Interactive Cryptocurrency Charts in R

## load library
library(cryptoQuotes)

As an experimental feature {cryptoQuotes} has a variety of chart-functions built on top of {plotly} and {TTR} to visualize cryptocurrency market data and trading indicators interactively.

Throughout this article we will analyze Bitcoin on the hourly chart called by the get_quotes()-function,

## Get the
## SPOT price of 
## Bitcoin on the hourly
BTC <- get_quote(
  ticker   = "BTCUSD",
  source   = "kraken",
  futures  = FALSE,
  interval = "1h",
  from     = Sys.Date() - 7
)

Price charts

There are two main price charts. The Japanese candlestick chart, kline(), and the basic OHLC chart, ohlc(), charts. A chart has to be wrapped in the chart()-function.

Candlestick charts

## chart BTC
## with candlesticks
chart(
  ticker = BTC,
  main   = kline()
)

OHLC charts

## chart BTC
## with candlesticks
chart(
  ticker = BTC,
  main   = ohlc()
)

The chart()-function comes with various options for color deficiency and themes. Use ?chart for further details.

Charting indicators

All the charts supports various indicators such as the relative strenght index (rsi())-, Moving Average Convergence Divergence (macd())- and various moving average indicators. These can be passed into their respective arguments as follows,

## chart BTC
## with candlesticks
## and various indicators
chart(
  ticker = BTC,
  main   = kline(),
  sub    = list(
    volume(),
    rsi(),
    macd()
  ),
  indicator = list(
    sma(n = 7),
    sma(n = 21),
    bollinger_bands()
  )
)

Charting events

All charts supports passing event_data, which are any type of data.frame, that can be plotted on the chart. Below is an example of plotting the crossing of the MA,

## Calculate moving averages
## SMA(7) and SMA(10)
BTC$short_SMA <- TTR::SMA(
  x = BTC$close,
  n = 7
)

BTC$long_SMA <- TTR::SMA(
  x = BTC$close,
  n = 21
)

## Determine the cross
## from below
BTC$cross <- as.numeric(
  lag(BTC$short_SMA) < BTC$long_SMA &
    BTC$short_SMA > BTC$long_SMA 
)

Based on the cross indicator, the BTC pair can now be subset, and processed accordingly.

## create event data
## and store it as a data.rfrae
event_data <- subset(
  BTC,
  cross == 1
)

# 1.1) Extract the index
# from the event data
index <- zoo::index(
  event_data
)

# 1.2) Convert the coredata
# into a data.frame
event_data <- as.data.frame(
  zoo::coredata(event_data)
)

# 1.3) Add the index into the data.frame
# case insensitive
event_data$index <- index

# 1.4) add events to the data.
# here we use Buys and Sells.
event_data$event <- 'Bull Cross'
event_data$color <- 'darkgray'

Finally, the crosses can be plotted using the chart() as follows,

## add the events
## the chart, along
## with Moving averages
## and the Bollinger Bands
## in a candlestick chart
chart(
  ticker    = BTC,
  main      = kline(),
  indicator = list(
    bollinger_bands(),
    sma(n = 7),
    sma(n = 21)
  ),
  event_data = event_data
)

Limitations

The charting functions are still at an experimental-phase as per version 1.3.0, and might therefore include various bugs of all sizes and species.