# Required packages
install.packages('dplyr')
install.packages('ramify')
install.packages('tibble')
install.packages('tidyr')
install.packages('purrr')
install.packages('slider')

library('dplyr', verbose = FALSE, warn.conflicts = FALSE)
library('ramify', verbose = FALSE, warn.conflicts = FALSE)
library('tibble', verbose = FALSE, warn.conflicts = FALSE)
library('tidyr', verbose = FALSE, warn.conflicts = FALSE)
library('purrr', verbose = FALSE, warn.conflicts = FALSE)
library('slider', verbose = FALSE, warn.conflicts = FALSE)

csv <- 'https://github.com/Brent-Morrison/Misc_scripts/raw/master/daily_price_ts_vw_20201018.csv'
daily_price_ts_vw_20201018 <- read.csv(csv)

tail(daily_price_ts_vw_20201018)

head(daily_price_ts_vw_20201018)

mtrx <- mat('1,2,3,4; 2,1,5,6; 3,5,1,7; 4,6,7,1')

# View as a tibble for nice formatting
as_tibble(mtrx)

mtrx_triu <- triu(mtrx, diag = FALSE)
as_tibble(mtrx_triu)

mean(mtrx_triu[mtrx_triu != 0], na.rm= TRUE)

mean(triu(mtrx, diag = FALSE)[triu(mtrx, diag = FALSE) != 0], na.rm= TRUE)

mean(mtrx[upper.tri(mtrx)])

mean_mtrx <- function(x) {
   mean(x[upper.tri(x)])
}

mean_mtrx(mtrx)

daily_price_ts_vw_20201018 %>% 
  group_by(symbol) %>%
  mutate(rtn_log_1d = log(adjusted_close) - lag(log(adjusted_close))) %>%
  slice(2:n()) %>%  # remove first row for each group, 'rtn_log_1d' will be NA
  ungroup() %>%
  select(date_stamp, symbol, rtn_log_1d) %>%
  pivot_wider(names_from = symbol, values_from = rtn_log_1d) %>% 
  select(-date_stamp) %>% 
  cor()

daily_price_ts_vw_20201018 %>% 
  group_by(symbol) %>%
  mutate(rtn_log_1d = log(adjusted_close) - lag(log(adjusted_close))) %>%
  slice(2:n()) %>%  # remove first row for each group, 'rtn_log_1d' will be NA
  ungroup() %>%
  select(date_stamp, symbol, rtn_log_1d) %>%
  pivot_wider(names_from = symbol, values_from = rtn_log_1d) %>% 
  select(-date_stamp) %>% 
  cor(use = 'pairwise.complete.obs')

daily_price_ts_vw_20201018 %>% 
  group_by(symbol) %>%
  mutate(rtn_log_1d = log(adjusted_close) - lag(log(adjusted_close))) %>%
  slice(2:n()) %>%  # remove first row for each group, 'rtn_log_1d' will be NA
  ungroup() %>%
  select(date_stamp, symbol, rtn_log_1d) %>%
  pivot_wider(names_from = symbol, values_from = rtn_log_1d) %>% 
  select(-date_stamp) %>% 
  cor(use = 'pairwise.complete.obs') %>%
  mean_mtrx()

ipc <- function(df) {
  
  max_date = max(df$date_stamp)
  
  ipc = df %>%
    select(date_stamp, symbol, rtn_log_1d) %>%
    pivot_wider(names_from = symbol, values_from = rtn_log_1d) %>% 
    select(-date_stamp) %>% 
    cor(use = 'pairwise.complete.obs') %>%
    mean_mtrx()

  return(tibble(date_stamp = max_date, ipc = ipc))
}

daily_price_rtn <- daily_price_ts_vw_20201018 %>% 
  mutate(
    date_stamp = as.Date(date_stamp),
    rtn_log_1d = log(adjusted_close) - lag(log(adjusted_close))
    ) %>%
  slice(2:n()) %>%
  arrange(date_stamp)

head(daily_price_rtn)

ipc(daily_price_rtn)

daily_price_rtn %>% 
  filter(date_stamp >= '2020-02-11' & date_stamp <= '2020-07-31') %>%
  split(.$sector) %>%
  map_dfr(., ipc, .id = 'sector')

daily_price_rtn %>% 
  filter(date_stamp >= '2020-02-11' & date_stamp <= '2020-07-31') %>%
  group_by(sector) %>%
  group_modify(~ ipc(.x))

daily_price_rtn %>% 
  filter(sector == 2) %>% 
  filter(date_stamp >= '2020-02-11' & date_stamp <= '2020-07-31') %>%
  select(date_stamp, symbol, rtn_log_1d) %>%
  pivot_wider(names_from = symbol, values_from = rtn_log_1d) %>% 
  select(-date_stamp) %>% 
  cor(use = 'pairwise.complete.obs') %>%
  mean_mtrx()

ipc_by_grp <- function(df) { 
  df %>% 
    split(.$sector) %>%
    map_dfr(., ipc, .id = 'sector')
}

daily_price_rtn %>% 
  filter(date_stamp >= '2020-02-11' & date_stamp <= '2020-07-31') %>%
  ipc_by_grp()

slide_period_dfr(
  .x = daily_price_rtn,
  .i = daily_price_rtn$date_stamp,
  .period = "month",
  .f = ipc_by_grp,
  .before = 5,
  .complete = TRUE
)

daily_price_rtn %>% 
  filter(date_stamp >= '2020-02-01' & date_stamp <= '2020-07-31') %>%
  ipc_by_grp()