Real-world transaction data

Taking this is here from an e-mail. This shows real transaction data at a daily frequency. We can see relevant peaks on Wed-Tue. Sun and Sat are missing.

Would it make sense to add this in the trading days chp as part of the introduction? This would make it clear that if we see fewer or less of these days it might have an impact on the final adjustment. Would make sense to make a rough estimate of what's the impact on different scenarios?

library(ggplot2)

trans_df <- 
  tibble::tibble(
    value_date = as.Date(c(
      "2022-07-01", "2022-07-04", "2022-07-05", "2022-07-06", "2022-07-07",
      "2022-07-08", "2022-07-13", "2022-07-14", "2022-07-15", "2022-07-18",
      "2022-07-19", "2022-07-20", "2022-07-21", "2022-07-22", "2022-07-25",
      "2022-07-26", "2022-07-27", "2022-07-28", "2022-07-29", "2022-08-01",
      "2022-08-02", "2022-08-03", "2022-08-04", "2022-08-05", "2022-08-08",
      "2022-08-09", "2022-08-10", "2022-08-11", "2022-08-12", "2022-08-15",
      "2022-08-16", "2022-08-17", "2022-08-18", "2022-08-19", "2022-08-22",
      "2022-08-23", "2022-08-24", "2022-08-25", "2022-08-26", "2022-08-29",
      "2022-08-30", "2022-08-31", "2022-09-01", "2022-09-02", "2022-09-05",
      "2022-09-06", "2022-09-07", "2022-09-08", "2022-09-09", "2022-09-12",
      "2022-09-13", "2022-09-14", "2022-09-15", "2022-09-16", "2022-09-19",
      "2022-09-20", "2022-09-21", "2022-09-22", "2022-09-23", "2022-09-26",
      "2022-09-27", "2022-09-28", "2022-09-29", "2022-09-30"
    )),
    amount = c(
      30293627, 29684302, 18354636, 35183606, 9582177, 14897203, 1069765, 43384861,
      39524015, 86802632, 6614005, 17761052, 8276755, 21152542, 16291322, 7772429,
      20393879, 5318004, 11859141, 23864014, 12376091, 279657, 3614375, 19932634,
      586553, 33552104, 33483075, 8039351, 6629124, 14459099, 161360799, 26694683,
      12940912, 32584637, 23363951, 23351179, 9331648, 5428734, 10499438, 7540658,
      6299517, 25319539, 8195559, 57542928, 31303483, 269582871, 18277812, 39795633,
      36711, 16797389, 20370978, 22465056, 43725288, 73089307, 36169962, 93165179,
      107295392, 69236616, 19283321, 42972503, 61393684, 329600009, 78949695,
      119115555
    ),
    day = ordered(
      c(
        "Fri", "Mon", "Tue", "Wed", "Thu", "Fri", "Wed", "Thu", "Fri", "Mon", "Tue",
        "Wed", "Thu", "Fri", "Mon", "Tue", "Wed", "Thu", "Fri", "Mon", "Tue", "Wed",
        "Thu", "Fri", "Mon", "Tue", "Wed", "Thu", "Fri", "Mon", "Tue", "Wed", "Thu",
        "Fri", "Mon", "Tue", "Wed", "Thu", "Fri", "Mon", "Tue", "Wed", "Thu", "Fri",
        "Mon", "Tue", "Wed", "Thu", "Fri", "Mon", "Tue", "Wed", "Thu", "Fri", "Mon",
        "Tue", "Wed", "Thu", "Fri", "Mon", "Tue", "Wed", "Thu", "Fri"
      ),
      levels = c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat")
    ),
  )

trans_df |>
  ggplot(aes(x = value_date, y = amount)) +
  geom_line() +
  scale_x_date(date_breaks = 'day', date_labels = '%Y-%m-%d, %a') +
  theme_minimal() +
  theme(
    panel.grid.minor.x = element_blank(),
    axis.text.x = element_text(angle = 90, size = rel(0.6), vjust = -0.01)
  )

^{Created on 2024-01-17 with reprex v2.0.2}

christophsax / seasonalbook.content

Real-world transaction data #17