(This article was first published on

**Systematic Investor » R**, and kindly contributed to R-bloggers)Mebane Faber posted another interesting blog post: Building a Simple Sector Rotation on Momentum and Trend that caught my interest. Today I want to show how you can test such strategy using the Systematic Investor Toolbox:

############################################################################### # Load Systematic Investor Toolbox (SIT) # http://systematicinvestor.wordpress.com/systematic-investor-toolbox/ ############################################################################### setInternet2(TRUE) con = gzcon(url('http://www.systematicportfolio.com/sit.gz', 'rb')) source(con) close(con) #***************************************************************** # Load historical data #****************************************************************** load.packages('quantmod') data = new.env() # load historical market returns temp = get.fama.french.data('F-F_Research_Data_Factors', periodicity = '',download = T, clean = T) ret = cbind(temp[[1]]$Mkt.RF + temp[[1]]$RF, temp[[1]]$RF) price = bt.apply.matrix(ret / 100, function(x) cumprod(1 + x)) data$SPY = make.stock.xts( price$Mkt.RF ) data$SHY = make.stock.xts( price$RF ) # load historical sector returns temp = get.fama.french.data('10_Industry_Portfolios', periodicity = '',download = T, clean = T) ret = temp[[1]] price = bt.apply.matrix(ret[,1:9] / 100, function(x) cumprod(1 + x)) for(n in names(price)) data[[n]] = make.stock.xts( price[,n] ) # align dates data$symbolnames = c(names(price), 'SHY', 'SPY') bt.prep(data, align='remove.na', dates='2000::') # back-test dates bt.dates = '2001:04::' #***************************************************************** # Setup #****************************************************************** prices = data$prices n = ncol(data$prices) models = list() #***************************************************************** # Benchmark Strategies #****************************************************************** data$weight[] = NA data$weight$SPY[1] = 1 models$SPY = bt.run.share(data, clean.signal=F, dates=bt.dates) weight = prices weight$SPY = NA weight$SHY = NA data$weight[] = NA data$weight[] = ntop(weight[], n) models$EW = bt.run.share(data, clean.signal=F, dates=bt.dates) #***************************************************************** # Code Strategies # http://www.mebanefaber.com/2013/12/04/square-root-of-f-squared/ #****************************************************************** sma = bt.apply.matrix(prices, SMA, 10) # create position score position.score = sma position.score[ prices < sma ] = NA position.score$SHY = NA position.score$SPY = NA # equal weight allocation weight = ntop(position.score[], n) # number of invested funds n.selected = rowSums(weight != 0) # cash logic weight$SHY[n.selected == 0,] = 1 weight[n.selected == 1,] = 0.25 * weight[n.selected == 1,] weight$SHY[n.selected == 1,] = 0.75 weight[n.selected == 2,] = 0.5 * weight[n.selected == 2,] weight$SHY[n.selected == 2,] = 0.5 weight[n.selected == 3,] = 0.75 * weight[n.selected == 3,] weight$SHY[n.selected == 3,] = 0.25 # cbind(round(100*weight,0), n.selected) data$weight[] = NA data$weight[] = weight models$strategy1 = bt.run.share(data, clean.signal=F, dates=bt.dates) #***************************************************************** # Create Report #****************************************************************** strategy.performance.snapshoot(models, one.page = T)

Mebane thank you very much for sharing your great ideas. I would encourage readers to play with this strategy and report back.

Please note that I back-tested the strategy using the monthly observations. The strategy’s draw-down is around 17% using monthly data. If we switch to the daily data, the strategy’s draw-down goes to around 22%. There was one really bad month in 2002.

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

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