Worldwide modeling with XGBOOST: Gold vs. Silver | R-Bloggers

China wants to increase its influence on the global bulliven market by leading friendly countries to save their gold reserves within its limits. This step is part of Beijing’s efforts to reduce the dependence on the dollar and to promote the global use of the Yuan.

Goldman Sachs predicts that if only 1% of corporate bonds move to gold, prices can rise to $ 5,000. According to the XGBOOST Model, both gold and silver near the upper tires, suggest that it is not a good time to buy at those levels.

Source code:

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

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

#Silver Futures (SI=F)
df_silver <- 
  tq_get("SI=F") %>% 
  select(date,  silver = close)


#Creating the survey data
df_survey <- 
  df_gold %>% 
  left_join(df_silver) %>% 
  pivot_longer(-date,
               names_to = "id",
               values_to = "value") %>% 
  mutate(id = toupper(id)) %>% 
  filter(date >= last(date) - months(36)) %>% 
  drop_na()

#Train/Test Splitting
splits <- 
  df_survey %>% 
  time_series_split(assess     = "15 days", 
                    cumulative = TRUE)


#Recipe

#The step_normalize() function is breaking the decision splits.
#Reducing the model's accuracy led to its removal.

rec_spec <- 
  recipe(value ~ ., training(splits)) %>%
  step_string2factor("id") %>% 
  step_mutate_at(id, fn = droplevels) %>%
  step_timeseries_signature(date) %>%
  step_rm(date) %>%
  step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% 
  step_zv(all_predictors()) %>%
  step_corr(all_predictors())


#Preprocessed data variables
rec_spec %>% 
  prep() %>% 
  bake(new_data = NULL) %>% 
  glimpse()
 

#Workflow fit
wflw_fit <- 
  workflow() %>%
  add_model(
    boost_tree("regression") %>% 
      set_engine("xgboost")
  ) %>%
  add_recipe(rec_spec) %>%
  fit(training(splits))

#Create a Modeltime Table
model_tbl <- 
  modeltime_table(wflw_fit)


#Calibrating by ID
calib_tbl <- 
  model_tbl %>%
  modeltime_calibrate(
    new_data = testing(splits), 
    id       = "id"
  )

#Measuring Test Accuracy

#Global Accuracy
calib_tbl %>% 
  modeltime_accuracy(acc_by_id = FALSE) %>% 
  table_modeltime_accuracy(.interactive = FALSE)

#Local Accuracy
calib_tbl %>% 
  modeltime_accuracy(acc_by_id = TRUE) %>% 
  table_modeltime_accuracy(.interactive = TRUE)


#Prediction intervals were used similarly to the Relative Strength Index (RSI).
calib_tbl %>%
  modeltime_forecast(
    new_data    = testing(splits),
    actual_data = testing(splits),
    conf_by_id  = TRUE) %>%
  group_by(id) %>%
  plot_modeltime_forecast(
    .facet_ncol  = 1,
    .interactive = FALSE,
    .line_size = 1.5
  )  +
  labs(title = "Global Modeling with XGBoost", 
       subtitle = "Predictive Intervals of XGBoost", 
       y = "", x = "") + 
  scale_y_continuous(labels = scales::label_currency()) +
  scale_x_date(labels = scales::label_date("%b %d"),
               date_breaks = "4 days") +
  theme_tq(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 = "snow"),
        strip.text = element_text(face = "bold", color = "black"),
        strip.background = element_rect(fill =  "azure"),
        axis.text= element_text(face = "bold"),
        legend.position = "none")

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