[Returnanalytics-commits] r3077 - in pkg/Meucci: R demo
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Sep 13 11:29:05 CEST 2013
Author: xavierv
Date: 2013-09-13 11:29:05 +0200 (Fri, 13 Sep 2013)
New Revision: 3077
Removed:
pkg/Meucci/R/RankingInformation.R
Modified:
pkg/Meucci/demo/ButterflyTrading.R
Log:
-deleted missing svn files from the R folder
Deleted: pkg/Meucci/R/RankingInformation.R
===================================================================
--- pkg/Meucci/R/RankingInformation.R 2013-09-13 05:18:31 UTC (rev 3076)
+++ pkg/Meucci/R/RankingInformation.R 2013-09-13 09:29:05 UTC (rev 3077)
@@ -1,224 +0,0 @@
-# TODO: add max weights constraint to EfficientFrontier()
-# TODO: add computeCVaR to EfficientFrontier()
-# TODO: confirm QuadProg does not have a bug (i.e. it can optimize expected returns without use dvec by adding an equality constraint)
-
-#' Plots the efficient frontier, as it appears in A. Meucci, "Fully Flexible Views: Theory and Practice", The Risk Magazine,
-#' October 2008, p 100-106.
-#'
-#' @param e the NumPortf x 1 matrix of expected returns for each portfolio along the efficient frontier
-#' @param s the NumPortf x 1 matrix of standard deviation of returns for each portfolio along the efficient frontier
-#' @param w the NumPortf x N matrix of compositions (security weights) for each portfolio along the efficient frontier
-#'
-#' @references
-#' A. Meucci, "Fully Flexible Views: Theory and Practice" \url{http://www.symmys.com/node/158}
-#' See Meucci script for "RankingInformation/PlotFrontier.m"
-#'
-#' @author Xavier Valls \email{flamejat@@gmail.com}
-#' @export
-
-
-PlotFrontier = function( e, s, w )
-{
- xx = dim( w )[ 1 ];
- N = dim( w )[ 2 ];
- Data = t( apply( w, 1, cumsum ) );
-
- plot( c(min(s), 0), xlim = c( min(s) , max(s) ), ylim = c( 0, max(Data) ),
- main= "frontier", xlab = " Portfolio # risk propensity", ylab = "Portfolio composition" );
-
- for( n in 1 : N )
- {
- x = rbind( min(s), s, max(s) );
- y = rbind( 0, matrix( Data[ , N-n+1 ] ), 0 );
- polygon( x, y, col = rgb( 0.9 - mod(n,3)*0.2, 0.9 - mod(n,3)*0.2, 0.9 - mod(n,3)*0.2) );
- }
-}
-
-#' Plots the results of computing the efficient frontier (Expected returns and frontier), as it appears in A. Meucci, "Fully Flexible Views: Theory and Practice", The Risk Magazine,
-#' October 2008, p 100-106.
-#'
-#' @param e the NumPortf x 1 matrix of expected returns for each portfolio along the efficient frontier
-#' @param s the NumPortf x 1 matrix of standard deviation of returns for each portfolio along the efficient frontier
-#' @param w the NumPortf x N matrix of compositions (security weights) for each portfolio along the efficient frontier
-#' @param M the NumPortf x 1 vector of expected returns for each asset
-#' @param Lower constraints
-#' @param Upper constraints
-#'
-#' @references
-#' A. Meucci, "Fully Flexible Views: Theory and Practice" \url{http://www.symmys.com/node/158}
-#' See Meucci script for "RankingInformation/PlotResults.m"
-#'
-#' @author Xavier Valls \email{flamejat@@gmail.com}
-
-PlotResults = function( e, s, w, M, Lower = NULL , Upper = NULL)
-{
- N = length( M );
- dev.new();
- par( mfrow = c( 1, 2 ) );
- h1 = hist( M*100, plot = F )
- barplot( h$density, horiz = T, main = "expected returns", xlab = "", ylab = "" );
- if(length(Lower) || length(Upper))
- {
- Changed = array( 0, N );
- Changed[ union( Lower, Upper ) ] = M[ union( Lower, Upper ) ] * 100;
- h2 = hist(Changed, plot = F );
- barplot( h2$density, horiz = T, col = "red", add = T );
- }
-
- PlotFrontier( e*100, s*100, w );
-}
-
-
-
-#' Computes posterior probabilities to view the rankings, as it appears in A. Meucci,
-#' "Fully Flexible Views: Theory and Practice", The Risk Magazine, October 2008, p 100-106.
