[Returnanalytics-commits] r3452 - pkg/PortfolioAnalytics/demo

[noreply at r-forge.r-project.org]

Author: rossbennett34
Date: 2014-06-30 22:32:24 +0200 (Mon, 30 Jun 2014)
New Revision: 3452

Added:
   pkg/PortfolioAnalytics/demo/meucci_relative_ranking.R
Modified:
   pkg/PortfolioAnalytics/demo/00Index
Log:
Adding demo script for meucci relative ranking

Modified: pkg/PortfolioAnalytics/demo/00Index
===================================================================
--- pkg/PortfolioAnalytics/demo/00Index	2014-06-30 19:38:58 UTC (rev 3451)
+++ pkg/PortfolioAnalytics/demo/00Index	2014-06-30 20:32:24 UTC (rev 3452)
@@ -32,3 +32,4 @@
 higher_moments_boudt     Demonstrate using a statistical factor model to estimate moments based on work by Kris Boudt.
 multi_layer_optimization    Demonstrate multi layer optimization of optimization problem with two layers and two sub portfolios in the lower layer.
 meucci_ffv    Demonstrate Meucci's Fully Flexible Views framework to estimate moments and use as inputs for  minimum variance optimization.
+meucci_relative_ranking   Demonstrate Meucci's Fully Flexible Views framework to express views on relative ranking and  estimate moments used as inputs for mean-variance optimization.

Added: pkg/PortfolioAnalytics/demo/meucci_relative_ranking.R
===================================================================
--- pkg/PortfolioAnalytics/demo/meucci_relative_ranking.R	                        (rev 0)
+++ pkg/PortfolioAnalytics/demo/meucci_relative_ranking.R	2014-06-30 20:32:24 UTC (rev 3452)
@@ -0,0 +1,72 @@
+# Demonstrate Meucci's Fully Flexible Views framework to express views on 
+# relative ranking and estimate moments used as inputs for mean-variance 
+# optimization
+
+library(PortfolioAnalytics)
+data(edhec)
+R <- edhec[,1:4]
+funds <- colnames(R)
+
+# Construct initial portfolio
+init.portf <- portfolio.spec(assets=funds)
+init.portf <- add.constraint(portfolio=init.portf, type="weight_sum", 
+                             min_sum=0.99, max_sum=1.01)
+init.portf <- add.constraint(portfolio=init.portf, type="box",
+                             min=0.05, max=0.5)
+init.portf <- add.objective(portfolio=init.portf, type="risk", name="StdDev")
+init.portf <- add.objective(portfolio=init.portf, type="return", name="mean")
+
+# Prior probabilities
+p <- rep(1 / nrow(R), nrow(R))
+
+# Relative ordering view
+# E{ R[,2] < R[,3], < R[,1] < R[,4] }
+moments <- meucci.ranking(R, p, c(2, 3, 1, 4))
+
+# Generate random portfolios
+rp <- random_portfolios(init.portf, 5000)
+
+# Optimization using first and second moments estimated from Meucci's Fully 
+# Flexible Views framework.
+opt.meucci <- optimize.portfolio(R, 
+                                 init.portf, 
+                                 optimize_method="random", 
+                                 rp=rp, 
+                                 trace=TRUE,
+                                 method="meucci", 
+                                 momentargs=moments)
+
+
+# Optimization using sample estimates for first and second moments
+opt.sample <- optimize.portfolio(R, 
+                                 init.portf, 
+                                 optimize_method="random", 
+                                 rp=rp,
+                                 trace=TRUE)
+
+#Extract the stats for plotting
+stats.meucci <- extractStats(opt.meucci)
+stats.sample <- extractStats(opt.sample)
+
+
+# Plots
+# Plot the optimal weights
+chart.Weights(combine.optimizations(list(meucci=opt.meucci, sample=opt.sample)), ylim=c(0,1))
+
+# Plot the risk-reward of each chart on the same scale
+xrange <- range(c(stats.meucci[,"StdDev"], stats.sample[,"StdDev"]))
+yrange <- range(c(stats.meucci[,"mean"], stats.sample[,"mean"]))
+layout(matrix(c(1,2)), widths=1, heights=1)
+# c(bottom, left, top, right)
+par(mar=c(0, 4, 4, 4) + 0.1)
+plot(x=stats.meucci[,"StdDev"], stats.meucci[,"mean"], xlab="", ylab="mean", 
+     xlim=xrange, ylim=yrange, xaxt="n", yaxt="n")
+axis(2, pretty(yrange), cex.axis=0.8)
+legend("topleft", legend="Meucci", bty="n")
+par(mar=c(5, 4, 0, 4) + 0.1)
+plot(x=stats.sample[,"StdDev"], stats.sample[,"mean"], xlab="StdDev", ylab="", 
+     xlim=xrange, ylim=yrange, yaxt="n", cex.axis=0.8)
+axis(4, pretty(yrange), cex.axis=0.8)
+legend("topleft", legend="Sample", bty="n")
+par(mar=c(5, 4, 4, 2) + 0.1)
+layout(matrix(1), widths=1, heights=1)