Author: serkor1

{cryptoQuotes}: Open access to cryptocurrency market data in R (Update)

The {cryptoQuotes}-package have been updated to version 1.3.0. With this update comes many new features,  and breaking changes. Prior to version 1.3.0 the package were using camelCase (See for example this post), with no particular style guide. The package now uses the tidyverse style guide which, in return, have deprecated a few core functions.

Note: Only the styling is affected, the returned market data is still xts/zoo-objects
Of the many new features and enhancements includes dark and light themed charting, and  a wide array of new sentiment indicators. The full documentation can be found on pkgdown.

In this blog post the new charting features will be showcased using hourly Bitcoin OHLC-V and long-short ratios from the last two days (From writing this draft).

Cryptocurrency market data in R

# 0) load library
library(cryptoQuotes)
To extract the Bitcoin OHLC-V,  the get_quote()-function [previously getQuote()]  is used as  shown below,

# 1) extract last two
# days of Bitcoin on the
# hourly chart
tail(
  BTC <- get_quote(
    ticker   = "BTCUSDT",
    source   = "binance",
    interval = "1h",
    from     = Sys.Date() - 2
  )
)
#>                        open    high     low   close    volume
#> 2024-06-05 02:00:00 70580.0 70954.1 70462.8 70820.1  7593.081
#> 2024-06-05 03:00:00 70820.2 71389.8 70685.9 71020.7 11466.934
#> 2024-06-05 04:00:00 71020.7 71216.0 70700.0 70892.1  7824.993
#> 2024-06-05 05:00:00 70892.2 71057.0 70819.1 70994.0  5420.481
#> 2024-06-05 06:00:00 70994.0 71327.9 70875.9 71220.2  7955.595
#> 2024-06-05 07:00:00 71220.2 71245.0 70922.0 70988.8  3500.795
The long-short ratios on Bitcoin in the same hourly interval is retrieved using the get_lsratio()-function [previously getLSRatio()] as shown below,

# 2) extract last two days
# of long-short ratio on
# Bitcoin
tail(
  BTC_LS <- get_lsratio(
    ticker   = "BTCUSDT",
    source   = "binance",
    interval = "1h",
    from     = Sys.Date() - 2
  )
)
#>                       long  short  ls_ratio
#> 2024-06-05 02:00:00 0.4925 0.5075 0.9704433
#> 2024-06-05 03:00:00 0.4938 0.5062 0.9755038
#> 2024-06-05 04:00:00 0.4942 0.5058 0.9770660
#> 2024-06-05 05:00:00 0.4901 0.5099 0.9611689
#> 2024-06-05 06:00:00 0.4884 0.5116 0.9546521
#> 2024-06-05 07:00:00 0.4823 0.5177 0.9316206
Prior to version 1.3.0 all charting with indicators were done with the magrittr-pipe operator, both internally and externally. This came with a overhead on both efficiency and readability (Opinionated, I know). The charting has been reworked in terms of layout and syntax.

Below is an example of a dark-themed chart with the long-short ratio alongside simple moving averages, bollinger bands and volume indicators,

# 3) dark-themed
# chart
chart(
  ticker = BTC,
  main   = kline(),
  indicator = list(
    bollinger_bands(),
    sma(n = 7),
    sma(n = 14)

  ),
  sub = list(
    volume(),
    lsr(ratio = BTC_LS)
  )
)

The light-themed chart have been reworked, and have received some extra love, such that its different from the default colors provided by the {plotly}-package,

# 4) light-themed
# chart
chart(
  ticker = BTC,
  main   = kline(),
  indicator = list(
    bollinger_bands(),
    sma(n = 7),
    sma(n = 14)
  ),
  sub = list(
    volume(),
    lsr(ratio = BTC_LS)
  ),
  options = list(
    dark = FALSE
  )
)

About the {cryptoQuotes}-package

The {cryptoQuotes}-package is a high-level API-client that interacts with public market data endpoints from major cryptocurrency exchanges using the {curl}-package.

The endpoints, which are publicly accessible and maintained by the exchanges themselves, ensure a consistent and reliable access to high-quality cryptocurrency market data with R.

Installing {cryptoQuotes}
The {cryptoQuotes}-package can be installed via CRAN,

# installing {cryptoQuotes}
install.packages(
  pkgs ="cryptoQuotes",
  dependencies = TRUE
)

Created on 2024-06-05 with reprex v2.1.0

Gauging Cryptocurrency Market Sentiment in R

Navigating the volatile world of cryptocurrencies requires a keen understanding of market sentiment. This blog post explores some of the essential tools and techniques for analyzing the mood of the crypto market, using the cryptoQuotes-package.

The Cryptocurrency Fear and Greed Index in R

The Fear and Greed Index is a market sentiment tool that measures investor emotions, ranging from 0 (extreme fear) to 100 (extreme greed). It analyzes data like volatility, market momentum, and social media trends to indicate potential overvaluation or undervaluation of cryptocurrencies. This index helps investors identify potential buying or selling opportunities by gauging the market’s emotional extremes.

