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 documentationSince 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’/‘weekly’)
You 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 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
#>
#> 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 × 20
#> 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 Bitco… bitc… http… bitcoin ""
#> 2 2 Litecoin LTC coin "## What Is Litec… lite… http… litecoin ""
#> 3 3 Namecoin NMC coin "Namecoin (NMC) i… name… http… namecoin ""
#> # ℹ 11 more variables: date_added <chr>, twitter_username <chr>,
#> # is_hidden <int>, date_launched <chr>,
#> # self_reported_circulating_supply <lgl>, self_reported_market_cap <lgl>,
#> # infinite_supply <lgl>, tags <list>, self_reported_tags <lgl>, urls <list>,
#> # platform <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
#> <chr> <chr>
#> 1 bitcoin mineable
#> 2 bitcoin ftx-bankruptcy-estate
#> 3 litecoin mineable
#> 4 litecoin medium-of-exchange
#> 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 × 3
#> slug name url
#> <chr> <chr> <chr>
#> 1 bitcoin reddit https://reddit.com/r/bitcoin
#> 2 litecoin twitter https://twitter.com/litecoin
#> 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", 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 × 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 bitcoin Bitc… BTC USD 2.90e+4 2.96e+4 2.88e+4
#> 2 2021-01-02 23:59:59 1 bitcoin Bitc… BTC USD 2.94e+4 3.32e+4 2.91e+4
#> 3 2021-01-01 23:59:59 2 liteco… Lite… LTC USD 1.25e+2 1.33e+2 1.23e+2
#> 4 2021-01-02 23:59:59 2 liteco… Lite… LTC USD 1.26e+2 1.40e+2 1.24e+2
#> 5 2021-01-01 23:59:59 3 nameco… Name… NMC USD 4.39e-1 4.63e-1 4.32e-1
#> 6 2021-01-02 23:59:59 3 nameco… Name… NMC USD 4.51e-1 5.10e-1 4.15e-1
#> # ℹ 7 more variables: close <dbl>, volume <dbl>, market_cap <dbl>,
#> # time_open <dttm>, time_close <dttm>, time_high <dttm>, time_low <dttm>Similarly, we could download the same data on a monthly basis.
# retrieve historical data for all (the first 3) of them
coin_hist_m <- crypto_history(coins, limit=3, start_date="20210101", end_date="20210501", interval ="monthly", 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 × 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 bitcoin Bitc… BTC USD 2.90e+4 2.96e+4 2.88e+4
#> 2 2021-02-01 23:59:59 1 bitcoin Bitc… BTC USD 3.31e+4 3.46e+4 3.24e+4
#> 3 2021-01-01 23:59:59 2 liteco… Lite… LTC USD 1.25e+2 1.33e+2 1.23e+2
#> 4 2021-02-01 23:59:59 2 liteco… Lite… LTC USD 1.30e+2 1.36e+2 1.26e+2
#> 5 2021-01-01 23:59:59 3 nameco… Name… NMC USD 4.39e-1 4.63e-1 4.32e-1
#> 6 2021-02-01 23:59:59 3 nameco… Name… NMC USD 7.82e-1 8.05e-1 7.48e-1
#> # ℹ 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 × 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 Kč CZK
#> 9 2789 Danish Krone kr DKK
#> 10 2790 Euro € EUR
#> # ℹ 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", 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) %>% slice(1:2)
#> # A tibble: 12 × 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 2021-01-02 00:00:00 1 bitco… Bitc… BTC BTC 1 e+0 1.00e+0 9.99e-1
#> 2 2021-01-03 00:00:00 1 bitco… Bitc… BTC BTC 1 e+0 1.00e+0 9.97e-1
#> 3 2021-01-02 00:00:00 1 bitco… Bitc… BTC EUR 2.37e+4 2.43e+4 2.36e+4
#> 4 2021-01-03 00:00:00 1 bitco… Bitc… BTC EUR 2.42e+4 2.73e+4 2.40e+4
#> 5 2021-01-02 00:00:00 2 litec… Lite… LTC BTC 4.30e-3 4.55e-3 4.26e-3
#> 6 2021-01-03 00:00:00 2 litec… Lite… LTC BTC 4.31e-3 4.24e-3 4.22e-3
#> 7 2021-01-02 00:00:00 2 litec… Lite… LTC EUR 1.02e+2 1.09e+2 1.01e+2
#> 8 2021-01-03 00:00:00 2 litec… Lite… LTC EUR 1.04e+2 1.16e+2 1.02e+2
#> 9 2021-01-02 00:00:00 3 namec… Name… NMC BTC 1.51e-5 1.58e-5 1.50e-5
#> 10 2021-01-03 00:00:00 3 namec… Name… NMC BTC 1.54e-5 1.57e-5 1.31e-5
#> 11 2021-01-02 00:00:00 3 namec… Name… NMC EUR 3.60e-1 3.80e-1 3.54e-1
#> 12 2021-01-03 00:00:00 3 namec… Name… NMC EUR 3.72e-1 4.21e-1 3.41e-1
#> # ℹ 7 more variables: close <dbl>, volume <dbl>, market_cap <dbl>,
