create_forecasts_dataframe.R

# Group the data dates by year and month
options(dplyr.summarise.inform = FALSE)
dates <- var_data %>%
  mutate(month = format(date, "%m"), year = format(date, "%Y")) %>%
  group_by(year, month)  %>% summarise(first_dates = first(date))

# Get the first date of Oct-2021
initial_date = subset(dates, (dates$year=='2019') & (dates$month=='01'))$first_dates

# Import the Excel file in case it exists
df_forecasts <- open_xlsx("df_results_tvp_var.xlsx")

# If data is a Null value
if (is.null(df_forecasts)) {
  # Subset the df2 dataframe to only our forecast data
  df_forecasts <- subset(var_data, var_data$date>=as.Date(initial_date))
  
  # Create names for each ticker's VAR forecasts
  ticker_var_forecasts <- lapply(tickers, paste0, "_var_signal")
  
  # Create names for each ticker's TVP-VAR-SV forecasts
  ticker_tvp_var_forecasts <- lapply(tickers, paste0, "_tvp_var_signal")
  
  # Create a new temp_df dataframe for the tickers' forecasts
  temp_df <- data.frame(matrix(ncol = 2*length(tickers), nrow = length(df_forecasts$date)))
  colnames(temp_df) <- c(ticker_var_forecasts,ticker_tvp_var_forecasts)
  
  # Join both dataframes in the df_forecasts dataframe
  df_forecasts <- cbind(df_forecasts,temp_df)
  
  df_forecasts$trade_done <- 0
  
  # Set the initial index number for the forecast data
  initial_iloc_to_forecast <- which(df_forecasts$date==as.Date(initial_date))

  # If data was obtained from the Excel file
} else {
  # Create names for each ticker's VAR forecasts
  ticker_var_forecasts <- lapply(tickers, paste0, "_var_signal")
  
  # Create names for each ticker's TVP-VAR-SV forecasts
  ticker_tvp_var_forecasts <- lapply(tickers, paste0, "_tvp_var_signal")
  
  # Set the initial index number for the forecast data
  initial_iloc_to_forecast <- which(df_forecasts$date == head(subset(df_forecasts, trade_done == 0) ,1)$date)
}

# Set the span to 6 years of data
span <- 1500