Free R

Accessing Kraken API Data With R

Jan 24, 2021 · by CryptoDataDownload

After several requests, we have re-produced the Kraken Python tutorial into R for users. This R script will access the Kraken API for a given cryptocurrency pair, return the historical Open, High, Low, Close, Volume, VWAP, and TradeCount; then enrich with Volume in USD terms and a user-readable datatime before saving to a CSV file.
(*Please note that Kraken's API has terms of service and market data usage that may have restrictions not covered here, and so you should consult with Kraken directly if you have any questions concerning their API - this tutorial is not officially endorsed by Kraken, and CDD is not affiliated with the Kraken exchange. This tutorial should be used for your personal informational purposes*).
The R script uses these 3 libraries: jsonlite, glue, and anytime. jsonlite library helps read the data received via the API. glue library helps to format string variables in our function and other places. The anytime library makes it easy to convert unix timestamps into UTC or other timezones. This code is very similar to the function we wrote for the Coinbase API, but requires a few extra modifications for Kraken's API.

Live codebase R

#CryptoDataDownload
rm(list = ls())  # reset global variables
#import the libraries we need
library(jsonlite)
library(glue)
library(anytime)

# create a function to retrieve daily data
retreive_daily_data <- function(pair, filename) {
    url = glue("https://api.kraken.com/0/public/OHLC?pair={pair}&interval=1440")
    columnNames <- c('unix', 'open', 'high', 'low', 'close', 'vwap', 'volume', 'tradecount', 'misc')
    mydata <- fromJSON(url)
    df <- as.data.frame(mydata['result'])
    colnames(df) <- columnNames  # rename the columns
    df <- subset(df, select=-c(misc))  # remove the misc column
    df[, c(1:7)] <- sapply(df[, c(1:7)], as.numeric)  # cast OHLC/VWAP/tradecount columns to floats
    df$USD_Volume <- with(df, df$volume * df$close) # multiply close price * volume to get USD
    df$date <- anytime(df$unix, asUTC = TRUE) # convert unix epoch timestamp to UTC timezone

write.csv(df, file = filename, row.names=FALSE)
    }

newPair <- "XBTUSD"
fileName <- glue("dailyData{newPair}.csv")
runFunc <- retreive_daily_data(newPair, filename = fileName)

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THE PERFORMANCE OF TRADING SYSTEMS IS BASED ON THE USE OF COMPUTERIZED SYSTEM LOGIC. IT IS HYPOTHETICAL. PLEASE NOTE THE FOLLOWING DISCLAIMER. CFTC RULE 4.41: HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN LIMITATIONS. UNLIKE AN ACTUAL PERFORMANCE RECORD, SIMULATED RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, SINCE THE TRADES HAVE NOT BEEN EXECUTED, THE RESULTS MAY HAVE UNDER-OR-OVER COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF LIQUIDITY. SIMULATED TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFIT OR LOSSES SIMILAR TO THOSE SHOWN. U.S. GOVERNMENT REQUIRED DISCLAIMER: COMMODITY FUTURES TRADING COMMISSION. FUTURES AND OPTIONS TRADING HAS LARGE POTENTIAL REWARDS, BUT ALSO LARGE POTENTIAL RISK. YOU MUST BE AWARE OF THE RISKS AND BE WILLING TO ACCEPT THEM IN ORDER TO INVEST IN THE FUTURES AND OPTIONS MARKETS. DON’T TRADE WITH MONEY YOU CAN’T AFFORD TO LOSE. THIS IS NEITHER A SOLICITATION NOR AN OFFER TO BUY/SELL FUTURES OR OPTIONS. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE DISCUSSED ON THIS WEBSITE. THE PAST PERFORMANCE OF ANY TRADING SYSTEM OR METHODOLOGY IS NOT NECESSARILY INDICATIVE OF FUTURE RESULTS.