Elastic Asset Allocation Strategy

#*****************************************************************
# Load historical data
#*****************************************************************
library(SIT)
load.packages('quantmod')

tickers = '
US.STOCKS = VTI + VTSMX
FOREIGN.STOCKS = VEU + FDIVX
EMERGING.MARKETS=EEM + VEIEX
US.10YR.GOV.BOND = IEF + VFITX
REAL.ESTATE = VNQ + VGSIX
COMMODITIES = DBC + CRB
CASH = BND + VBMFX
'

# load saved Proxies Raw Data, data.proxy.raw
load('data.proxy.raw.Rdata')

data <- new.env()

getSymbols.extra(tickers, src = 'yahoo', from = '1970-01-01', env = data, raw.data = data.proxy.raw, auto.assign = T, set.symbolnames = T, getSymbols.fn = getSymbols.fn, calendar=calendar)
  for(i in data$symbolnames) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)
bt.prep(data, align='remove.na', dates='::')

print(last(data$prices))

#*****************************************************************
# Setup
#*****************************************************************
data$universe = data$prices > 0
	# do not allocate to CASH
	data$universe$CASH = NA 

prices = data$prices * data$universe
	n = ncol(prices)

#*****************************************************************
# Code Strategy
#******************************************************************
ret = diff(log(prices))

n.top = 3

mom.lookback = 80
vol.lookback = 12*22
cor.lookback = 12*22

weight=list(r=1, v=0, c=0.5, s=2)

hist.vol = sqrt(252) * bt.apply.matrix(ret, runSD, n = vol.lookback)

#mean of cumulative returns of 1, 3, 6, and 12 month periods
mom = (prices / mlag(prices, 22)-1 + prices / mlag(prices, 3*22)-1
+ prices / mlag(prices, 6*22)-1 + prices / mlag(prices, 12 * 22)-1)/22

mkt.ret = rowMeans(ret, na.rm=T)

#*****************************************************************
# Compute Correlation to Market
#******************************************************************
mkt.cor = data$weight * NA
for(i in period.ends[period.ends > cor.lookback]){
  index = (i - cor.lookback):i
  hist = ret[index,]
    include.index = !is.na(colSums(hist))
    
  if(any(include.index))
  	mkt.cor[i,include.index] = cor(hist[,include.index], mkt.ret[index], use='complete.obs',method='pearson')
}

avg.rank = (mom^weight$r * (1 - mkt.cor)^weight$c / hist.vol^weight$v) ^ weight$s
meta.rank = br.rank(avg.rank[period.ends,])

#absolute momentum filter
weight = (meta.rank <= n.top)/rowSums(meta.rank <= n.top, na.rm=T) * (mom[period.ends,] > 0)

# otherwise, it's weight is allocated to cash
weight$CASH = 1 - rowSums(weight,na.rm=T)

obj$weights$strategy = weight