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Calendar-based Sector Strategy

I recently came across the Kaeppel’s Sector Seasonality Strategy which is described in Kaeppel’s Corner: Sector Seasonality and updated in Kaeppel’s Corner: Get Me Back, Clarence.

Today I want to show how to back-test the Kaeppel’s Sector Seasonality Strategy using the Systematic Investor Toolbox. Following are the strategy rules:

  • Buy Fidelity Select Technology (FSPTX) at the October close.
  • Switch from FSPTX to Fidelity Select Energy (FSENX) at the January close.
  • Switch from FSENX to cash at the May close.
  • Switch from cash to Fidelity Select Gold (FSAGX) at the August close.
  • Switch from FSAGX to cash at the September close.
  • Repeat by switching from cash to FSPTX at the October close.

Let’s start by loading historical data

###############################################################################
# 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')  

    tickers = spl('FSPTX,FSENX,FSAGX,VFINX,BIL') 

    data <- new.env()
        getSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)

    #--------------------------------   
    # BIL     30-May-2007 
    # load 3-Month Treasury Bill from FRED
    TB3M = getSymbols('DTB3', src='FRED', auto.assign = FALSE)		
    TB3M[] = ifna.prev(TB3M)	
    TB3M = processTBill(TB3M, timetomaturity = 1/4, 261)	
    #--------------------------------       	

    # extend BIL with 3-Month Treasury Bills
    data$BIL = extend.data(data$BIL, TB3M, scale=T)	
	

        for(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)						
    bt.prep(data, align='remove.na')

Next let’s create 2 benchmark strategies:

  • Vanguard 500 Index Investor (VFINX)
  • VFINX from the October close through the May close and cash otherwise (VFINX /Cash)
    #*****************************************************************
    # Code Strategies
    #****************************************************************** 
    prices = data$prices 
    dates = data$dates	
	        
    models = list()

    # find period ends
    period.ends = endpoints(prices, 'months')
        period.ends = period.ends[period.ends > 0]	
	
    months = date.month(dates[period.ends])
	    	
    #*****************************************************************
    # Code Strategies
    #****************************************************************** 
    # Vanguard 500 Index Investor (VFINX)
    data$weight[] = NA
        data$weight$VFINX[] = 1
    models$VFINX  = bt.run.share(data, clean.signal=F) 
	
	
    # VFINX from the October[10] close through the May[5] close and cash otherwise (VFINX /Cash)
    data$weight[] = NA
        data$weight$VFINX[period.ends] = iif( months >= 10 | months <= 5, 1, 0)
        data$weight$BIL[period.ends] = iif( !(months >= 10 | months <= 5), 1, 0)
    models$VFINX_Cash  = bt.run.share(data, clean.signal=F) 	

And finally, let’s create the Kaeppel’s Sector Seasonality Strategy and make reports

    #*****************************************************************
    # Calendar-based sector strategy
    #****************************************************************** 	
    # Buy Fidelity Select Technology (FSPTX) at the October close.
    # Switch from FSPTX to Fidelity Select Energy (FSENX) at the January close.
    # Switch from FSENX to cash at the May close.
    # Switch from cash to Fidelity Select Gold (FSAGX) at the August close.
    # Switch from FSAGX to cash at the September close.
    # Repeat by switching from cash to FSPTX at the October close.
    data$weight[] = NA
        # Buy Fidelity Select Technology (FSPTX) at the October close.
        data$weight$FSPTX[period.ends] = iif( months >= 10 | months < 1, 1, 0)
		
        # Switch from FSPTX to Fidelity Select Energy (FSENX) at the January close.
        data$weight$FSENX[period.ends] = iif( months >= 1 & months < 5, 1, 0)
				
        # Switch from cash to Fidelity Select Gold (FSAGX) at the August close.
        data$weight$FSAGX[period.ends] = iif( months >= 8 & months < 9, 1, 0)

        # Rest time in Cash
        data$weight$BIL[period.ends] = 1 - rowSums(data$weight[period.ends], na.rm = T)
    models$Sector  = bt.run.share(data, clean.signal=F) 	
		           
    #*****************************************************************
    # Create Report
    #****************************************************************** 
    strategy.performance.snapshoot(models, T)

plot1

The Technology exposure is surely made a big difference from 1998 to 2000. But looking at the strategy perfromance since 2002, the strategy is still doing better than our benchmarks.

plot2

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

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Categories: Backtesting, R
  1. lovemath
    May 1, 2014 at 1:40 am

    The rotation strategy suggests data mining. Even if the anomaly was discovered without data mining, it is best to calculate excess returns by subtracting monthly broad market index returns (e.g. the total return from S&P 500 or the S&P 500 Spider SPY) and then do a One Sample t-test for significance. See the paper http://faculty-gsb.stanford.edu/oyer/wp/tech.pdf for a more rigorous method of validating such anomalies.

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