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

#Gold Futures
df_gold <- 
  tq_get("GC=F") %>% 
  select(date, close) %>% 
  drop_na()


#Make a Cross-Validation Training Plan
resamples_tscv <- 
  time_series_cv(
  data        = df_gold,
  assess      = "6 months",
  initial     = "5 years",
  skip        = "1 years",
  slice_limit = 4
)

#Begin with a Cross Validation Strategy
resamples_tscv %>%
  tk_time_series_cv_plan() %>%
  plot_time_series_cv_plan(date,
                           close, 
                           .facet_ncol = 2, 
                           .interactive = FALSE)


#Model 1: auto_arima
model_arima <- 
  arima_reg() %>%
  set_engine(engine = "auto_arima") %>% 
  fit(close ~ date, data = df_gold)

#Model 2: prophet
model_prophet <- 
  prophet_reg() %>%
  set_engine(engine = "prophet") %>% 
  fit(close ~ date, data = df_gold)

#Model 3: glmnet
model_glmnet <- 
  linear_reg(penalty = 0.2) %>%
  set_engine("glmnet")

rec_glmnet <- 
  recipe(close ~ ., data = df_gold) %>% 
  step_mutate(date_num = as.numeric(date)) %>% 
  step_date(date, features = "month") %>% 
  step_rm(date) %>% 
  step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% 
  step_normalize(all_numeric_predictors()) 

glmnet_fit <- 
  workflow() %>% 
  add_recipe(rec_glmnet) %>% 
  add_model(model_glmnet) %>% 
  fit(df_gold)
  
#Modeltime Table
gold_models <- 
  modeltime_table(
    model_arima,
    model_prophet,
    glmnet_fit
  )

#Generate Resample Predictions
resamples_fitted <- 
  gold_models %>%
  modeltime_fit_resamples(
    resamples = resamples_tscv,
    control   = control_resamples(verbose = FALSE)
  )

#Accuracy Table
resamples_fitted %>%
  modeltime_resample_accuracy(summary_fns = mean) %>%
  table_modeltime_accuracy(.interactive = FALSE)


#Calibration for the Prophet Model 
calibration_prophet <- 
  model_prophet %>% 
  modeltime_calibrate(new_data = df_gold)

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


#Forecast Forward
calibration_prophet %>% 
  modeltime_forecast(h = "6 months", 
                     actual_data = df_gold %>% 
                                   filter(date>= as.Date("2025-01-01"))) %>%
  plot_modeltime_forecast(.interactive = FALSE,
                          .legend_show = FALSE,
                          .line_size = 1.5,
                          .color_lab = "",
                          .title = "Gold Futures") +
  
  labs(subtitle = "<span style = 'color:dimgrey;'>Predictive Intervals</span><br><span style = 'color:red;'>ML Model</span>") + 
  scale_x_date(expand = expansion(mult = c(.1, .1)),
               labels = scales::label_date(format = "%b'%y")) +
  scale_y_continuous(labels = scales::label_currency()) +
  theme_minimal(base_family = "Roboto Slab", base_size = 20) +
  theme(legend.position = "none",
        plot.background = element_rect(fill = "azure", 
                                       color = "azure"),
        plot.title = element_text(face = "bold"),
        axis.text = element_text(face = "bold"),
        plot.subtitle = ggtext::element_markdown(face = "bold"))