crypto2

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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

Version 2.0.2 (August 2024)

Slight change in api output broke crypto_info() (new additional column). Fixed.

Version 2.0.1 (July 2024)

Slight change in api output broke crypto_info(). Fixed.

Version 2.0.0 (May 2024)

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:

Version 1.4.7

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’)

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, 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"               CATEGORY

Additionally: 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 $     USD

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="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 rows

and 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      0.04

or 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"

Author/License

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

Acknowledgments

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