Book a Demo!
CoCalc Logo Icon
StoreFeaturesDocsShareSupportNewsAboutPoliciesSign UpSign In
braverock
GitHub Repository: braverock/portfolioanalytics
 Path: blob/master/demo/multiple_portfolio_optimization.R
1433 views
1


2
library(PortfolioAnalytics)

3


4
# Examples of passing a list portfolio objects to optimize.portfolio and

5
# optimize.portfolio.rebalancing

6


7
data(edhec)

8
R <- edhec[, 1:4]

9
funds <- colnames(R)

10


11
# Construct initial portfolio

12
init.portf <- portfolio.spec(assets=funds)

13
init.portf <- add.constraint(portfolio=init.portf, type="weight_sum",

14
                             min_sum=0.99, max_sum=1.01)

15
init.portf <- add.constraint(portfolio=init.portf, type="long_only")

16


17
# Minimize portfolio standard deviation

18
minSD.portf <- add.objective(portfolio=init.portf, type="risk", name="StdDev")

19


20
# Maximize mean return per unit portfolio standard deviation

21
meanSD.portf <- add.objective(portfolio=minSD.portf, type="return", name="mean")

22


23
# Minimize expected shortfall

24
minES.portf <- add.objective(portfolio=init.portf, type="risk", name="ES")

25


26
# Maximize mean return per unit portfolio expected shortfall

27
meanES.portf <- add.objective(portfolio=minES.portf, type="return", name="mean")

28


29
# Combine the portfolios

30
mult.portf <- combine.portfolios(list(minSD.portf, meanSD.portf, minES.portf, meanES.portf))

31
mult.portf

32


33
# run the optimization for mult.portf

34
mult.opt <- optimize.portfolio(R, mult.portf, optimize_method="random", 

35
                               search_size=2000, trace=TRUE, message = TRUE)

36


37
class(mult.opt)

38
mult.opt

39


40
# This combines the weights for each portfolio optimized

41
extractWeights(mult.opt)

42


43
# This combines the objective measures for each portfolio

44
extractObjectiveMeasures(mult.opt)

45


46
# For N portfolios, this returns a list of length N with the stats

47
# for each portfolio

48
opt.xtract <- extractStats(mult.opt)

49


50
# Run the rebalancing optimization for mult.portf

51
mult.opt.rebal <- optimize.portfolio.rebalancing(R, mult.portf, 

52
                                                 optimize_method="random", 

53
                                                 search_size=2000, 

54
                                                 trace=TRUE, 

55
                                                 message=TRUE,

56
                                                 rebalance_on="quarters", 

57
                                                 training_period=140)

58


59
class(mult.opt.rebal)

60
mult.opt.rebal

61


62
# For N portfolios, this returns a list of length N with the optimal weights

63
# at each rebalancing date

64
extractWeights(mult.opt.rebal)

65


66
# For N portfolios, this returns a list of length N with the objective measures

67
# at each rebalancing date

68
extractObjectiveMeasures(mult.opt.rebal)

69


70
# For N portfolios, this returns a list of length N with the stats

71
# for each portfolio

72
opt.rebal.xtract <- extractStats(mult.opt.rebal)

73


74


75


76