Author: serkor1

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'
)