Interactive Cryptocurrency Charts in R

## load library
library(cryptoQuotes)

As an experimental feature, the cryptoQuotes-package 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.

All charts comes with volume and bollinger bands by default.

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 RSI, MACD and MA. 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() and addEvents() functions, like below,

## 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