predict.bsts does not capture holiday information EDIT (Tue12Mar): The bug fixes are on their way. See https://github.com/steve-the-bayesian/BOOM/issues/25
I've been working with bsts to model time-series and make forecasts, but have run into some problems with the predict function and holidays.
When I train the model with regressor holidays these regressors do not appear to be used in the predict function. For instance in a simple local level model the holiday regressor causes the response variable to go down, but the predictor of the response over the same period does not capture this behaviour.
I attach below a close to minimum working example based on the regression.holiday code from the documentation. You will see the green line representing the mean/95%CI failing to capture the Memorial Day holiday 
EDIT (Mon4Mar18): Here's a .csv with the simulated data. Consists of 3 columns: Date, Value and Train (TRUE/FALSE depending whether in training set)
library(bsts)
library(tidyverse)
set.seed(12345)

trend <- cumsum(rnorm(1095, 0, .1))
  dates <- seq.Date(from = as.Date("2014-01-01"), length = length(trend), by = "day")
  y <- zoo(trend + rnorm(length(trend), 0, .2), dates)

AddHolidayEffect <- function(y, dates, effect) {
  ## Adds a holiday effect to simulated data.
  ## Args:
  ##   y: A zoo time series, with Dates for indices.
  ##   dates: The dates of the holidays.
  ##   effect: A vector of holiday effects of odd length.  The central effect is
  ##     the main holiday, with a symmetric influence window on either side.
  ## Returns:
  ##   y, with the holiday effects added.
  time <- dates - (length(effect) - 1) / 2
  for (i in 1:length(effect)) {
    y[time] <- y[time] + effect[i]
    time <- time + 1
  }
  return(y)
}

## Define some holidays.
memorial.day <- NamedHoliday("MemorialDay")
memorial.day.effect <- c(-.75, -2, -2)
memorial.day.dates <- as.Date(c("2014-05-26", "2015-05-25", "2016-05-25"))
y <- AddHolidayEffect(y, memorial.day.dates, memorial.day.effect)

## The holidays can be in any order.
holiday.list <- list(memorial.day)

## Let's train the model to just before MemorialDay
cut_date = as.Date("2016-05-20")
train_data <- y[time(y) < cut_date]
test_data <- y[time(y) >= cut_date]
ss <- AddLocalLevel(list(), train_data)
ss <- AddRegressionHoliday(ss, train_data, holiday.list = holiday.list)
model <- bsts(train_data, state.specification = ss, niter = 500, ping = 0)
## Now let's make a prediction covering MemorialDay
my_horizon = 15
## Note adding the time stamps here doesn't help either
pred <- predict(object = model, horizon = my_horizon)
## Make a data frame for plotting
plot_info <- data.frame(Date = time(y), 
                        value = y, 
                        predict_mean = NA,
                        predict_upper = NA,
                        predict_lower = NA
                       )
plot_info[plot_info$Date %in% time(test_data)[1:my_horizon],]$predict_mean = pred$mean
plot_info[plot_info$Date %in% time(test_data)[1:my_horizon],]$predict_lower = pred$interval[1,]
plot_info[plot_info$Date %in% time(test_data)[1:my_horizon],]$predict_upper = pred$interval[2,]
## Let's make a pretty plot to demonstrate the problem
filter(plot_info, Date > time(test_data)[1] - 25 & Date < time(test_data)[my_horizon] + 10)  %>% 
    ggplot(aes(x = Date, y = value)) +
    geom_line() +
    geom_line(aes(y = predict_mean), col = "Forest Green") + # The prediction
    geom_line(aes(y = predict_lower), col = "Forest Green", lty = 2) + # lower bound
    geom_line(aes(y = predict_upper), col = "Forest Green", lty = 2)  # upper bound

 A: I don't use bsts so i can't precisely tell what is the flaw be it simulation or analysis. Post your data in a csv file and I will try to help you. 
Take a look at https://autobox.com/capable.pdf (slide 49) for some pointers on daily data and in general https://stats.stackexchange.com/search?q=user%3A3382+daily+data
edited after receipt of data (1095 observations)
The plot of the data  suggests that the simulation did not totally work as a number of interventions around memorial day are visually obvious. This is perhaps due to the effect of the ARIMA structure not being properly introduced in the simulation 
AUTOBOX ( which I have helped to develop) automatically identified 1) the underlying arima model (1,1,0)(0,0,0)7 with coefficient = .413 AND 2) the following list of interventions with a curious suggestion of anomalies primarily around Memorial Day AND 3)A suggested model model of  and here  &  and 4) an estimated effect of -1.99 for Memorial Day. Note well that Holiday Effects are often over a period of days NOT JUST on the actual holiday. AUTOBOX identifies lead and lag effects if they exist ... not so in this case as only the actual holiday was effected as a result of the simulation.
Hope this helps you and the author of the software you are using. Your local level model is clearly inadequate . I didn;t see where you actually suggested the ARIMA model form when you performed the simulation but again I don't know bsts .
