Compute a Rolling Value by Period

Applies an arbitrary summary function over rolling time-period windows. Each window spans periods units of period (e.g., 12 months). All rows that fall into the same period receive the value of that period's window. 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.

Usage

compute_rolling_value(
  data,
  .f,
  period = "month",
  periods = 12,
  min_obs = periods,
  data_options = NULL
)

Arguments

data: A data frame with a date column of class Date, named according to data_options$date (default "date").
.f: A function applied to each window. Receives a data-frame slice (complete cases only) and must return a single scalar value.
period: A string specifying the period for rolling windows (e.g., "month", "quarter", "year").
periods: Number of periods to include in the rolling window.
min_obs: Minimum number of non-missing rows required per window. Defaults to periods.
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".

Value

A numeric vector aligned with the rows of data.

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

# 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
    )
  )
#> # A tibble: 24 × 3
#>    date         value rolling_sd
#>    <date>       <dbl>      <dbl>
#>  1 2020-01-01  1.37       NA    
#>  2 2020-02-01 -0.565       1.37 
#>  3 2020-03-01  0.363       0.968
#>  4 2020-04-01  0.633       0.800
#>  5 2020-05-01  0.404       0.529
#>  6 2020-06-01 -0.106       0.310
#>  7 2020-07-01  1.51        0.675
#>  8 2020-08-01 -0.0947      0.760
#>  9 2020-09-01  2.02        1.10 
#> 10 2020-10-01 -0.0627      1.08 
#> # ℹ 14 more rows

# 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
    )
  )
#> # A tibble: 60 × 6
#>    date       ret_excess mkt_excess        smb      hml residual
#>    <date>          <dbl>      <dbl>      <dbl>    <dbl>    <dbl>
#>  1 2020-01-01    0.0685    -0.0147  -0.0448     0.00454       NA
#>  2 2020-02-01   -0.0282     0.00741 -0.0441    -0.0175        NA
#>  3 2020-03-01    0.0182     0.0233   0.00374    0.0111        NA
#>  4 2020-04-01    0.0316     0.0560  -0.0299     0.00884       NA
#>  5 2020-05-01    0.0202    -0.0291  -0.0000547 -0.00838       NA
#>  6 2020-06-01   -0.00531    0.0521  -0.0128    -0.0401        NA
#>  7 2020-07-01    0.0756     0.0134  -0.0184     0.0210        NA
#>  8 2020-08-01   -0.00473    0.0415  -0.0607     0.0166        NA
#>  9 2020-09-01    0.101      0.0368  -0.0367    -0.0251        NA
#> 10 2020-10-01   -0.00314    0.0288   0.00539   -0.0478        NA
#> # ℹ 50 more rows

# 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
    )
  )
#> # A tibble: 24 × 3
#>    date       int_roll_residual return_to_sd
#>    <date>                 <dbl>        <dbl>
#>  1 2020-01-01           0.0274            NA
#>  2 2020-02-01          -0.0113            NA
#>  3 2020-03-01           0.00726           NA
#>  4 2020-04-01           0.0127            NA
#>  5 2020-05-01           0.00809           NA
#>  6 2020-06-01          -0.00212           NA
#>  7 2020-07-01           0.0302            NA
#>  8 2020-08-01          -0.00189           NA
#>  9 2020-09-01           0.0404            NA
#> 10 2020-10-01          -0.00125           NA
#> # ℹ 14 more rows

Usage

Arguments

Value

See also

Examples