Cryptocurrency Market Data with quantmod and TTR

## load libraries
library(cryptoQuotes)

The cryptoQuotes were built with quantmod and TTR in mind. To demonstrate how cryptoQuotes works with these packages, we will use the following data,

## Get daily 
## Bitcoin from 
## the spot market
BTC <- get_quote(
  ticker  = "BTCUSD",
  source  = "kraken",
  futures = FALSE,
  interval= "1d",
  from    = "2024-01-01"
)

Cryptocurrency and quantmod

The BTC can be passed into the charting functions, or be used in the quantmod::tradeModel()-functions.

Building Cryptocurrency Trade Models

Below is an example on how to use the quantmod::tradeModel with BTC,

## 1) specify
## the model
BTC_model <-  quantmod::specifyModel(
  quantmod::Next(quantmod::OpCl(BTC)) ~ quantmod::Lag(quantmod::OpHi(BTC),0:3)
  )
#> Warning in formula.character(object, env = baseenv()): Using formula(x) is deprecated when x is a character vector of length > 1.
#>   Consider formula(paste(x, collapse = " ")) instead.

## 2) build the
## model
built_model <-  quantmod::buildModel(
  x = BTC_model,
  method ='lm',
  training.per = range(head(zoo::index(BTC), 10))
  )

## 3) trade the model
quantmod::tradeModel(built_model,leverage=2)
#> 
#>   Model:  lm1716033210.04428 
#> 
#>   C.A.G.R.:  29.58%  H.P.R.:  13.95% 
#> 
#>   Returns by period summary:
#> 
#>              weekly monthly quarterly yearly
#>     Max.     25.77%  48.73%    31.94% 34.57%
#>     3rd Qu.   4.49%  25.44%    24.45% 34.57%
#>     Mean      2.13%   8.89%    16.97% 34.57%
#>     Median   -1.69%   1.88%    16.97% 34.57%
#>     2rd Qu.  -5.00% -12.93%     9.48% 34.57%
#>     Min.    -14.61% -18.69%     1.99% 34.57%
#> 
#>   Period to date returns:
#> 
#>              weekly monthly quarterly yearly
#>              25.04%  25.44%     1.99% 34.57%

Charting Cryptocurrency with quantmod

## chart the
## BTC with candlesticks
## and Bollinger BAnds
quantmod::chartSeries(
  x = BTC,
  type = "candlesticks",
  TA = c(
    quantmod::addBBands()
  )
)

Cryptocurrency and TTR

Below is an example on how to use the BBands()-function from the TTR-package,

# 1) calculate
# Bollinger Bands
indicator <- TTR::BBands(
  quantmod::HLC(BTC)
)

# 2) add to the 
# cryptocurrency quote
BTC <- cbind(
  BTC, 
  indicator
)

Bitcoin with Bollinger Bands using TTR

Index	open	high	low	close	volume	dn	mavg	up	pctB
2024-05-13	61444.6	63400	60773.2	62910	1728.049	59007.488	62380.203	65752.919	0.497
2024-05-14	62910	63075	61000	61568.6	1843.74	59063.903	62226.352	65388.8	0.445
2024-05-15	61568.6	66450	61325.4	66233	4260.469	59021.242	62250.948	65480.654	0.874
2024-05-16	66233.1	66700	64557	65228.4	2547.44	58873.539	62328.205	65782.871	0.958
2024-05-17	65228.4	67443	65121.1	67043.5	2179.142	58594.212	62493.088	66391.965	1.018
2024-05-18	67043.6	67362.9	66798.2	67200.1	325.869	58299.518	62679.08	67058.642	1.007