library(tidyverse)
library(tidymodels)
library(tidyquant)
library(timetk)
library(modeltime)

#Silver Futures
df_silver <- 
  tq_get("SI=F") %>% 
  select(date, close) %>% 
  filter(date >= last(date) - months(36)) %>% 
  drop_na()


#Splitting the data
df_split <- 
  df_silver %>% 
  time_series_split(assess = "30 days", 
                    cumulative = TRUE)

df_train <- 
  training(df_split)

df_test <- 
  testing(df_split)

# Turn the normal mean function into a rolling mean with a 5 row .period
mean_roll_5 <- slidify(mean, .period = 5, .align = "right")

#Preprocessing
rec_spec <- 
  recipe(close ~ ., data = df_train) %>% 
  step_timeseries_signature(date) %>% 
  step_mutate(slid_close = mean_roll_5(close)) %>% 
  step_impute_bag(slid_close) %>% 
  step_fourier(date, period = 365, K = 5) %>%
  step_rm(date) %>%
  step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% 
  step_zv(all_predictors()) %>% 
  step_normalize(all_numeric_predictors())


#Model Specification
mod_spec <- 
  linear_reg() %>% 
  set_engine("lm")


#Training
wflow_fit <- 
  workflow() %>% 
  add_recipe(rec_spec) %>% 
  add_model(mod_spec) %>% 
  fit(df_train)

#Modeltime
df_modeltime <- 
  modeltime_table(wflow_fit)

#Calibrate the model to the testing set
calibration_tbl <- 
  df_modeltime %>%
  modeltime_calibrate(new_data = df_test)


#Accuracy of the finalized model
calibration_tbl %>%
  modeltime_accuracy(metric_set = metric_set(rmse, rsq, mape))

  
#Prediction Intervals
calibration_tbl %>%
  modeltime_forecast(
    new_data    = df_test,
    actual_data = df_test
  ) %>% 
  plot_modeltime_forecast(
    .interactive = FALSE,
    .line_size = 1.5
  )  +
  labs(title = "Silver Futures", 
       subtitle = "<span style = 'color:dimgrey;'>Predictive Intervals</span> of <span style = 'color:red;'>ML Model</span> Model", 
       y = "", x = "") + 
  scale_y_continuous(labels = scales::label_currency()) +
  scale_x_date(labels = scales::label_date("%b %d"),
               date_breaks = "4 days") +
  theme_minimal(base_family = "Roboto Slab", base_size = 16) +
  theme(plot.subtitle = ggtext::element_markdown(face = "bold"),
        plot.title = element_text(face = "bold"),
        plot.background = element_rect(fill = "azure", color = "azure"),
        panel.background = element_rect(fill = "snow", color = "snow"),
        axis.text = element_text(face = "bold"),
        axis.text.x = element_text(angle = 45, 
                                   hjust = 1, 
                                   vjust = 1),
        legend.position = "none")