Home > Backtesting, R, Strategy, Trading Strategies > Multi-Asset Backtest : Rotational Trading Strategies

Multi-Asset Backtest : Rotational Trading Strategies

I want to discuss the implementation of Rotational Trading Strategies using the backtesting library in the Systematic Investor Toolbox.The Rotational Trading strategy switches investment allocations throughout the time, betting on few top ranked assets. For example, the ranking can be based on relative strength or momentum. A few examples of the Rotational Trading Strategies (or Tactical Asset Allocation) are:

I want to illustrate the Rotational Trading using the strategy introduced at ETF Screen in the ETF Sector Strategy post. Each month, this strategy invests into the top two of the 21 ETFs sorted by their 6 month returns. To reduce the turnover, in subsequent months the ETF positions are kept as long as these ETFs are in the top 6 rank.

Before we can implement this strategy, we need to create two helper routines. First, let’s create a function that will select the top N positions for each period:

############################################################################### 
# Select top N for each period
############################################################################### 
ntop <- function
(
	data,		# matrix with observations
	topn = 1, 	# top n
	dirMaxMin = TRUE
) 
{
	out = data
	out[] = NA
	
	for( i in 1:nrow(data) ) {
		x = coredata(data[i,])
		o = sort.list(x, na.last = TRUE, decreasing = dirMaxMin)
		index = which(!is.na(x))
		x[] = NA
		
		if(len(index)>0) {
			n = min(topn, len(index))
			x[o[1:n]] = 1/n
		}
		out[i,] = x
	}
	out[is.na(out)] = 0	
	return( out )
}

Next, let’s create a function that will select the top N positions for each period and keep them until they drop below KeepN rank:

############################################################################### 
# Select top N for each period, and keep them till they drop below keepn rank
############################################################################### 
ntop.keep <- function
(
	data, 		# matrix with observations
	topn = 1, 	# top n
	keepn = 1, 	# keep n
	dirMaxMin = TRUE
) 
{
	out = data
	out[] = NA
	
	for( i in 1:nrow(data) ) {
		x = coredata(data[i,])
		o = sort.list(x, na.last = TRUE, decreasing = dirMaxMin)
		index = which(!is.na(x))
		x[] = NA
			
		if(len(index)>0) {
			n = min(topn, len(index))
			x[o[1:n]] = 1
		
			# keepn logic
			if( i>=2 ) {
				y = coredata(out[(i-1),])	# previous period selection
				n1 = min(keepn, len(index))
				y[-o[1:n1]] = NA	# remove all not in top keepn
				
				index1 = which(!is.na(y))
				if(len(index1)>0) {
					x[] = NA
					x[index1] = 1	# keep old selection
					
					for( j in 1:n ) {
						if( sum(x, na.rm = T) == topn ) break
						x[o[j]] = 1
					}
				}
			}
		}		
		out[i,] = x/sum(x, na.rm = T)	
	}
	out[is.na(out)] = 0	
	return( out )
}

Now we are ready to implement this strategy using the backtesting library in the Systematic Investor Toolbox:

# Load Systematic Investor Toolbox (SIT)
setInternet2(TRUE)
con = gzcon(url('https://github.com/systematicinvestor/SIT/raw/master/sit.gz', 'rb'))
	source(con)
close(con)

	#*****************************************************************
	# Load historical data
	#****************************************************************** 
	load.packages('quantmod')	
	tickers = spl('XLY,XLP,XLE,XLF,XLV,XLI,XLB,XLK,XLU,IWB,IWD,IWF,IWM,IWN,IWO,IWP,IWR,IWS,IWV,IWW,IWZ')	

	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='keep.all', dates='1970::2011')

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

	# find month ends
	month.ends = endpoints(prices, 'months')
		month.ends = month.ends[month.ends > 0]		
		
	# Equal Weight
	data$weight[] = NA
		data$weight[month.ends,] = ntop(prices, n)[month.ends,]	
		capital = 100000
		data$weight[] = (capital / prices) * data$weight
	equal.weight = bt.run(data, type='share')
		
			
	# Rank on 6 month return
	position.score = prices / mlag(prices, 126)	
	
	# Select Top 2 funds
	data$weight[] = NA
		data$weight[month.ends,] = ntop(position.score[month.ends,], 2)	
		capital = 100000
		data$weight[] = (capital / prices) * bt.exrem(data$weight)		
	top2 = bt.run(data, type='share', trade.summary=T)

	# Seletop Top 2 funds,  and Keep then till they are in 1:6 rank
	data$weight[] = NA
		data$weight[month.ends,] = ntop.keep(position.score[month.ends,], 2, 6)	
		capital = 100000
		data$weight[] = (capital / prices) * bt.exrem(data$weight)		
	top2.keep6 = bt.run(data, type='share', trade.summary=T)
	
	#*****************************************************************
	# Create Report
	#****************************************************************** 
	plotbt.custom.report(top2.keep6, top2, equal.weight, trade.summary=T)

There are many ways to improve this strategy. Here is a sample list of additional ways to consider:

The only boundary is your imagination. I would also recommend to do sensitivity analysis during your strategy development to make sure your are not overfitting the data.

To view the complete source code for this example, please have a look at the bt.rotational.trading.test() function in bt.test.r at github.

For more examples, please have a look at the implementation of the Timing Model as presented in A Quantitative Approach to Tactical Asset Allocation by M. Faber (2006) at the bt.timing.model.test() function in bt.test.r at github.

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  1. December 7, 2011 at 1:51 pm

    Nice Work. I use this to learn more about the usage or R and possible algos to improve my TAA which is currently static weighted (with overall protection strategy). However, when I look into it I think that you are using the close and not the adjusted close in bt.prep. Maybe I am wrong in understading your code. I am just curious why…

  2. December 7, 2011 at 3:31 pm

    Andreas,

    Thank you for a good question and reading my blog. In my own testing I use a custom version of getSymbols that adjusts prices internally. I have updated the post to use Adjusted prices. I added call to adjustOHLC before calling bt.prep. Here is the modified section:

    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='keep.all', dates='1970::2011')
    
  3. December 7, 2011 at 6:51 pm

    Thanks. I am very impressed by your programming skills….
    Any objections if I may use you toolbox for own post with a proper link to and description of you as the owner and programmer of it?

  4. December 8, 2011 at 1:02 am

    Andreas,

    Please go ahead and use the Systematic Investor Toolbox in your posts.

    I’m writing a series of posts that will explain functionality and present examples of the Backtesting library.

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