-#'
-#' @param X a vector containing returns for all the asset classes
-#' @param p a vector containing the prior probability values
-#' @param Lower a vector of indexes indicating which column is lower than the corresponding column number in Upper
-#' @param Upper a vector of indexes indicating which column is lower than the corresponding column number in Upper
-#'
-#' @references
-#' A. Meucci, "Fully Flexible Views: Theory and Practice" \url{http://www.symmys.com/node/158}
-#' See Meucci script for "RankingInformation/ViewRanking.m"
-#'
-#' @author Ram Ahluwalia \email{ram@@wingedfootcapital.com}
-#' @export EntropyProg
-
-# example ViewRanking( X , p , Lower = c(3,4) , Upper = c(4,5) ) # two inequality views: asset 3 < asset 4 returns, and asset 4 < asset 5 returns
-
-ViewRanking = function( X , p , Lower , Upper )
-{
- library( matlab )
- J = nrow( X )
- N = ncol( X )
-
- K = length( Lower )
-
- # constrain probabilities to sum to one across all scenarios...
- Aeq = ones( 1 , J )
- beq = 1
-
- # ...constrain the expectations... A*x <= 0
- # X[,Lower] refers to the column of returns for Asset-lower
- # X[,Upper] refers to the column of returns for Asset-lower
- # X[ , Lower ] - X[ , Upper ] is vector returns of the "lower"" asset less the returns of the "higher" asset
- V = X[ , Lower ] - X[ , Upper ] # Jx1 vector. Expectation is assigned to each scenario
-
- A = t( V )
- b = 0 # The expectation is that (Lower - Upper)x <= 0. (i.e. The returns of upper are greater than zero for each scenario)
-
- # ...compute posterior probabilities
- p_ = EntropyProg( p , A , as.matrix(b) , Aeq , as.matrix(beq) )
-
- return( p_ )
-}
-
-#' Generates an efficient frontier based on Meucci's Ranking Information version and returns a A list with
-#' NumPortf efficient portfolios whos returns are equally spaced along the whole range of the efficient frontier,
-#' as it appears in A. Meucci, "Fully Flexible Views: Theory and Practice", The Risk Magazine, October 2008,
-#' p 100-106.
-#'
-#' Most recent version of article and MATLAB code available at
-#' http://www.symmys.com/node/158
-#'
-#' @param X a matrix with the joint-scenario probabilities by asset (rows are joint-scenarios, columns are assets)
-#' @param p a vector of probabilities associated with each scenario in matrix X
-#' @param Options a list of options....TBD
-#'
-#' @return Exps the NumPortf x 1 vector of expected returns for each asset
-#' @return Covs the NumPortf x N vector of security volatilities along the efficient frontier
-#' @return w the NumPortf x N matrix of compositions (security weights) for each portfolio along the efficient frontier
-#' @return e the NumPortf x 1 matrix of expected returns for each portfolio along the efficient frontier
-#' @return s the NumPortf x 1 matrix of standard deviation of returns for each portfolio along the efficient frontier
-#'
-#' @references
-#' A. Meucci, "Fully Flexible Views: Theory and Practice" \url{http://www.symmys.com/node/158}
-#' See Meucci script for "RankingInformation/EfficientFrontier.m"
-#'
-#' @author Ram Ahluwalia \email{ram@@wingedfootcapital.com} and Xavier Valls \email{flamejat@@gmail.com}
-#' @export
-
-RIEfficientFrontier = function( X , p , Options)
-{
-
- if( !require("limSolve") ) stop("This script requieres the limSolve package installed")
-
-
- library( matlab )
-
- J = nrow( X ) # number of scenarios
- N = ncol( X ) # number of assets
-
- Exps = t(X) %*% p # probability-weighted expected return of each asset
-
- Scnd_Mom = t(X) %*% (X * ( p %*% matrix( 1, 1 , N ) ) )
- Scnd_Mom = ( Scnd_Mom + t(Scnd_Mom) ) / 2 # an N*N matrix
- Covs = Scnd_Mom - Exps %*% t( Exps )
-
- Constr = list()
-
- # constrain the sum of weights to 1
- Constr$Aeq = matrix( 1, 1 , N )
- Constr$beq = 1
-
- # constrain the weight of any security to between 0 and 1
- Constr$Aleq = rbind( diag( 1, N ) , - diag( 1, N ) ) # linear coefficients matrix A in the inequality constraint A*x <= b
- Constr$bleq = rbind( matrix( 1, N, 1 ) , matrix( 0, N, 1 ) ) # constraint vector b in the inequality constraint A*x <= b
-
- Amat = rbind( Constr$Aeq , Constr$Aleq ) # stack the equality constraints on top of the inequality constraints
- bvec = rbind( Constr$beq , Constr$bleq ) # stack the equality constraints on top of the inequality constraints
-
- ############################################################################################
- # determine return of minimum-risk portfolio
- FirstDegree = matrix( 0, N , 1 ) # TODO: assumes that securities have zero expected returns when computing efficient frontier?
