random.rebal.weights.06oct2015.R

# R code re: CapitalSpecator.com post on random rebalancing with weights:
# "Using Random Portfolios To Test Asset Allocation Strategies"
# http://www.capitalspectator.com/using-random-portfolios-to-test-asset-allocation-strategies/
# 06 Oct 2015
# By James Picerno
# http://www.capitalspectator.com/
# (c) 2015 by Beta Publishing LLC

# load packages
library(quantmod)
library(tseries)
library(PerformanceAnalytics)

# download fund prices
symbols <-c("SPY","IJS","EFA","EEM","IEF","LQD","VWEHX","RPIBX","PREMX","QRAAX","VGSIX")
getSymbols(symbols, src='yahoo', from='2003-12-31')

for(symbol in symbols) {
  x <- get(symbol)
  indexFormat(x) <- '%Y-%m-%d'
  colnames(x) <- gsub("x",symbol,colnames(x))
  x <- x[,6]
  assign(symbol,x)
}
  
# merge price histories into one data set   
port.global.prices <- do.call(merge, lapply(symbols, get))
colnames(port.global.prices) <-c(symbols)

# generate daily return series for funds
port.global.returns <-na.omit(ROC(port.global.prices,1,"discrete"))
colnames(port.global.returns) <-c("spy","ijs","efa","eem","ief","lqd","vwehx","rpibx","premx","qraax","vgsix")

# asset weights for benchmark strategy
w.global.benchmark = c(0.25,0.05,0.20,0.05,0.10,0.10,0.05,0.05,0.05,0.05,0.05) # global aa

# calculate benchmark strategy data
port.global.dec31rebal.benchmark <-Return.portfolio(port.global.returns,
                                       rebalance_on="years",
                                       weights=w.global.benchmark,wealth.index=TRUE,verbose=TRUE)

# function to generate random strategy portfolios
rand.aa <-function(ret,min,max) {
  w.global <-prop.table(runif(11,min,max)) # create random weights for assets
  port.global.dec31rebal <-Return.portfolio(ret,
                                            rebalance_on="years",
                                            weights=w.global,
                                            wealth.index=TRUE,verbose=TRUE)
  return(as.numeric(port.global.dec31rebal$wealthindex))
}

# generate random portfolio performance indexes based on random weights, ranging from 5% to 25%
# warning: processing time could be long, depending on your
# computer's capabilities. For a quick test,
# reduce the replications to, say, 10.
rand.aa.data <-replicate(1000,rand.aa(port.global.returns,0.05,0.25)) 
rand.aa.data.1 <-xts(rand.aa.data,index(port.global.returns))*100 # create xts file 

# benchmark portfolio performance index
port.global.dec31rebal.benchmark$wealthindex.1 <-port.global.dec31rebal.benchmark$wealthindex*100

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