Accessing Coin Gecko's API for Cryptocurrency Market Data

Here I will give some simple examples of using Coin Gecko’s API to access market data for the broad cross-section of cryptocurrencies and tokens from various blockchains. Coin Gecko offers a basic subscription that is free to use for everyone interested in learning more about cryptocurrency data

*** Please include a citation if you use this code for a research project. ***

Load Packages

# Install Packages
# Load Packages

A Basic GET Call

Set the generic URL that can be used in conjunction with any of Coin Gecko’s endpoints to make a GET call. In lamen’s terms, a GET call is your computer asking the API to grant you access to a particular dataset (i.e. an “endpoint”).

# Base URL
base <- ''

We will use this as a simple example of a single GET call. For this endpoint (coins/list), we will be getting every cryptocurrency/token that is available on CoinGecko’s API

##### List all assets #####  

  # set endpoint
  endpoint <- 'coins/list'

  # GET call
  get <- GET(glue(base,endpoint))
  # prepare data
  dat <- fromJSON(rawToChar(get$content))
  # view data
  df1 <- data.frame(name=dat$name,ticker=dat$symbol,id=dat$id)
  # vector of all asset IDs
  IDs <- df1$id

Optimizing API Limits to get Full Historical Data

Each API has limits on the data you can retrieve for a single GET call, as well as limits on the number of GET calls you can push to the API in a given timeframe. For example, depending on your account with CoinGecko, you may be able to send somewhere between 10-50 GET calls per minute. For this reason, it is important to optimize the GET calls when getting data in bulk

First, we will save a list of crypto IDs that will go into our GET calls. We will also save the beginning and ending for our endpoint

##### Loop to get all days historical data --> 1 request for 1 day of data #####

  # blank list
  l1 <- list()

  # vector of all asset IDs
  IDs <- df1$id

  # start of endpoint
  start <- ''

  # end of endpoint
  end <- '/market_chart?vs_currency=usd&days=4000&interval=daily' # notice 4,000 days which gives me more than 10 years

Create a loop to optimize API request limits

p.s. I am only running one iteration of the outer loop. To run the full set, replace this with length(IDs). This will take a very long time to run

### Loop over all iterations of 30 IDs --> due to the request limit per minute ###

    # create index of ID places (per 30 requests)
      # number of iterations
      x <- floor(length(IDs)/30) # 30 IDs per 1 request
      # data frame of iterations and total IDs
      iters <- data.frame(tot.IDs=1:(x*30),iter=rep(1:x,each=30)) # 30 IDs per 1 request

  # run loop over all iterations (30 per)
  for(j in 1:1){

    # select which iteration you want to perform
    index <- iters %>% filter(iter==j)

    ### Loop over all IDs ###
    for(n in ((min(index$tot.IDs)):(max(index$tot.IDs)))){ # can only make 50 requests per minute

      # repeat this GET call until you don't get a status 429
      # GET call
      get <- GET(glue(start,IDs[n],end))

        # there are rate limits for GET calls for every API service. The best way to deal with this constraint is
        # to simply get for a `429` error which signifies that you have reached the limit. Simply wait a minute and try again
        # if you get a successful status code (200), then break
      # notice each call is in USD, 10 years of data (3,650 days), and daily frequency
      # prepare data
      dat <- fromJSON(rawToChar(get$content))
      # save
      l1[[n]] <- data.frame(unix=as.POSIXct(dat$prices[,1]/1000,origin = '1970-01-01'),price=dat$prices[,2],
                            mkt.cap=dat$market_caps[,2],vol=dat$total_volumes[,2]) %>% try()
      # save name of crypto
      l1[[n]]$name <- IDs[n]

    } # end first loop

# Export data before you lose it (piece meal)
df.out <-,l1)
Chad Dulle
Chad Dulle
PhD Candidate in Financial Economics