- SecondDegree = Covs
- # Why is FirstDegree "expected returns" set to 0?
- # We capture the equality view in the equality constraints matrix
- # In other words, we have a constraint that the Expected Returns by Asset %*% Weights = Target Return
- MinVol_Weights = solve.QP( Dmat = SecondDegree , dvec = -1*FirstDegree , Amat = -1*t(Amat) , bvec = -1*bvec , meq = length( Constr$beq ) )
- MinSDev_Exp = t( MinVol_Weights$solution ) %*% Exps
-
- ############################################################################################
- # determine return of maximum-return portfolio
- FirstDegree = -Exps
- MaxRet_Weights = linp( E = Constr$Aeq , F = Constr$beq , G = -1*Constr$Aleq , H = -1*Constr$bleq , Cost = FirstDegree , ispos = FALSE )$X
- MaxExp_Exp = t( MaxRet_Weights) %*% Exps
-
- ############################################################################################
- # slice efficient frontier in NumPortf equally thick horizontal sections
- Grid = matrix( , ncol = 0 , nrow = 0 )
- Grid = t( seq( from = Options$FrontierSpan[1] , to = Options$FrontierSpan[2] , length.out = Options$NumPortf ) )
-
- # the portfolio return varies from a minimum of MinSDev_Exp up to a maximum of MaxExp_Exp
- # We establish equally-spaced portfolio return targets and use this find efficient portfolios
- # in the next step
- Targets = as.numeric( MinSDev_Exp ) + Grid * as.numeric( ( MaxExp_Exp - MinSDev_Exp ) )
-
- ############################################################################################
- # compute the NumPortf compositions and risk-return coordinates
- FirstDegree = matrix( 0, N , 1 )
-
- w = matrix( , ncol = N , nrow = 0 )
- e = matrix( , ncol = 1 , nrow = 0 )
- s = matrix( , ncol = 1 , nrow = 0 )
-
- for ( i in 1:Options$NumPortf )
- {
- # determine least risky portfolio for given expected return
- # Ax = b ; Exps %*% weights = Target Return
- AEq = rbind( Constr$Aeq , t( Exps ) ) # equality constraint: set expected return for each asset...
- bEq = rbind( Constr$beq , Targets[ i ] ) # ...and target portfolio return for i'th efficient portfolio
-
- Amat = rbind( AEq , Constr$Aleq ) # stack the equality constraints on top of the inequality constraints
- bvec = rbind( bEq , Constr$bleq )
-
- Weights = solve.QP( Dmat = SecondDegree , dvec = -1*FirstDegree , Amat = -1*t(Amat) , bvec = -1*bvec , meq = length( bEq ) )
-
- w = rbind( w , Weights$solution )
- s = rbind( s , sqrt( t(Weights$solution) %*% Covs %*% Weights$solution ) )
- e = rbind( e , Weights$solution %*% Exps )
- }
-
- return( list( e = e , Sdev = s , Composition = w , Exps = Exps , Covs = Covs ) )
-}
Modified: pkg/Meucci/demo/ButterflyTrading.R
===================================================================
--- pkg/Meucci/demo/ButterflyTrading.R 2013-09-13 05:18:31 UTC (rev 3076)
+++ pkg/Meucci/demo/ButterflyTrading.R 2013-09-13 09:29:05 UTC (rev 3077)
@@ -63,7 +63,7 @@
PlotFrontier( optimalPortfolios$Exp , optimalPortfolios$CVaR , optimalPortfolios$Composition )
-[Exp,SDev,CVaR,w] = LongShortMeanCVaRFrontier(PnL,p,butterfliesAnalytics,Options);
+#[Exp,SDev,CVaR,w] = LongShortMeanCVaRFrontier(PnL,p,butterfliesAnalytics,Options);
#PlotEfficientFrontier(Exp,CVaR,w)
###########################################################################################################
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