tvp_var_sv_signal_creation_loop.R

print(paste0(strrep('=',50)))
print(paste0(strrep('=',50)))
print(paste0(strrep('=',50)))
print(paste0('Estimation of TVP-VAR-SV forecasts'))

if (length(initial_iloc_to_forecast<nrow(df_forecasts))!=0) {
  # The for loop to estimate the model each day
  for (i in initial_iloc_to_forecast:nrow(df_forecasts)) {
    
    # Set the current iteration date
    iter_date <- df_forecasts[i,'date']
    # Print the date
    print(paste0(strrep('=',50)))
    print(paste0("Date is ",iter_date))
    
    # Select the the 6 years of data to train the model
    data <- as.matrix(subset(var_data, date<iter_date)[,match(tickers,colnames(var_data))])
  
    bv <- bvar.sv.tvp(data, nf=1, tau=40, nrep = 300, nburn=20)
    forecast_ys <- rowMeans(bv$fc.ydraws[1:7,1,])
    print(paste0('forecast_ys'))
    print(paste0(forecast_ys))
    
    # We go long if the forecast return is positive, otherwise, we make no position.
    for (c in 1:length(tickers)) {
      df_forecasts[i,paste0(tickers[c],'_tvp_var_signal')] = if (forecast_ys[c]>0) 1 else next
    }
    
    # Set the trade_done value as 1 to record up to which day we have progressed
    df_forecasts[i,'trade_done'] <- 1
    
    # Print the signals
    for (ticker in tickers) {
      print(paste0(ticker," tvp-var-sv signal is ",df_forecasts[i,paste0(ticker,'_tvp_var_signal')]))
    }
    # Save the df_forecasts dataframe
    dataframe <- data.frame(coredata(df_forecasts))
    write.xlsx(dataframe, 'df_results_tvp_var.xlsx')
  }
} else {
  print(paste0('Estimation of TVP-VAR-SV forecasts has been completed before...'))
}