Backtesting Classical Technical Patterns

May 28, 2012

(This article was first published on Systematic Investor » R, and kindly contributed to R-bloggers)

In the last post, Classical Technical Patterns, I discussed the algorithm and pattern definitions presented in the Foundations of Technical Analysis by A. Lo, H. Mamaysky, J. Wang (2000) paper. Today, I want to check how different patterns performed historically using SPY.

I will follow the rolling window procedure discussed on pages 14-15 of the paper. Let’s begin by loading the historical data for the SPY and running a rolling window pattern search algorithm.

###############################################################################
# Load Systematic Investor Toolbox (SIT)
###############################################################################
con = gzcon(url('http://www.systematicportfolio.com/sit.gz', 'rb'))
    source(con)
close(con)

	#*****************************************************************
	# Load historical data
	#****************************************************************** 
	load.packages('quantmod')
	ticker = 'SPY'
	
	data = getSymbols(ticker, src = 'yahoo', from = '1970-01-01', auto.assign = F)
		data = adjustOHLC(data, use.Adjusted=T)

	#*****************************************************************
	# Search for all patterns over a rolling window
	#****************************************************************** 
	load.packages('sm') 
	history = as.vector(coredata(Cl(data)))
	
	window.L = 35
	window.d = 3
	window.len = window.L + window.d

	patterns = pattern.db()
	
	found.patterns = c()
	
	for(t in window.len : (len(history)-1)) {
		ret = history[(t+1)]/history[t]-1
		
		sample = history[(t - window.len + 1):t]		
		obj = find.extrema( sample )	
		
		if(len(obj$data.extrema.loc) > 0) {
			out =  find.patterns(obj, patterns = patterns, silent=F, plot=F)  
			
			if(len(out)>0) found.patterns = rbind(found.patterns,cbind(t,out,t-window.len+out, ret))
		}
		if( t %% 10 == 0) cat(t, 'out of', len(history), '\n')
	}
	colnames(found.patterns) = spl('t,start,end,tstart,tend,ret')

There are many patterns that are found multiple times. Let’s remove the entries that refer to the same pattern and keep only the first occurrence.

	#*****************************************************************
	# Clean found patterns
	#****************************************************************** 	
	# remove patterns that finished after window.L
	found.patterns = found.patterns[found.patterns[,'end'] <= window.L,]
		
	# remove the patterns found multiple times, only keep first one
	pattern.names = unique(rownames(found.patterns))
	all.patterns = c()
	for(name in pattern.names) {
		index = which(rownames(found.patterns) == name)
		temp = NA * found.patterns[index,]
		
		i.count = 0
		i.start = 1
		while(i.start < len(index)) {
			i.count = i.count + 1
			temp[i.count,] = found.patterns[index[i.start],]
			subindex = which(found.patterns[index,'tstart'] > temp[i.count,'tend'])			
						
			if(len(subindex) > 0) {
				i.start = subindex[1]
			} else break		
		} 
		all.patterns = rbind(all.patterns, temp[1:i.count,])		
	}

Now we can visualize the performance of each pattern using the charts from my presentation about Seasonality Analysis and Pattern Matching at the R/Finance conference.

	#*****************************************************************
	# Plot
	#****************************************************************** 	
	# Frequency for each Pattern
	frequency = tapply(rep(1,nrow(all.patterns)), rownames(all.patterns), sum)
	layout(1)
	barplot.with.labels(frequency/100, 'Frequency for each Pattern')

	
	# Summary for each Pattern
	all.patterns[,'ret'] = history[(all.patterns[,'t']+20)] / history[all.patterns[,'t']] - 1
	data_list = tapply(all.patterns[,'ret'], rownames(all.patterns), list)
	group.seasonality(data_list, '20 days after Pattern')


	# Details for BBOT pattern
	layout(1)
	name = 'BBOT'
	index = which(rownames(all.patterns) == name)	
	time.seasonality(data, all.patterns[index,'t'], 20, name)