Created
August 28, 2024 20:26
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| print(paste0(strrep('=',50))) | |
| print(paste0(strrep('=',50))) | |
| print(paste0(strrep('=',50))) | |
| print(paste0('Estimation of basic-VAR forecasts')) | |
| # Check if VAR forecasts have already been estimated | |
| if (length(as.numeric(rownames(tail(subset(df_forecasts, trade_done == 1) ,1))))==0) { | |
| print(paste0(strrep('=',50))) | |
| print(paste0(strrep('=',50))) | |
| print(paste0(strrep('=',50))) | |
| print(paste0('Estimation of VAR 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 <- subset(var_data, var_data$date<iter_date)[,match(tickers,colnames(var_data))] | |
| # Estimate up to the 15-lag VAR with no constants or trends | |
| lagselect <- VARselect(data, lag.max = 15, type = "none") | |
| # Choose the best lag order as per the Bayesian Information Criteria | |
| lag_order <- lagselect$selection[3] | |
| # Estimate the best VAR model | |
| model <- VAR(data, p = lag_order, type = "none", season = NULL, exog = NULL) | |
| # Forecast one step ahead | |
| var_forecast <- predict(model, n.ahead = 1) | |
| # We go long if the forecast return is positive, otherwise, we make no position. | |
| for (ticker in tickers) { | |
| df_forecasts[i,paste0(ticker,'_var_signal')] = if (var_forecast$fcst[[ticker]][1]>=0) 1 else next | |
| } | |
| # Print the signals | |
| for (ticker in tickers) { | |
| print(paste0(ticker," var signal is ",df_forecasts[i,paste0(ticker,'_var_signal')])) | |
| } | |
| } | |
| } else { | |
| print(paste0('Estimation of TVP-VAR-SV forecasts has been completed before...')) | |
| } | |
| } |
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