**Systematic Investor » R**, and kindly contributed to R-bloggers)

In the prior post, Optimal number of clusters, we looked at methods of selecting number of clusters. Today, I want to continue with clustering theme and show historical Number of Clusters time series using these methods.

In particular, I will look at the following methods of selecting optimal number of clusters:

- Minimum number of clusters that explain at least 90% of variance
- Elbow method
- Hierarchical clustering tree cut at 1/3 height

Let’s first load historical prices for the 10 major asset classes

############################################################################### # 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 for ETFs #****************************************************************** load.packages('quantmod') tickers = spl('GLD,UUP,SPY,QQQ,IWM,EEM,EFA,IYR,USO,TLT') data <- new.env() getSymbols(tickers, src = 'yahoo', from = '1900-01-01', env = data, auto.assign = T) for(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T) bt.prep(data, align='remove.na')

Next, I created 3 helper functions to automate cluster selection. In particular, I used following methods of selecting optimal number of clusters:

- Minimum number of clusters that explain at least 90% of variance – cluster.group.kmeans.90 function
- Elbow method – cluster.group.kmeans.elbow function
- Hierarchical clustering tree cut at 1/3 height – cluster.group.hclust function

To view the complete source code for these functions please have a look at the startegy.r at github.

Let’s use these functions on our data set every week with 250 days look-back to compute correlations.

#***************************************************************** # Use following 3 methods to determine number of clusters # * Minimum number of clusters that explain at least 90% of variance # cluster.group.kmeans.90 # * Elbow method # cluster.group.kmeans.elbow # * Hierarchical clustering tree cut at 1/3 height # cluster.group.hclust #****************************************************************** # helper function to compute portfolio allocation additional stats portfolio.allocation.custom.stats.clusters <- function(x,ia) { return(list( ncluster.90 = max(cluster.group.kmeans.90(ia)), ncluster.elbow = max(cluster.group.kmeans.elbow(ia)), ncluster.hclust = max(cluster.group.hclust(ia)) )) } #***************************************************************** # Compute # Clusters #****************************************************************** periodicity = 'weeks' lookback.len = 250 obj = portfolio.allocation.helper(data$prices, periodicity = periodicity, lookback.len = lookback.len, min.risk.fns = list(EW=equal.weight.portfolio), custom.stats.fn = portfolio.allocation.custom.stats.clusters )

Finally, the historical number of cluster time series plots for each method:

#***************************************************************** # Create Reports #****************************************************************** temp = list(ncluster.90 = 'Kmeans 90% variance', ncluster.elbow = 'Kmeans Elbow', ncluster.hclust = 'Hierarchical clustering at 1/3 height') for(i in 1:len(temp)) { hist.cluster = obj[[ names(temp)[i] ]] title = temp[[ i ]] plota(hist.cluster, type='l', col='gray', main=title) plota.lines(SMA(hist.cluster,10), type='l', col='red',lwd=5) plota.legend('Number of Clusters,10 period moving average', 'gray,red', x = 'bottomleft') }

All methods selected clusters a little bit differently, as expected. The “Minimum number of clusters that explain at least 90% of variance” method seems to produce the most stable results. I would suggest looking at the larger universe (for example DOW30) and longer period of time (for example 1995-present) to evaluate these methods.

Takeaways: As I mentioned in the Optimal number of clusters post, there are many different methods to create clusters, and I have barely scratched the surface. There is also another dimension that I have not explored yet, the distance matrix. Currently, I’m using a correlation matrix as a distance measure to create clusters. I was pointed out by Matt Considine that there is an R interface to the Maximal Information-based Nonparametric Exploration (MINE) metric that can be used as a better measure of correlation.

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

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