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R/Finance Conference

I will be presenting at the R/Finance conference in Chicago on May 17-18. Details of the conference are available at: www.rinfinance.com

See you there!

Categories: Uncategorized

Updates

I will be traveling to Freeport, Bahamas during the last week of August. If you want to meet for a coffee drop me a line.

I also plan to release the first version of SIT package at the end of September.

Categories: Uncategorized

R/Finance 2012 presentation

I have attended for the first time the R/Finance conference this year. I must say that I’m impressed with the effort that organizers put into the conference and the breadth and the depth of the material / ideas presented.

I just want to share slides and examples that I used in my presentation about Seasonality Analysis and Pattern Matching.

In the next post, I plan to discuss in more detail the algorithm and the price pattern definitions I used to find classical technical patterns.

Categories: Uncategorized

R/Finance Conference

April 21, 2012 1 comment

I will be presenting at the R/Finance conference in Chicago on May 11-12. Details of the conference are available here: www.rinfinance.com

See you there!

Categories: Uncategorized

Historical Seasonality Analysis: What company in DOW 30 is likely to do well in January?

December 30, 2011 2 comments

THIS IS NOT INVESTMENT ADVICE. The information is provided for informational purposes only.

Stock Market Seasonality is easy to understand, but hard to justify if you subscribe to Efficient-market hypothesis. For a good summary of seasonality, have a look at Capitalizing On Seasonal Effects article at Investopedia. Another interesting discussion was started by Douglas R Terry in his post Efficient Market Hypothesis and Seasonality that goes into detail analysis how these two ideas can co-exist.

To study Seasonality, I want to introduce the new tool I developed. The Seasonality Tool is a user-friendly, point and click, application to create seasonality statistics and reports.

To demonstrate the Seasonality Tool, I want to show how it can be used to evaluate an investment idea. I want find which company in DOW 30 is likely to do well in January and evaluate it using Seasonality Tool.

Following code loads historical prices from Yahoo Fiance for all companies in the DOW 30 index and computes their average performance in January. I will use the Systematic Investor Toolbox to load and analyze the data:

###############################################################################
# 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')        
    tickers = dow.jones.components()
    
    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')
        
    #*****************************************************************
    # Compute monthly returns
    #****************************************************************** 
    prices = data$prices   
    n = ncol(prices)    
    
    # find month ends
    month.ends = endpoints(prices, 'months')
    
    prices = prices[month.ends,]
    ret = prices / mlag(prices) - 1

    # keep only January    
    ret = ret[date.month(index(ret)) == 1, ]
    
    # keep last 20 years
    ret = last(ret,20)

    #*****************************************************************
    # Compute stats
    #****************************************************************** 
    stats = matrix(rep(NA,2*n), nc=n)
        colnames(stats) = colnames(prices)
        rownames(stats) = spl('N,Positive')
        
    for(i in 1:n) {
        stats['N',i] = sum(!is.na(ret[,i]))
        stats['Positive',i] = sum(ret[,i]>0, na.rm=T)    
    }
    sort(stats['Positive',])

The Walt Disney Co. (DIS) was positive 17 times in January in the last 20 years.
Let’s investigate the Walt Disney Co. (DIS) record using the Seasonality Tool.

The Seasonality Analysis Report confirms Walt Disney Co. (DIS) outstanding track record in January. Next let’s see the details for January.

The Detail Seasonality Analysis Report shows that the Walt Disney Co. (DIS) had an amazing returns in January till 2008. Following by a 3 consecutive negative Januaries which most likely indicates a change in trend (regime) for this company.

So do I expect the Walt Disney Co. (DIS) be positive in the upcoming January? I’m not so sure anymore.

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

Categories: Uncategorized

Happy Holidays and Best Wishes for 2012

December 23, 2011 2 comments

This is just a quick note to wish you and your family a very healthy and happy holidays and wonderful New Year! I hope you enjoyed reading my blog and thank you for your comments and emails.

Here is a short R code that implements an interesting idea from the Charting the Santa Claus Rally post by Woodshedder. I will plot and compare the SPY performance this December versus average performance in previous Decembers.