This index can be retrieved by using the cryptoQuotes::getFGIndex()-function, which returns the daily index within a specified time-frame,

## Fear and Greed Index
## from the last 14 days
tail(
  FGI <- cryptoQuotes::getFGIndex(
    from = Sys.Date() - 14
  )
)
#>            FGI
#> 2024-01-03  70
#> 2024-01-04  68
#> 2024-01-05  72
#> 2024-01-06  70
#> 2024-01-07  71
#> 2024-01-08  71

The Long-Short Ratio of a Cryptocurrency Pair in R

The Long-Short Ratio is a financial metric indicating market sentiment by comparing the number of long positions (bets on price increases) against short positions (bets on price decreases) for an asset. A higher ratio signals bullish sentiment, while a lower ratio suggests bearish sentiment, guiding traders in making informed decisions.

The Long-Short Ratio can be retrieved by using the cryptoQuotes::getLSRatio()-function, which returns the ratio within a specified time-frame and granularity. Below is an example using the Daily Long-Short Ratio on Bitcoin (BTC),

## Long-Short Ratio
## from the last 14 days
tail(
  LSR <- cryptoQuotes::getLSRatio(
    ticker = "BTCUSDT",
    interval = '1d',
    from = Sys.Date() - 14
  )
)
#>              Long  Short LSRatio
#> 2024-01-03 0.5069 0.4931  1.0280
#> 2024-01-04 0.6219 0.3781  1.6448
#> 2024-01-05 0.5401 0.4599  1.1744
#> 2024-01-06 0.5499 0.4501  1.2217
#> 2024-01-07 0.5533 0.4467  1.2386
#> 2024-01-08 0.5364 0.4636  1.1570

Putting it all together

Even though cryptoQuotes::getLSRatio() is an asset-specific sentiment indicator, and cryptoQuotes::getFGIndex() is a general sentiment indicator, there is much information to be gathered by combining this information.

This information can be visualized by using the the various charting-functions in the cryptoQuotes-package,

## get the BTCUSDT
## pair from the last 14 days
BTCUSDT <- cryptoQuotes::getQuote(
  ticker = "BTCUSDT",
  interval = "1d",
  from = Sys.Date() - 14
)
## chart the BTCUSDT
## pair with sentiment indicators
cryptoQuotes::chart(
  slider = FALSE,
  chart = cryptoQuotes::kline(BTCUSDT) %>%
    cryptoQuotes::addFGIndex(FGI = FGI) %>% 
    cryptoQuotes::addLSRatio(LSR = LSR)
)
Bitcoin charted against Fear and Greed Index and the Long-Short Ratio using R
Bitcoin (BTC) plotted with Fear and Greed Index along side the Long-Short Ratio

Installing cryptoQuotes

Installing via CRAN

# install from CRAN
install.packages(
  pkgs = 'cryptoQuotes',
  dependencies = TRUE
)

Installing via Github

# install from github
devtools::install_github(
  repo = 'https://github.com/serkor1/cryptoQuotes/',
  ref = 'main'
)

Note: The latest price may vary depending on time of publication relative to the rendering time of the document. This document were rendered at 2024-01-08 23:30 CET

Cryptocurrency Market Data in R

Getting cryptocurrency OHLCV data in R without having to depend on low-level coding using, for example, curl or httr2, have not been easy for the R community.

There is now a high-level API Client available on CRAN which fetches all the market data without having to rely on web-scrapers, API keys or low-level coding.

Bitcoin Prices in R (Example)

This high-level API-client have one main function, getQuotes(), which returns cryptocurrency market data with a xts– and zoo-class. The returned objects contains Open, High, Low, Close and Volume data with different granularity, from the currently supported exchanges.

In this blog post I will show how to get hourly Bitcoin (BTC) prices in R
using the getQuotes()-function. See the code below,
# 1) getting hourly BTC
# from the last 3 days

BTC <- cryptoQuotes::getQuote(
 ticker   = "BTCUSDT", 
 source   = "binance", 
 futures  = FALSE, 
 interval = "1h", 
 from     = as.character(Sys.Date() - 3)
)
Bitcoin (BTC) OHLC-prices (Output from getQuote-function)
Index Open High Low Close Volume
2023-12-23 19:00:00 43787.69 43821.69 43695.03 43703.81 547.96785
2023-12-23 20:00:00 43703.82 43738.74 43632.77 43711.33 486.4342
2023-12-23 21:00:00 43711.33 43779.71 43661.81 43772.55 395.6197
2023-12-23 22:00:00 43772.55 43835.94 43737.85 43745.86 577.03505
2023-12-23 23:00:00 43745.86 43806.38 43701.1 43702.16 940.55167
2023-12-24 43702.15 43722.25 43606.18 43716.72 773.85301

The returned Bitcoin prices from getQuotes() are compatible with quantmod and TTR, without further programming. Let me demonstrate this using chartSeries(), addBBands() and addMACD() from these powerful libraries,

# charting BTC
# using quantmod
quantmod::chartSeries(
 x = BTC,
 TA = c(
    # add bollinger bands
    # to the chart
    quantmod::addBBands(), 
    # add MACD indicator
    # to the chart
    quantmod::addMACD()
 ), 
 theme = quantmod::chartTheme("white")
)
Cryptocurrency charts using R
Charting Bitcoin prices using quantmod and TTR

Installing cryptoQuotes

Stable version

# install from CRAN
install.packages(
  pkgs = 'cryptoQuotes',
  dependencies = TRUE
)

Development version

# install from github
devtools::install_github(
  repo = 'https://github.com/serkor1/cryptoQuotes/',
  ref = 'main'
)