#' Compute a Rolling Value by Period
#'
#' @description `r lifecycle::badge('experimental')`
#'
#' Applies an arbitrary summary function over rolling time-period windows.
#' Each window spans `periods` units of `period` (e.g., 12 months). Before
#' calling `.f`, rows with any missing values are dropped from the window;
#' if fewer than `min_obs` rows remain, the result is `NA_real_` instead.
#'
#' @param data A data frame with a date column of class `Date`, named according
#'   to `data_options$date` (default `"date"`).
#' @param .f A function applied to each window. Receives a data-frame slice
#'   (complete cases only) and must return a single scalar value.
#' @param period A string specifying the period for rolling windows
#'   (e.g., `"month"`, `"quarter"`, `"year"`).
#' @param periods Number of periods to include in the rolling window.
#' @param min_obs Minimum number of non-missing rows required per window.
#'   Defaults to `periods`.
#' @param data_options A list of class `tidyfinance_data_options` (created via
#'   [data_options()]) specifying column name mappings. The `date` element is
#'   used to specify the date column. Uses [data_options()] default if `NULL`:
#'   `"date" = "date"`.
#'
#' @returns A numeric vector aligned with the rows of `data`.
#'
#' @family rolling and lagging functions
#' @export
#'
#' @examples
#' library(dplyr)
#'
#' # Rolling standard deviation
#' set.seed(42)
#' df <- tibble(
#'   date = seq.Date(
#'     from = as.Date("2020-01-01"),
#'     by = "month",
#'     length.out = 24
#'   ),
#'   value = rnorm(24)
#' )
#'
#' df |>
#'   mutate(
#'     rolling_sd = compute_rolling_value(
#'       pick(everything()),
#'       .f = ~ sd(.x$value, na.rm = TRUE),
#'       period = "month",
#'       periods = 4,
#'       min_obs = 2
#'     )
#'   )
#'
#' # Rolling last residual from a regression
#' set.seed(42)
#' df_reg <- tibble(
#'   date = seq.Date(
#'     from = as.Date("2020-01-01"),
#'     by = "month",
#'     length.out = 60
#'   ),
#'   ret_excess = rnorm(60, 0, 0.05),
#'   mkt_excess = rnorm(60, 0, 0.04),
#'   smb = rnorm(60, 0, 0.03),
#'   hml = rnorm(60, 0, 0.03)
#' )
#'
#' df_reg |>
#'   mutate(
#'     residual = compute_rolling_value(
#'       pick(everything()),
#'       .f = \(x) {
#'         last(lm(ret_excess ~ mkt_excess + smb + hml, data = x)$residuals)
#'       },
#'       period = "month",
#'       periods = 24,
#'       min_obs = 12
#'     )
#'   )
#'
#' # Rolling cumulative-return-to-SD ratio
#' set.seed(42)
#' df_resid <- tibble(
#'   date = seq.Date(
#'     from = as.Date("2020-01-01"),
#'     by = "month",
#'     length.out = 24
#'   ),
#'   int_roll_residual = rnorm(24, 0, 0.02)
#' )
#'
#' df_resid |>
#'   mutate(
#'     return_to_sd = compute_rolling_value(
#'       pick(everything()),
#'       .f = ~ (prod(1 + .x$int_roll_residual) - 1) / sd(.x$int_roll_residual),
#'       period = "month",
#'       periods = 12,
#'       min_obs = 12
#'     )
#'   )
#'
compute_rolling_value <- function(
  data,
  .f,
  period = "month",
  periods = 12,
  min_obs = periods,
  data_options = NULL
) {
  if (is.null(data_options)) {
    data_options <- data_options()
  }
  date_col <- data_options$date

  if (!is.character(date_col) || length(date_col) != 1 || is.na(date_col)) {
    cli::cli_abort(
      paste(
        "{.field date} in {.arg data_options} must be a single non-missing",
        "string, not {.obj_type_friendly {date_col}}."
      )
    )
  }

  if (!date_col %in% names(data)) {
    cli::cli_abort("{.arg data} must contain a {.field {date_col}} column.")
  }
  if (!inherits(data[[date_col]], "Date")) {
    cli::cli_abort(
      paste(
        "The {.field {date_col}} column must be of class {.cls Date},",
        "not {.cls {class(data[[date_col]])}}."
      )
    )
  }
  if (!is.character(period) || length(period) != 1) {
    cli::cli_abort(
      paste(
        "{.arg period} must be a single string,",
        "not {.obj_type_friendly {period}}."
      )
    )
  }

  .f <- rlang::as_function(.f)

  slider::slide_period_vec(
    .x = data,
    .i = data[[date_col]],
    .period = period,
    .f = function(.x) {
      complete <- tidyr::drop_na(.x)
      if (nrow(complete) < min_obs) {
        NA_real_
      } else {
        .f(complete)
      }
    },
    .before = periods - 1,
    .complete = FALSE
  )
}
