Distribution of inter-peak days of the DJIA

DJI_inter_peak_days.R

library(tidyverse)
library(tidyquant)
source(
    "https://raw.githubusercontent.com/mkiang/disproportionate_prescribing/master/code/mk_nytimes.R"
)

getSymbols("^DJI")

DJI <- as_tibble(DJI) %>%
    mutate(
        date = index(DJI),
        year = lubridate::year(date),
        day_index = 1:n()
    )

DJI$peak_day <- 0
for (i in 1:NROW(DJI)) {
    if (i == 1) {
        current_peak <- as.numeric(DJI$DJI.Close[1])
        next()
    }
    
    if (as.numeric(DJI$DJI.Close[i]) > current_peak) {
        DJI$peak_day[i] <- 1
        current_peak <- as.numeric(DJI$DJI.Close[i])
    }
}

peak_days <- DJI %>%
    filter(peak_day == 1) %>%
    mutate(inter_peak_days = day_index - lag(day_index)) %>%
    mutate(inter_peak_trunc = ifelse(inter_peak_days > 20, 21, inter_peak_days))

p1 <- ggplot(peak_days,
             aes(x = inter_peak_trunc)) +
    geom_histogram(bins = 20) +
    scale_x_continuous(
        "Market days since last ^DJI 'record high'",
        expand = c(0, 0),
        breaks = seq(0, 20, 5),
        labels = c("0", "5", "10", "15", "20+"),
        limits = c(0, 20.5)
    ) +
    scale_y_continuous("Frequency",
                       expand = c(0, 0),
                       breaks = c(0, 20, 40)) +
    facet_wrap( ~ year) +
    mk_nytimes()

ggsave(
    "./dji_inter_peak_times.jpg",
    p1,
    width = 6,
    height = 5,
    dpi = 300,
    scale = 1
)

mean(peak_days$inter_peak_days, na.rm = TRUE)