##### Scenario-based Portfolio Optimization (scenportopt)
##### (c)2013-2014 Ronald Hochreiter <ron@hochreiter.net>
##### http://www.finance-r.com/
### Portfolio Optimization minimizing Conditional Value at Risk (CVaR)
# Implementation based on [Rockafellar and Uryasev 2001]
# maximize { b - sum(s) { ( p[s] * z[s] ) / alpha } }
# for(s) { loss[s] == sum(a) { (data[s,a] * x[a]) } }
# for(s) { b - loss[s] - z[s] <= 0 } // z >= b - loss
# for(s) { z[s] >= 0 }
optimal.portfolio.expected.shortfall.long.short <- function(model) {
### Variables: b, x[asset], xp[asset], xm[asset], loss[scenario], z[scenario]
n_var <- 1 + 3*model$assets + 2*model$scenarios
ix_b <- 1
ix_x <- 2
ix_xp <- ix_x + model$assets
ix_xm <- ix_xp + model$assets
ix_loss <- ix_xm + model$assets
ix_z <- ix_loss + model$scenarios
### Objective function
# maximize { b - sum(s) { ( p[s] * z[s] ) / alpha } }
Objective <- list()
Objective$linear <- rep(0, n_var)
Objective$linear[ix_b] <- 1
for (s in 0:(model$scenarios-1)) { Objective$linear[ix_z+s] <- -model$scenario.probabilities[s+1]/model$alpha }
### Constraints
Constraints <- list(n=n_var, A=NULL, b=NULL, Aeq=NULL, beq=NULL)
# sum(a) { x[a] } == sum.portfolio
Constraints <- linear.constraint.eq(Constraints, c((ix_x):(ix_x+model$assets-1)), model$sum.portfolio)
# sum(a) { x[a] * mean[a] } => min.mean
if(!is.null(model$min.mean)) { Constraints <- linear.constraint.iq(Constraints, c((ix_x):(ix_x+model$assets-1)), -model$min.mean, -1*model$asset.means) }
# sum(a) { x[a] * mean[a] } == fix.mean
if(!is.null(model$fix.mean)) { Constraints <- linear.constraint.eq(Constraints, c((ix_x):(ix_x+model$assets-1)), model$fix.mean, model$asset.means) }
### Long/Short constraints
# sum(a) { xp[a] } == sum.long
Constraints <- linear.constraint.eq(Constraints, c(ix_xp:(ix_xp+model$assets-1)), model$sum.long)
# sum(a) { xm[a] } == sum.short
Constraints <- linear.constraint.eq(Constraints, c(ix_xm:(ix_xm+model$assets-1)), model$sum.short)
# for(a) { xp[a] - xm[a] - x[a] == 0 } // portfolio == xp - xm
for (a in 0:(model$assets-1)) { Constraints <- linear.constraint.eq(Constraints, c(ix_x+a, ix_xp+a, ix_xm+a), 0, c(-1, 1, -1)) }
### CVaR constraints
# for(s) { loss[s] == sum(a) { (data[s,a] * x[a]) } }
for (s in 0:(model$scenarios-1)) { Constraints <- linear.constraint.eq(Constraints, c(((ix_x):(ix_x+model$assets-1)), ix_loss+s), 0, c(as.vector(model$data[(s+1),]), -1)) }
# for(s) { b - loss[s] - z[s] <= 0 } // z >= b - loss
for (s in 0:(model$scenarios-1)) { Constraints <- linear.constraint.iq(Constraints, c(ix_b, ix_loss+s, ix_z+s), 0, c(1,-1,-1)) }
### Bounds
Bounds <- list()
# all variables unbounded
M <- 1e9
Bounds$lower <- rep(-M, n_var)
Bounds$upper <- rep(M, n_var)
# portfolio constrained to model parameters
Bounds$lower[(ix_x):(ix_x+model$assets-1)] <- model$asset.bound.lower
Bounds$upper[(ix_x):(ix_x+model$assets-1)] <- model$asset.bound.upper
# xp >= 0;
Bounds$lower[(ix_xp):(ix_xp+model$assets-1)] <- 0
# xm >= 0;
Bounds$lower[(ix_xm):(ix_xm+model$assets-1)] <- 0
# z >= 0
Bounds$lower[(ix_z):(ix_z+model$scenarios-1)] <- 0
### Solve optimization problem using modopt.linprog
solution <- linprog(-Objective$linear, Constraints$A, Constraints$b, Constraints$Aeq, Constraints$beq, Bounds$lower, Bounds$upper)
### Add optimal portfolio to model
portfolio <- list()
portfolio$x <- solution$x[ix_x:(ix_x+model$assets-1)]
portfolio$x <- round(portfolio$x, model$precision)
model$portfolio <- portfolio
return(model)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.