# install.packages("devtools") devtools::install_github("rubetron/AssetAllocation")

library(AssetAllocation) #> Registered S3 method overwritten by 'quantmod': #> method from #> as.zoo.data.frame zoo ## Example 1: backtesting one of the asset allocations in the package us_60_40 <- asset_allocations$static$us_60_40 # test using the data set provided in the package bt_us_60_40 <- backtest_allocation(us_60_40, ETFs$Prices, ETFs$Returns, ETFs$risk_free) # plot returns library(PerformanceAnalytics) #> Loading required package: xts #> Loading required package: zoo #>  #> Attaching package: 'zoo' #> The following objects are masked from 'package:base': #>  #> as.Date, as.Date.numeric #>  #> Attaching package: 'PerformanceAnalytics' #> The following object is masked from 'package:graphics': #>  #> legend chart.CumReturns(bt_us_60_40$returns, main = paste0("Backtest of the ", bt_us_60_40$strat$name, " portfolio"), ylab = "Cumulative returns" )

# show table with performance metrics bt_us_60_40$table_performance #> United.States.60.40 #> Annualized Return 0.0803 #> Annualized Std Dev 0.1013 #> Annualized Sharpe (Rf=1.18%) 0.6669 #> daily downside risk 0.0045 #> Annualised downside risk 0.0718 #> Downside potential 0.0019 #> Omega 1.1466 #> Sortino ratio 0.0618 #> Upside potential 0.0022 #> Upside potential ratio 0.6175 #> Omega-sharpe ratio 0.1466 #> Semi Deviation 0.0046 #> Gain Deviation 0.0046 #> Loss Deviation 0.0053 #> Downside Deviation (MAR=210%) 0.0100 #> Downside Deviation (Rf=1.18%) 0.0045 #> Downside Deviation (0%) 0.0045 #> Maximum Drawdown 0.3260 #> Historical VaR (95%) -0.0093 #> Historical ES (95%) -0.0155 #> Modified VaR (95%) -0.0086 #> Modified ES (95%) -0.0086

library(AssetAllocation) # create a strategy that invests equally in momentum (MTUM), value (VLUE), low volatility (USMV) and quality (QUAL) ETFs. factors_EW <- list(name = "EW Factors", tickers = c("MTUM", "VLUE", "USMV", "QUAL"), default_weights = c(0.25, 0.25, 0.25, 0.25), rebalance_frequency = "month", portfolio_rule_fn = "constant_weights") # get data for tickers using getSymbols factor_ETFs_data <- get_data_from_tickers(factors_EW$tickers, starting_date = "2013-08-01") #> 'getSymbols' currently uses auto.assign=TRUE by default, but will #> use auto.assign=FALSE in 0.5-0. You will still be able to use #> 'loadSymbols' to automatically load data. getOption("getSymbols.env") #> and getOption("getSymbols.auto.assign") will still be checked for #> alternate defaults. #>  #> This message is shown once per session and may be disabled by setting  #> options("getSymbols.warning4.0"=FALSE). See ?getSymbols for details. # backtest the strategy bt_factors_EW <- backtest_allocation(factors_EW,factor_ETFs_data$P, factor_ETFs_data$R) # plot returns charts.PerformanceSummary(bt_factors_EW$returns, main = bt_factors_EW$strat$name, )

# table with performance metrics bt_factors_EW$table_performance #> EW.Factors #> Annualized Return 0.1233 #> Annualized Std Dev 0.1731 #> Annualized Sharpe (Rf=0%) 0.7125 #> daily downside risk 0.0078 #> Annualised downside risk 0.1246 #> Downside potential 0.0032 #> Omega 1.1652 #> Sortino ratio 0.0664 #> Upside potential 0.0037 #> Upside potential ratio 0.5665 #> Omega-sharpe ratio 0.1652 #> Semi Deviation 0.0081 #> Gain Deviation 0.0077 #> Loss Deviation 0.0094 #> Downside Deviation (MAR=210%) 0.0125 #> Downside Deviation (Rf=0%) 0.0078 #> Downside Deviation (0%) 0.0078 #> Maximum Drawdown 0.3499 #> Historical VaR (95%) -0.0161 #> Historical ES (95%) -0.0266 #> Modified VaR (95%) -0.0151 #> Modified ES (95%) -0.0168