create_strategies_returns.R

# Create the VAR-based equally-weighted portfolio returns
df_forecasts$var_stra_returns <- rowMeans((df_forecasts[,match(tickers,colnames(df_forecasts))] *
                                        df_forecasts[,match(ticker_var_forecasts,colnames(df_forecasts))]), 
                                      na.rm=TRUE)

# Set the NaN values of the strategy returns to zero
df_forecasts$var_stra_returns[is.na(df_forecasts$var_stra_returns)] = 0.0

# Create the strategy cumulative returns
df_forecasts$var_stra_cum_returns <- exp(cumsum(df_forecasts$var_stra_returns))

# Create the TVP-VAR-SV-based equally-weighted portfolio returns
df_forecasts$tvp_var_sv_stra_returns <- rowMeans((df_forecasts[,match(tickers,colnames(df_forecasts))] *
                                             df_forecasts[,match(ticker_tvp_var_forecasts,colnames(df_forecasts))]), 
                                          na.rm=TRUE)

# Set the NaN values of the TVP-VAR-SV strategy returns to zero
df_forecasts$tvp_var_sv_stra_returns[is.na(df_forecasts$tvp_var_sv_stra_returns)] = 0.0

# Create the TVP-VAR-SV strategy cumulative returns
df_forecasts$tvp_var_sv_stra_cum_returns <- exp(cumsum(df_forecasts$tvp_var_sv_stra_returns))

# Create the equally-weighted benchmark portfolio returns
df_forecasts$bnh_returns <- rowMeans(df_forecasts[,match(tickers,colnames(df_forecasts))])

# Create the equally-weighted benchmark portfolio cumulative returns
df_forecasts$bnh_cum_returns <- exp(cumsum(df_forecasts$bnh_returns))

# Convert the date column in date type
df_forecasts$date <- as.Date(df_forecasts$date)

# Create a 15-day moving average long signal
df_forecasts$ma_signal <- ifelse(df_forecasts$bnh_cum_returns>rollapply(df_forecasts$bnh_cum_returns,15,mean,fill=1,align='right'),1,0)

# Buy the assets only when the VAR predict a positive return and the 200-day moving average signal is long too
df_forecasts$stra_improved_returns <- c(0,df_forecasts$ma_signal[-length(df_forecasts$ma_signal)]*df_forecasts$tvp_var_sv_stra_returns[-1])

# Compute the improved strategy returns
df_forecasts$stra_improved_cum_returns <- exp(cumsum(df_forecasts$stra_improved_returns))