#> # time_open <dttm>, time_close <dttm>, time_high <dttm>, time_low <dttm>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: 10 × 24
#> id name symbol slug infinite_supply self_reported_circulatin…¹
#> <int> <chr> <chr> <chr> <lgl> <lgl>
#> 1 1 Bitcoin BTC bitcoin FALSE NA
#> 2 52 XRP XRP xrp FALSE NA
#> 3 74 Dogecoin DOGE dogecoin TRUE NA
#> 4 825 Tether USDt USDT tether TRUE NA
#> 5 1027 Ethereum ETH ethereum TRUE NA
#> 6 1839 BNB BNB bnb FALSE NA
#> 7 2010 Cardano ADA cardano FALSE NA
#> 8 3408 USDC USDC usd-coin FALSE NA
#> 9 5426 Solana SOL solana TRUE NA
#> 10 5805 Avalanche AVAX avalanche FALSE NA
#> # ℹ abbreviated name: ¹self_reported_circulating_supply
#> # ℹ 18 more variables: self_reported_market_cap <lgl>, tvl_ratio <lgl>,
#> # last_updated <date>, USD_price <dbl>, USD_volume_24h <dbl>,
#> # USD_volume_change_24h <dbl>, USD_percent_change_1h <dbl>,
#> # USD_percent_change_24h <dbl>, USD_percent_change_7d <dbl>,
#> # USD_percent_change_30d <dbl>, USD_percent_change_60d <dbl>,
#> # USD_percent_change_90d <dbl>, USD_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)
all_quotes
#> # A tibble: 3,921 × 13
#> timestamp btc_dominance eth_dominance active_cryptocurrencies
#> <date> <dbl> <dbl> <int>
#> 1 2013-04-29 94.2 0 NA
#> 2 2013-04-30 94.4 0 NA
#> 3 2013-05-01 94.4 0 NA
#> 4 2013-05-02 94.1 0 NA
#> 5 2013-05-03 94.2 0 NA
#> 6 2013-05-04 93.9 0 NA
#> 7 2013-05-05 94.0 0 NA
#> 8 2013-05-06 94.1 0 NA
#> 9 2013-05-07 94.4 0 NA
#> 10 2013-05-08 94.4 0 NA
#> # ℹ 3,911 more rows
#> # ℹ 9 more variables: active_exchanges <int>, active_market_pairs <int>,
#> # USD_total_market_cap <dbl>, USD_total_volume_24h <dbl>,
#> # USD_total_volume_24h_reported <dbl>, USD_altcoin_market_cap <dbl>,
#> # USD_altcoin_volume_24h <dbl>, USD_altcoin_volume_24h_reported <dbl>,
#> # USD_timestamp <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: 703 × 8
#> id name slug is_active is_listed is_redistributable
#> <int> <chr> <chr> <int> <int> <int>
#> 1 16 Poloniex poloniex 1 0 1
#> 2 21 BTCC btcc 1 0 1
#> 3 24 Kraken kraken 1 0 1
#> 4 34 Bittylicious bittylicious 1 0 0
#> 5 36 CEX.IO cex-io 1 0 1
#> 6 37 Bitfinex bitfinex 1 0 1
#> 7 42 HitBTC hitbtc 1 0 1
#> 8 50 EXMO exmo 1 0 1
#> 9 61 Okcoin okcoin 1 0 1
#> 10 68 Indodax indodax 1 0 1
#> # ℹ 693 more rows
#> # ℹ 2 more variables: first_historical_data <date>, last_historical_data <date>and then download information on “binance” and “bittrex”:
ex_info <- exchange_info(exchanges %>% filter(slug %in% c('binance','bittrex')), finalWait=FALSE)
#> ❯ Scraping exchange info
#>
#> Scraping exchanges from https://web-api.coinmarketcap.com/v1/exchange/info?id=270 with 57 characters!
#> ❯ Processing exchange info
#>
ex_info
#> # A tibble: 1 × 23
#> id name slug description notice logo type porStatus porAuditStatus
#> * <int> <chr> <chr> <chr> <chr> <chr> <chr> <int> <int>
#> 1 270 Binance binance "## What Is… "" http… "" 1 0
#> # ℹ 14 more variables: walletSourceStatus <int>, porSwitch <chr>,
#> # date_launched <chr>, is_hidden <int>, is_redistributable <int>,
#> # maker_fee <dbl>, taker_fee <dbl>, spot_volume_usd <dbl>,
#> # spot_volume_last_updated <dttm>, weekly_visits <int>, tags <lgl>,
#> # urls <list>, countries <lgl>, fiats <list>Then we can access information on the fee structure,
ex_info %>% select(contains("fee"))
#> # A tibble: 1 × 2
#> maker_fee taker_fee
#> <dbl> <dbl>
#> 1 0.02 0.04the amount of cryptocurrencies being traded (in USD)
ex_info %>% select(contains("spot"))
#> # A tibble: 1 × 2
#> spot_volume_usd spot_volume_last_updated
#> <dbl> <dttm>
#> 1 11352096686. 2024-01-29 23:25:16or the fiat currencies allowed:
ex_info %>% select(slug,fiats) %>% tidyr::unnest(fiats)
#> # A tibble: 11 × 2
#> slug value
#> <chr> <chr>
#> 1 binance "EUR"
#> 2 binance " GBP"
#> 3 binance " BRL"
#> 4 binance " AUD"
#> 5 binance " UAH"
#> 6 binance " RUB"
#> 7 binance " TRY"
#> 8 binance " ZAR"
#> 9 binance " PLN"
#> 10 binance " NGN"
#> 11 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.