In msperlin/adfeR: Data, exercises and Functions for Book "Analise de Dados Financeiros e Economicos com o R"

library(tidyverse)
library(BatchGetSymbols)

ticker <- '^FTSE'

first_date <- '2010-01-01'
last_date <- '2021-01-01'

df_FTSE_daily <- BatchGetSymbols(tickers = ticker, 
                            first.date = first_date,
                            last.date = last_date)[[2]]

# buy at t, sell at t+30
trade_window <- 30 

# find largest drops
largest_drops <- df_FTSE_daily %>%
  arrange(ret.adjusted.prices) %>%
  slice(1:20)

# There are many ways to solve the exercise. 
# Here we will use a loop which is the simplest way to looking at the problem.
# You could also solve it with the functional approach of package purrrr,
# that is, writing a function.

tab <- tibble()
for (i_date in seq_along(largest_drops$ref.date)) {

  my_date <- largest_drops$ref.date[i_date]
  # filter data to keep only datapoints in each horizon
  temp_df <- df_FTSE_daily %>%
    filter(ref.date >= my_date,
           ref.date <= my_date + trade_window)


  buy_price <- first(temp_df$price.adjusted)
  sell_price <- last(temp_df$price.adjusted)
  return <- sell_price/buy_price - 1

  tab <- bind_rows(tab, 
                   tibble(date = my_date, 
                          buy_price = buy_price, 
                          sell_price = sell_price, 
                          return = return))
}

print(tab)

# solution
my_sol <- mean(tab$return)

# none
my_answers <- format_percent(as.numeric(
  make_random_answers(my_sol))
)

Question

Para os mesmos dados diários do FTSE, verifique as datas e preços das 20 maiores quedas de preços. Se, para cada um desses casos, um investidor comprasse o índice no preço das maiores quedas e o mantivesse por 30 dias, qual seria seu retorno nominal médio por transação?

exams::answerlist(my_answers, markup = "markdown")

Solution

Meta-information

extype: schoice exsolution: r mchoice2string(c(TRUE, FALSE, FALSE, FALSE, FALSE), single = TRUE) exname: "function 01" exshuffle: TRUE

msperlin/adfeR documentation built on March 26, 2021, 3:05 a.m.