Adjusted Momentum

David Varadi has published two excellent posts / ideas about cooking with momentum:

• VIX-Adjusted Momentum
• Error-Adjusted Momentum

I just could not resist the urge to share these ideas with you. Following is implementation using the Systematic Investor Toolbox.

###############################################################################
# Load Systematic Investor Toolbox (SIT)
# https://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('SPY,^VIX')
		
	data <- new.env()
	getSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)
		for(i in data$symbolnames) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)
	bt.prep(data, align='remove.na', fill.gaps = T)

	VIX = Cl(data$VIX)
	bt.prep.remove.symbols(data, 'VIX')
	
	#*****************************************************************
	# Setup
	#*****************************************************************
	prices = data$prices
		
	models = list()

	#*****************************************************************
	# 200 SMA
	#****************************************************************** 
	data$weight[] = NA
		data$weight[] = iif(prices > SMA(prices, 200), 1, 0)
	models$ma200 = bt.run.share(data, clean.signal=T)
	
	#*****************************************************************
	# 200 ROC
	#****************************************************************** 
	roc = prices / mlag(prices) - 1
	
	data$weight[] = NA
		data$weight[] = iif(SMA(roc, 200) > 0, 1, 0)
	models$roc200 = bt.run.share(data, clean.signal=T)
	
	#*****************************************************************
	# 200 VIX MOM
	#****************************************************************** 
	data$weight[] = NA
		data$weight[] = iif(SMA(roc/VIX, 200) > 0, 1, 0)
	models$vix.mom = bt.run.share(data, clean.signal=T)

	#*****************************************************************
	# 200 ER MOM
	#****************************************************************** 
	forecast = SMA(roc,10)
	error = roc - mlag(forecast)
	mae = SMA(abs(error), 10)
	
	data$weight[] = NA
		data$weight[] = iif(SMA(roc/mae, 200) > 0, 1, 0)
	models$er.mom = bt.run.share(data, clean.signal=T)
		
	#*****************************************************************
	# Report
	#****************************************************************** 
	strategy.performance.snapshoot(models, T)

Please enjoy and share your ideas with David and myself.

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

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