Execution Prices

	#*****************************************************************
	# Load historical data
	#****************************************************************** 
	library(SIT)
	load.packages('quantmod')	
	tickers = spl('SPY,XLY,XLP,XLE,XLF,XLV,XLI,XLB,XLK,XLU')	
	
	data <- new.env()
	getSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)
		for(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)		
	bt.prep(data, align='remove.na')

	#*****************************************************************
	# Code Strategies
	#****************************************************************** 
	prices = data$prices  
	n = len(tickers)  

	# find month ends
	month.ends = endpoints(prices, 'months')
		month.ends = month.ends[month.ends > 0]		

	models = list()
					
	#*****************************************************************
	# Code Strategies
	#****************************************************************** 
	dates = '2001::'
					
	# Rank on 6 month return
	position.score = prices / mlag(prices, 126)	
	
	frequency = month.ends
	# Select Top 4 funds
	weight = ntop(position.score, 4)
	
	#*****************************************************************
	# Code Strategies, please note that there is only one price per day
	# so all transactions happen at selected price
	# i.e. below both buys and sells take place at selected price
	#****************************************************************** 
	for(name in spl('Cl,Op,Hi,Lo')) {
		fun = match.fun(name)
	
		exec.prices = bt.apply(data, fun)
		
		data$weight[] = NA
			data$execution.price[] = NA
		  	data$execution.price[frequency,] = exec.prices[frequency,]
		  	data$weight[frequency,] = weight[frequency,]
		models[[name]] = bt.run.share(data, trade.summary=T, dates=dates, silent=T, clean.signal=F)
	}	

	#*****************************************************************
	# Code Strategies
	#****************************************************************** 
	low.prices = bt.apply(data, Lo)
	high.prices = bt.apply(data, Hi)
	
	# buy at low price
	execution.price = low.prices[frequency,]
	
	# sell(i.e. weight=0) at high price
	index = (weight[frequency,])==0
	execution.price[index] = coredata(high.prices[frequency,])[index]
	
	data$weight[] = NA
		data$execution.price[] = NA		
	  	data$execution.price[frequency,] = execution.price	  		  	
	  	data$weight[frequency,] = weight[frequency,]
	models$Buy.Low.Sell.High = bt.run.share(data, trade.summary=T, dates=dates, silent=T, clean.signal=F)

	#*****************************************************************
	# Code Strategies
	#****************************************************************** 
	low.prices = bt.apply(data, Lo)
	high.prices = bt.apply(data, Hi)
	
	# buy at high price
	execution.price = high.prices[frequency,]
	
	# sell(i.e. weight=0) at low price
	index = (weight[frequency,])==0
	execution.price[index] = coredata(low.prices[frequency,])[index]
	
	data$weight[] = NA
		data$execution.price[] = NA		
	  	data$execution.price[frequency,] = execution.price	  		  	
	  	data$weight[frequency,] = weight[frequency,]
	models$Buy.High.Sell.Low = bt.run.share(data, trade.summary=T, dates=dates, silent=T, clean.signal=F)

	#*****************************************************************
	# 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))

bt.execution.price.high.low.test('SPY,EEM,EFA,TLT,IWM,QQQ,GLD', n.top=3)