Demo of eur and eurusds from the xchangeR package

This vignette demos the usage of two functions from the xchangeR package:

• eur converts a number to a string by adding the Euro sign (€), rounding up to 2 digits and using the comma as the separator.
• eurusds looks up the daily USD/EUR exchange rate via an API call for the specified date range.

In this demo we’re going to:

1. Fetch the daily volume of Bitcoins sold for “USDT” on Binance in the past 45 days.
2. Calculate the prices in EUR.
3. Report on the overall value of the asset in EUR on a ggplot.

Load the xchangeR package

# Install the package if necessary
# remotes::install_github("szigony/xchangeR")
library(xchangeR)

Other packages used for this demo:

• binancer
• dplyr
• data.table
• ggplot2
• scales

Fetch the daily volume of Bitcoins sold

Let’s look at the daily Bitcoin prices in USD for the past 45 days. We’re only interested in the day (open_time) and the opening price (open).

btc_prices <- as.data.table(
  binance_klines("BTCUSDT", interval = "1d", limit = 45) %>% 
  mutate(date = as.Date(open_time, format = "%Y-%m-%d"),
         price = open) %>% 
  select(date, price)
)

Calculate the price in EUR

At this point we have the daily prices for Bitcoin, but only in USD. In order to see the prices in EUR, we have to look up the daily exchange rates by utilizing the eurusds function and join them to btc_prices.

exchange_rates <- eurusds(last_x_days = 45)
head(left_join(btc_prices, exchange_rates, by = "date"))
#>         date   price exchange_rate
#> 1 2019-04-18 5202.41     0.8888889
#> 2 2019-04-19 5258.44            NA
#> 3 2019-04-20 5258.68            NA
#> 4 2019-04-21 5292.91            NA
#> 5 2019-04-22 5257.41            NA
#> 6 2019-04-23 5357.14     0.8892841

As we can see, there are some NA values in the exchange_rate column. The reason for this is that the exchange rates are only available from the API for weekdays. To overcome this obstacle, we have to apply a rolling join, rather than a left join.

# Set the keys for the join
setkey(btc_prices, date)
setkey(exchange_rates, date)

btc_prices <- exchange_rates[btc_prices, roll = TRUE] %>% 
  na.omit()
head(btc_prices)
#>          date exchange_rate   price
#> 1: 2019-04-18     0.8888889 5202.41
#> 2: 2019-04-19     0.8888889 5258.44
#> 3: 2019-04-20     0.8888889 5258.68
#> 4: 2019-04-21     0.8888889 5292.91
#> 5: 2019-04-22     0.8888889 5257.41
#> 6: 2019-04-23     0.8892841 5357.14

Now we can calculate the price in EUR, and format it as such with the eur function.

btc_prices <- btc_prices %>% 
  mutate(price_in_eur = eur(exchange_rate * price)) %>%
  select(date, price_in_eur)
head(btc_prices)
#>         date price_in_eur
#> 1 2019-04-18    \2004,624.36
#> 2 2019-04-19    \2004,674.17
#> 3 2019-04-20    \2004,674.38
#> 4 2019-04-21    \2004,704.81
#> 5 2019-04-22    \2004,673.25
#> 6 2019-04-23    \2004,764.02

# E.g. the price for the first day in Euro format:
btc_prices$price_in_eur[1]
#> [1] "\2004,624.36"

Report on the overall value of the asset

As we converted the price to Euro format in the previous example, in order to visualize it, we’ll utilize the uneur function from the xchangeR package that transforms the string to a number.

ggplot(btc_prices, aes(date, uneur(price_in_eur))) +
  geom_col(fill = "skyblue2") +
  ggtitle("Bitcoin prices in Euro for the past 45 days") +
  labs(x = NULL, y = NULL) +
  scale_y_continuous(breaks = seq(0, 7500, 500), labels = eur) +
  scale_x_date(labels = date_format("%m-%d"), date_breaks = "5 days") +
  geom_hline(yintercept = mean(uneur(btc_prices$price_in_eur)), color = "indianred2", size = 1) +
  annotate(geom = "text", x = min(btc_prices$date) + 6, 
           y = mean(uneur(btc_prices$price_in_eur)) + 250, size = 3.5,
           label = paste("Mean price in Euro:", eur(mean(uneur(btc_prices$price_in_eur))))) +
  theme(axis.text.x = element_text(angle = 45),
        plot.title = element_text(hjust = 0.5, size = 16, face = "bold"))