Generate a Full Tax Report"
In cryptoTax: Report Crypto Taxes (Canada Only)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(cryptoTax)

It is possible to generate a full tax report with cryptoTax. This vignette shows how.

Preparing your list of coins

First, you want to fetch the price information for all your coins.

Note: Some exchanges don't require fetching this information from coinmarketcap because they already give the CAD value of your trades or revenues. However, this is still necessary to get the current value, and therefore your unrealized gains and losses.

library(cryptoTax)

my.coins <- c("BTC", "ETH", "ADA", "CRO", "LTC", "USDC", "BUSD", "CEL", "PRE", "ETHW", "BAT")
list.prices <- prepare_list_prices(coins = my.coins, start.date = "2021-01-01")

Note: When you have a lot of coins, it can be better to split the calls to coinmarketcap in two or more and row bind the results with rbind() because otherwise we risk getting an error from the coinmarketcap API. See below for an example:

library(cryptoTax)

my.coins1 <- c("BTC", "ETH", "ADA", "CRO", "LTC", "USDC")
my.coins2 <- c("BUSD", "CEL", "PRE", "ETHW", "BAT")

list.prices1 <- prepare_list_prices(coins = my.coins1, start.date = "2021-01-01")
list.prices2 <- prepare_list_prices(coins = my.coins2, start.date = "2021-01-01")

list.prices <- rbind(list.prices1, list.prices2)

Formatting your data

Below we use a shortcut for this vignette to format all exchanges quickly with lapply() and the format_detect() function. However, you can use the dedicated functions should you wish so.

exchanges <- list(
  data_adalite, data_binance, data_binance_withdrawals, data_blockfi, data_CDC, 
  data_CDC_exchange_rewards, data_CDC_exchange_trades, data_CDC_wallet, data_celsius,
  data_coinsmart, data_exodus, data_gemini, data_newton, data_pooltool, data_presearch,
  data_shakepay, data_uphold)

formatted.data <- format_detect(exchanges)

Adjusted Cost Base

Next, we can begin processing that data. We start by formatting the Adjusted Cost Base (ACB) of each transaction. The formatted.ACB is going to be our core object with which we will be working for all future steps.

formatted.ACB <- format_ACB(formatted.data)

Per default, format_ACB() considers taxable revenue (and not aquisition at $0 ACB) the following transaction types: staking, interests, and mining. If you want a different treatment for those transactions (or other types of revenues like cashback and airdrops), use the as.revenue argument of format_ACB().

We get a warning that there are negative values in some places, therefore that we might have forgotten some transactions. The check_missing_transactions() function makes it easy to identify which transactions (and therefore which coin) are concerned with this.

check_missing_transactions(formatted.ACB)

Next, we might want to make sure that we have downloaded the latest files for each exchange. If you shake your phone every day for sats, or you receive daily weekly payments from staking, you would expect the latest date to be recent. For this we use the get_latest_transactions() function.

get_latest_transactions(formatted.ACB)

At this stage, it is possible to list transactions by coin, if you have few transactions. The output can be very long as soon as you have many transactions however, so we will not be showing it here, but you can have a look at the listby_coin() function and its example.

Full Tax Report

Next, we need to calculate a few extra bits of information for the final report. Fortunately, the prepare_report() function makes this easy for us.

report.info <- prepare_report(formatted.ACB, 
                              tax.year = 2021, 
                              local.timezone = "America/Toronto",
                              list.prices = list.prices)

The report.info object is a list containing all the different info (tables, figures) necessary for the final report. They can be accessed individually too:

names(report.info)

Finally, to generate the report, we use print_report() with the relevant tax year, name of the individual, and the report.info object we just created:

print_report(tax.year = "2021", 
             name = "Mr. Cryptoltruist", 
             report.info)

library(dplyr)
tax.year <- "2021"
name <- "Mr. Cryptoltruist"
person.name <- paste("Name:", name)
total.income.numeric <- dplyr::last(report.info$table.revenues$staking) +
  dplyr::last(report.info$table.revenues$interests)
total.income <- format_dollars(total.income.numeric)
total.cost <- report.info$report.summary$Amount[5]
gains <- report.info$report.summary$Amount[2]
gains.numeric <- format_dollars(gains, "numeric")
gains.50 <- format_dollars(gains.numeric * 0.5)
losses <- report.info$report.summary$Amount[3]
net <- report.info$report.summary$Amount[4]
net.numeric <- format_dollars(net, "numeric")
net.50 <- format_dollars(net.numeric * 0.5)
total.tax <- format_dollars(net.numeric * 0.5 + total.income.numeric)
sup.losses.total <- report.info$sup.losses[nrow(report.info$sup.losses), "sup.loss"]
tot.losses <- format_dollars(as.numeric(losses) - sup.losses.total)
tot.sup.loss <- as.numeric(tot.losses) + sup.losses.total
if (tax.year == "all") {
  tax.year <- "all years"
} else {
  tax.year <- tax.year
}