Adjusted Data

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


tickers = '
AGG # iShares Barclays Aggregate Bond Fund
DBC # PowerShares DB Com Indx Trckng Fund
EEM # iShares MSCI Emerging Markets Indx
EFA # iShares MSCI EAFE Index Fund
GLD # SPDR Gold Trust
IYR # iShares Dow Jones US Real Estate
JNK # SPDR Barclays Capital High Yield Bond
PPH # Market Vectors Pharmaceutical
SPY # SPDR S&P 500 Trust
TIP # iShares Barclays TIPS Bond Fund
'

tickers = '
EEM # iShares MSCI Emerging Markets Index ETF
EFA # iShares MSCI EAFE Index ETF
FXI # iShares China Large#Cap ETF
IEF # iShares 7#10 Year Treasury Bond ETF
IYR # iShares Dow Jones US Real Estate ETF
SHY # iShares 1#3 Year Treasury Bond ETF
SPY # SPDR S&P 500 Trust ETF
TIP # iShares Barclays TIPS Bond ETF
XLV # Health Care Select Sector SPDR ETF
UUP # PowerShares DB US Dollar Bullish ETF
'

data = new.env()
getSymbols.extra(tickers, src = 'yahoo', from = '1970-01-01', env = data, set.symbolnames = T, auto.assign = T)
#print(bt.start.dates(data))
#*****************************************************************
# Contruct another back-test environment with split-adjusted prices, do not include dividends
# http://www.fintools.com/wp-content/uploads/2012/02/DividendAdjustedStockPrices.pdf
# http://www.pstat.ucsb.edu/research/papers/momentum.pdf
#
# For example of split and dividend schedule
# http://finance.yahoo.com/q/hp?s=EFA&g=v
#*****************************************************************
data.price = new.env()
data.price$symbolnames = data$symbolnames
  for(i in data$symbolnames) data.price[[i]] = adjustOHLC(data[[i]], symbol.name=i, adjust='split', use.Adjusted=F)
bt.prep(data.price, align='keep.all', dates='2002::', fill.gaps=T)

#*****************************************************************
# Adjust prices
#*****************************************************************
  for(i in data$symbolnames) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)
bt.prep(data, align='keep.all', dates='2002::', fill.gaps=T)

#*****************************************************************
# Setup
#*****************************************************************
prices = data$prices
  n = ncol(prices)
  nperiods = nrow(prices)

frequency = 'months'
period.ends = endpoints(prices, frequency)
  period.ends1 = period.ends - 1 # lag by one day
  period.ends1 = period.ends1[period.ends1 > 0]
  period.ends0 = period.ends1 + 1

models = list()

commission = list(cps = 0.01, fixed = 10.0, percentage = 0.0)


#*****************************************************************
# 60 day rate of change for ranking
#******************************************************************
return = prices / mlag(prices,60) - 1
position.score = iif(return < 0, NA, return)

data$weight[] = NA
  data$weight[period.ends0,] = ntop(position.score[period.ends1,], 1)
models$mom60 = bt.run.share(data, clean.signal=F, commission = commission, trade.summary=T, silent=T)

# alternatively use do.lag
#data$weight[] = NA
#  data$weight[period.ends1,] = ntop(position.score[period.ends1,], 1)
#models$mom60A = bt.run.share(data, clean.signal=F, do.lag=2, commission = commission, trade.summary=T, silent=T)

#print(last.trades(models$mom60, make.plot=F, return.table=T))
#print(last.trades(models$mom60A, make.plot=F, return.table=T))

#*****************************************************************
# 60/120 day rate of change for ranking
#******************************************************************
return = prices / mlag(prices,60) - 1 + prices / mlag(prices,120) - 1
position.score = iif(return < 0, NA, return)

data$weight[] = NA
  data$weight[period.ends0,] = ntop(position.score[period.ends1,], 1)
models$mom120 = bt.run.share(data, clean.signal=F, commission = commission, trade.summary=T, silent=T)

#*****************************************************************
# Create Report
#*****************************************************************
#strategy.performance.snapshoot(models, T)
plotbt(models, plotX = T, log = 'y', LeftMargin = 3, main = NULL)
	mtext('Cumulative Performance', side = 2, line = 1)

print(plotbt.strategy.sidebyside(models, make.plot=F, return.table=T, perfromance.fn=engineering.returns.kpi))

print(last.trades(models$mom60, make.plot=F, return.table=T))

#*****************************************************************
# Helper function
#******************************************************************
run.strategy = function(prices , data, name) {
  models = list()
    
  return = prices / mlag(prices,60) - 1
  position.score = iif(return < 0, NA, return)

  data$weight[] = NA
    data$weight[period.ends0,] = ntop(position.score[period.ends1,], 1)
  models[[paste0('mom60',name)]] = bt.run.share(data, clean.signal=F, commission = commission, trade.summary=T, silent=T)


  return = prices / mlag(prices,60) - 1 + prices / mlag(prices,120) - 1
  position.score = iif(return < 0, NA, return)

  data$weight[] = NA
    data$weight[period.ends0,] = ntop(position.score[period.ends1,], 1)
  models[[paste0('mom120',name)]] = bt.run.share(data, clean.signal=F, commission = commission, trade.summary=T, silent=T)
  
  models
}

#*****************************************************************
# Setup
#******************************************************************
prices.adj = data$prices
prices.split = data.price$prices

all.models = list()
all.models = c(all.models, run.strategy(prices.adj, data, 'Adjusted'))
all.models = c(all.models, run.strategy(prices.split, data.price, 'Actual'))
all.models = c(all.models, run.strategy(prices.split, data, 'Hybrid'))

#*****************************************************************
# Create Report
#*****************************************************************
models = all.models[grep('mom60', names(all.models))]

plotbt(models, plotX = T, log = 'y', LeftMargin = 3, main = NULL)
	mtext('Cumulative Performance', side = 2, line = 1)

print(plotbt.strategy.sidebyside(models, make.plot=F, return.table=T, perfromance.fn=engineering.returns.kpi))

models = all.models[grep('mom120', names(all.models))]

plotbt(models, plotX = T, log = 'y', LeftMargin = 3, main = NULL)
	mtext('Cumulative Performance', side = 2, line = 1)

print(plotbt.strategy.sidebyside(models, make.plot=F, return.table=T, perfromance.fn=engineering.returns.kpi))