Model de ansamblu pentru futures de aur

Goldman Sachs a declarat că, dacă reputația Fed suferă, iar investitorii mută doar o mică parte din exploatațiile lor de obligațiuni în aur, prețul aurului ar putea crește la aproape 5.000 de dolari pe uncie.

Dar cel puțin acum, modelul de ansamblu indică faptul că prețurile aurului sunt într -o zonă supra -cumpărată.

Cod sursă:

library(tidymodels)
library(tidyverse)
library(modeltime)
library(modeltime.ensemble)
library(timetk)

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


#Splitting
splits <- 
  time_series_split(df_gold, 
                    assess = "30 days", 
                    cumulative = TRUE)

df_train <- training(splits)
df_test <- testing(splits)

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


#Model 1 - Auto ARIMA
mod_spec_arima <- 
  arima_reg() %>%
  set_engine("auto_arima")

wflw_fit_arima <- 
  workflow() %>%
  add_model(mod_spec_arima) %>%
  add_recipe(rec_spec %>% step_rm(all_predictors(), -date)) %>%
  fit(df_train)

#Model 2 - Prophet
mod_spec_prophet <- 
  prophet_reg() %>%
  set_engine("prophet")

wflw_fit_prophet <- 
  workflow() %>%
  add_model(mod_spec_prophet) %>%
  add_recipe(rec_spec) %>%
  fit(df_train)

#Model 3: Boosted ARIMA
mod_arima_boosted <- 
  arima_boost(
    min_n = 2,
    learn_rate = 0.015
  ) %>%
  set_engine(engine = "auto_arima_xgboost")

wflw_fit_arima_boost <- 
  workflow() %>%
  add_model(mod_arima_boosted) %>%
  add_recipe(rec_spec) %>%
  fit(df_train)

#Modeltime Workflow for Ensemble Forecasting
df_models <- 
  modeltime_table(
    wflw_fit_arima,
    wflw_fit_prophet,
    wflw_fit_arima_boost
  )


#Make an Ensemble
ensemble_fit <- 
  df_models %>%
  ensemble_average(type = "mean")

#Calibration
calibration_tbl <- 
  modeltime_table(
    ensemble_fit
  ) %>%
  modeltime_calibrate(df_test)


#Accuracy
calibration_tbl %>%
  modeltime_accuracy() %>%
  table_modeltime_accuracy(
    .interactive = TRUE
  )


#Predictive intervals
calibration_tbl %>%
  modeltime_forecast(actual_data = df_test,
                     new_data = df_test) %>%
  plot_modeltime_forecast(.interactive = FALSE,
                          .legend_show = FALSE,
                          .line_size = 1.5,
                          .color_lab = "",
                          .title = "Gold Futures") +
  labs(subtitle = "Predictive Intervals of the Ensemble Model") + 
  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")