In anb133/EurUsd: Getting EUR-USD exchange rates and evaluating Bitcoins.

The goal of this vignette is to demonstrate how eur and eurusds functions can be applied by using them in Bitocoin report generation.

Load all packages

library(EurUsd)
library(binancer)
library(data.table)
library(ggplot2)
library(scales)
library(pander)

1. Fetch the Daily Volume of Bitcoins

The daily volume and close prices of Bitcoins sold for "USDT" on Binance for the last 45 days can be fetched with the help of binance_klines from the binancer package:

# Getting data
coin_prices <- binance_klines('BTCUSDT', interval = '1d', limit = 45) 

# Keeping only relevant columns
BTC_45_days <- coin_prices[, .(date = as.Date(close_time), btcusd = close, volume)] 

# Set Key (for further merge)
setkey(BTC_45_days, date)

2. Get EUR/USD exchange rates

The exchange rates can be fetched with the help of eurusds function for specified dates:

# Get the first and the last dates for which exchange rates should be fetched
date_from <- min(BTC_45_days$date)
date_to <- max(BTC_45_days$date)

# Get the exchange rates
exchange_rates <- eurusds(date_from, date_to)

# Set Key (for further merge)
setkey(exchange_rates, date)

3. Put all information together

Merge the tables by date. Note that the Foreign Exchange market is closed on holidays and weekends, therefore for the days with the missing exchange rate the latest rate available would be used.

# Merge the tables
BTC_45_days <- exchange_rates[BTC_45_days, roll = TRUE] 

# Get the total value ein Euro 
BTC_45_days[, value_eur := btcusd * volume * eurusd] 

# Show first entires of the table
pandoc.table(head(BTC_45_days))

4. Create a plot

Now, as all necessary information is fetched, the plot is created. Note the usage of eur function in the last line: it formats numeric values on the Y-axis as Euros.

ggplot(BTC_45_days, aes(as.POSIXct(date), value_eur)) + 
  geom_line(aes(col = value_eur), size = 2) +
  theme_bw() + 
  theme(axis.text.x = element_text(vjust = 0.5), panel.grid.major = element_line(colour = "darkgrey"), 
        legend.position = 'None') + 
    labs(x = '', y = '', title = 'Overall Value of Bitcoins Sold in EUR', 
       subtitle = paste0(date_format('%B %d')(date_from), ' - ', date_format('%B %d')(date_to), ', ', 
                         date_format('%Y')(date_to))) + 
  scale_x_datetime(breaks = "5 day", labels = date_format("%d-%B"), minor_breaks = "1 day") + 
  scale_color_continuous(low = 'royalblue', high = 'olivedrab2') + 
  scale_y_continuous(labels = eur)