Cluster Risk Parity back-test

from: http://www.r-bloggers.com/cluster-risk-parity-back-test/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+RBloggers+%28R+bloggers%29

In the Cluster Portfolio Allocation post, I have outlined the 3 steps to construct Cluster Risk Parity portfolio. At each rebalancing period:

• Create Clusters
• Allocate funds within each Cluster using Risk Parity
• Allocate funds across all Clusters using Risk Parity

I created a helper function distribute.weights() function in strategy.r at github to automate these steps. It has 2 parameters:

• Function to allocate funds. For example, risk.parity.portfolio, will use use risk parity to allocate funds both within and across clusters.
• Function to create clusters. For example, cluster.group.kmeans.90, will create clusters using k-means algorithm

Here is the example how to put it all together. 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, let’s run the 2 versions of Cluster Portfolio Allocation using Equal Weight and Risk Parity algorithms to allocate funds:

 #*****************************************************************
 # Code Strategies
 #******************************************************************  
 periodicity = 'months'
 lookback.len = 250
 cluster.group = cluster.group.kmeans.90
 
 obj = portfolio.allocation.helper(data$prices, 
  periodicity = periodicity, lookback.len = lookback.len,
  min.risk.fns = list(
    EW=equal.weight.portfolio,
    RP=risk.parity.portfolio,
      
    C.EW = distribute.weights(equal.weight.portfolio, cluster.group),
    C.RP=distribute.weights(risk.parity.portfolio, cluster.group)
   )
 )   
 
 models = create.strategies(obj, data)$models

Finally, let’s examine the results:

 #*****************************************************************
 # Create Report
 #******************************************************************  
 strategy.performance.snapshoot(models, T)

The Cluster Portfolio Allocation produce portfolios with better risk-adjusted returns and smaller drawdowns.
To view the complete source code for this example, please have a look at the bt.cluster.portfolio.allocation.test1() function in bt.test.r at github.