download information on “binance” and “kraken”:
ex_info <- exchange_info(exchanges %>% filter(slug %in% c('binance','kraken')))
#> > Scraping crypto info
#>
#> > Processing historical crypto data
#>
ex_info
#> # A tibble: 2 x 19
#> id name slug description notice logo type date_launched is_hidden
#> <int> <chr> <chr> <lgl> <chr> <chr> <chr> <chr> <int>
#> 1 24 Kraken kraken NA "" https:~ "" 2011-07-28T0~ 0
#> 2 270 Binan~ binan~ NA "Binanc~ https:~ "" 2017-07-14T0~ 0
#> # ... with 10 more variables: is_redistributable <lgl>, maker_fee <dbl>,
#> # taker_fee <dbl>, spot_volume_usd <dbl>, spot_volume_last_updated <dttm>,
#> # status <dttm>, tags <lgl>, urls <list>, countries <lgl>, fiats <list>Then we can access information on the fee structure,
ex_info %>% select(contains("fee"))
#> # A tibble: 2 x 2
#> maker_fee taker_fee
#> <dbl> <dbl>
#> 1 -0.02 0.075
#> 2 0.02 0.04the amount of cryptocurrencies being traded (in USD)
ex_info %>% select(contains("spot"))
#> # A tibble: 2 x 2
#> spot_volume_usd spot_volume_last_updated
#> <dbl> <dttm>
#> 1 2584452727. 2021-06-23 11:35:15
#> 2 39019591303. 2021-06-23 11:35:15or the fiat currencies allowed:
ex_info %>% select(slug,fiats) %>% tidyr::unnest(fiats)
#> # A tibble: 53 x 2
#> slug value
#> <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 AED
#> 9 binance ARS
#> 10 binance AUD
#> # ... with 43 more rowsThis project is licensed under the MIT License - see the <license.md> file for details</license.md>
crypto-package that inspired this package.