# 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('SPY')

	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='remove.na', dates='1970::2011')

	#*****************************************************************
	# Prepare Data for the plot
	#****************************************************************** 
	prices = data$prices  
	n = len(tickers)  
	ret = prices / mlag(prices) - 1

	
	# find prices in December
	dates = index(prices)
	years = date.year(dates)	
	index = which(date.month(dates) == 12)
	
	# rearrange data in trading days
	trading.days = sapply(tapply(ret[index,], years[index], function(x) coredata(x)), function(x) x[1:22])
		
	# average return each trading days, excluding current year
	avg.trading.days = apply(trading.days[, -ncol(trading.days)], 1, mean, na.rm=T)
	current.year = trading.days[, ncol(trading.days)]
	
	# cumulative
	avg.trading.days = 100 * ( cumprod(1 + avg.trading.days) - 1 )
	current.year = 100 * ( cumprod(1 + current.year) - 1 )
	
	#*****************************************************************
	# Create Plot
	#****************************************************************** 	
	par(mar=c(4,4,1,1))
	plot(avg.trading.days, type='b', col=1,
		ylim=range(avg.trading.days,current.year,na.rm=T),
		xlab = 'Number of Trading Days in December',
		ylab = 'Avg % Profit/Loss'
		)
		lines(current.year, type='b', col=2)
	grid()
	plota.legend('Avg SPY,SPY Dec 2011', 1:2)

Hope this year will not disappoint and we will see the rally towards the year end.

If you want to find average performance in the other months, I recommend reading Trading Calendar article by CXO Advisory.

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

Categories: R, Uncategorized

Periodic Table of Investment Returns

To get a better sense of historical data, I like to examine a Periodic Table of Investment Returns. For an example of a Periodic Table, have a look at the Single Country Index Returns Periodic Table for 2001-2010 published by iShares.

I can easily create a similar table with the following R code, using the historical data from the Black-Litterman Model post.

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

	#--------------------------------------------------------------------------
	# Get Historical Data
	#--------------------------------------------------------------------------
	# Country's IA are based on monthly data
	ia = aa.test.create.ia.country()
		hist.returns = ia$hist.returns

	# convert returns to prices
	hist.prices = cumprod(1 + hist.returns)

	# extract annual prices
	period.ends = endpoints(hist.prices, 'years')
		hist.prices = hist.prices[period.ends, ]

	# compute simple returns
	hist.returns = na.omit( ROC(hist.prices, type = 'discrete') )
		hist.returns = hist.returns['2000::2010']

	#--------------------------------------------------------------------------
	# Create Periodic table
	#--------------------------------------------------------------------------
	# create temp matrix with data you want to plot
	temp = t(coredata(hist.returns))
		colnames(temp) = format(index(hist.returns), '%Y')
		rownames(temp) = 1:ia$n
			rownames(temp)[1] = ' Best '
			rownames(temp)[ia$n] = ' Worst '

	# highlight each column
	col = plota.colors(ia$n)
	highlight = apply(temp,2, function(x) col[order(x, decreasing = T)] )
	
	# sort each column
	temp[] = apply(temp,2, sort, decreasing = T)
	
	# format data as percentages
	temp[] = plota.format(100 * temp, 0, '', '%')	

	# plot temp and legend
	plot.table(temp, highlight = highlight)			
	plota.legend(ia$symbols,col,cex=1.5)

The Canadian and Australian markets outperformed US and Japanese markets in most years.

Here is a slightly different version of Periodic table:

	#--------------------------------------------------------------------------
	# Create Periodic table, another version
	#--------------------------------------------------------------------------
	# create temp matrix with data you want to plot
	temp = t(coredata(hist.returns))
		colnames(temp) = format(index(hist.returns), '%Y')

	# format data as percentages
	temp[] = plota.format(100 * temp, 0, '', '%')

	# highlight each column separately
	highlight = apply(temp,2, function(x) plot.table.helper.color(t(x)) )

	# plot temp with colorbar
	plot.table(temp, highlight = highlight, colorbar = TRUE)

To view the complete source code for this example, please have a look at the aa.periodic.table.test() function in aa.test.r at github.

Categories: Uncategorized
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