crypto2

Project Status Build Status CRAN status

Historical Cryptocurrency Prices for Active and Delisted Tokens!

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

Update

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.

Installation

You can install crypto2 from CRAN with

install.packages("crypto2")

or directly from github with:

# install.packages("devtools")
devtools::install_github("sstoeckl/crypto2")

Package Contribution

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.

Package Usage

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
#> 
#> Scraping  https://web-api.coinmarketcap.com/v1/cryptocurrency/info?id=1,2,3  with  65  characters!
#> > Processing crypto info
#> 

# and give the first two lines of information per coin
coin_info
#> # A tibble: 3 x 18
#>      id name     symbol category description     slug   logo    subreddit notice
#> * <int> <chr>    <chr>  <chr>    <chr>           <chr>  <chr>   <chr>     <chr> 
#> 1     1 Bitcoin  BTC    coin     "## What Is Bi~ bitco~ https:~ bitcoin   ""    
#> 2     2 Litecoin LTC    coin     "## What Is Li~ litec~ https:~ litecoin  ""    
#> 3     3 Namecoin NMC    coin     "Namecoin (NMC~ namec~ https:~ namecoin  ""    
#> # ... with 9 more variables: date_added <chr>, twitter_username <chr>,
#> #   is_hidden <int>, date_launched <lgl>,
#> #   self_reported_circulating_supply <lgl>, tags <list>,
#> #   self_reported_tags <lgl>, 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-portfolio 
#> 3 litecoin mineable           
#> 4 litecoin binance-smart-chain
#> 5 namecoin mineable           
#> 6 namecoin platform

Additionally: 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/namecoin

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")
#> > 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              id slug   name   symbol ref_cur    open    high     low
#>   <dttm>              <int> <chr>  <chr>  <chr>  <chr>     <dbl>   <dbl>   <dbl>
#> 1 2021-01-01 23:59:59     1 bitco~ Bitco~ BTC    USD     2.90e+4 2.96e+4 2.88e+4
#> 2 2021-01-02 23:59:59     1 bitco~ Bitco~ BTC    USD     2.94e+4 3.32e+4 2.91e+4
#> 3 2021-01-01 23:59:59     2 litec~ Litec~ LTC    USD     1.25e+2 1.33e+2 1.23e+2
#> 4 2021-01-02 23:59:59     2 litec~ Litec~ LTC    USD     1.26e+2 1.40e+2 1.24e+2
#> 5 2021-01-01 23:59:59     3 namec~ Namec~ NMC    USD     4.39e-1 4.63e-1 4.32e-1
#> 6 2021-01-02 23:59:59     3 namec~ 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 rows

So 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              id slug   name  symbol ref_cur    open    high     low
#>    <dttm>              <int> <chr>  <chr> <chr>  <chr>     <dbl>   <dbl>   <dbl>
#>  1 2021-01-01 23:59:43     1 bitco~ Bitc~ BTC    BTC     1   e+0 1.00e+0 9.98e-1
#>  2 2021-01-02 23:59:43     1 bitco~ Bitc~ BTC    BTC     1   e+0 1.00e+0 9.99e-1
#>  3 2021-01-01 23:59:06     1 bitco~ Bitc~ BTC    EUR     2.37e+4 2.43e+4 2.36e+4
#>  4 2021-01-02 23:59:06     1 bitco~ Bitc~ BTC    EUR     2.42e+4 2.73e+4 2.40e+4
#>  5 2021-01-01 23:59:43     2 litec~ Lite~ LTC    BTC     4.30e-3 4.56e-3 4.27e-3
#>  6 2021-01-02 23:59:43     2 litec~ Lite~ LTC    BTC     4.30e-3 4.24e-3 4.23e-3
#>  7 2021-01-01 23:59:06     2 litec~ Lite~ LTC    EUR     1.02e+2 1.09e+2 1.01e+2
#>  8 2021-01-02 23:59:06     2 litec~ Lite~ LTC    EUR     1.04e+2 1.16e+2 1.02e+2
#>  9 2021-01-01 23:59:43     3 namec~ Name~ NMC    BTC     1.51e-5 1.58e-5 1.50e-5
#> 10 2021-01-02 23:59:43     3 namec~ Name~ NMC    BTC     1.54e-5 1.57e-5 1.31e-5
#> 11 2021-01-01 23:59:06     3 namec~ Name~ NMC    EUR     3.60e-1 3.80e-1 3.54e-1
#> 12 2021-01-02 23:59:06     3 namec~ 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: 454 x 6
#>       id name         slug         is_active first_historical_~ last_historical~
#>    <int> <chr>        <chr>            <int> <date>             <date>          
#>  1    16 Poloniex     poloniex             1 2018-04-26         2022-01-10      
#>  2    22 Bittrex      bittrex              1 2018-04-26         2022-01-10      
#>  3    24 Kraken       kraken               1 2018-04-26         2022-01-10      
#>  4    32 Bleutrade    bleutrade            1 2018-04-26         2021-10-04      
#>  5    34 Bittylicious bittylicious         1 2018-04-26         2022-01-10      
#>  6    36 CEX.IO       cex-io               1 2018-04-26         2022-01-10      
#>  7    37 Bitfinex     bitfinex             1 2018-04-26         2022-01-10      
#>  8    42 HitBTC       hitbtc               1 2018-04-26         2022-01-10      
#>  9    50 EXMO         exmo                 1 2018-04-26         2022-01-10      
#> 10    61 Okcoin       okcoin               1 2018-04-26         2022-01-10      
#> # ... with 444 more rows

and then download information on “binance” and “kraken”:

ex_info <- exchange_info(exchanges %>% filter(slug %in% c('binance','kraken')))
#> > Scraping exchange info
#> 
#> Scraping exchanges from  https://web-api.coinmarketcap.com/v1/exchange/info?id=24,270  with  60  characters!
#> > Processing exchange info
#> 
ex_info
#> # A tibble: 2 x 18
#>      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          ""       http~ ""    2011-07-28T0~         0
#> 2   270 Binance binance NA          "Binanc~ http~ ""    2017-07-14T0~         0
#> # ... with 9 more variables: is_redistributable <lgl>, maker_fee <dbl>,
#> #   taker_fee <dbl>, spot_volume_usd <dbl>, spot_volume_last_updated <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     0.04

the 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     1292760024. 2022-01-10 19:30:16     
#> 2    18540127903. 2022-01-10 19:30:16

or 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 rows

Author/License

This project is licensed under the MIT License - see the <license.md> file for details</license.md>

Acknowledgments