create.EfficientFrontier: create an efficient frontier

Description

create an efficient frontier

create.EfficientFrontier(R, portfolio, type, n.portfolios = 25,
  risk_aversion = NULL, match.col = "ES", search_size = 2000, ...)

an object of class 'efficient.frontier' with the objective measures and weights of portfolios along the efficient frontier.

R: xts object of asset returns
portfolio: object of class 'portfolio' specifying the constraints and objectives, see portfolio.spec.
type: type of efficient frontier, see Details.
n.portfolios: number of portfolios to calculate along the efficient frontier
risk_aversion: vector of risk_aversion values to construct the efficient frontier. n.portfolios is ignored if risk_aversion is specified and the number of points along the efficient frontier will be equal to the length of risk_aversion.
match.col: column to match when extracting the efficient frontier from an objected created by optimize.portfolio.
search_size: passed to optimize.portfolio for type="DEoptim" or type="random".
...: passthrough parameters to optimize.portfolio.

Ross Bennett

Currently there are 4 'types' supported to create an efficient frontier:

"mean-var", "mean-sd", or "mean-StdDev": This is a special case for an efficient frontier that can be created by a QP solver. The portfolio object should have two objectives: 1) mean and 2) var. If the portfolio object does not contain these objectives, they will be added using default parameters. The efficient frontier will be created via meanvar.efficient.frontier.
"mean-ETL", "mean-ES", "mean-CVaR", "mean-etl": This is a special case for an efficient frontier that can be created by an LP solver. The portfolio object should have two objectives: 1) mean and 2) ETL/ES/CVaR. If the portfolio object does not contain these objectives, they will be added using default parameters. The efficient frontier is created via meanetl.efficient.frontier.
"DEoptim": This can handle more complex constraints and objectives than the simple mean-var and mean-ETL cases. For this type, we actually call optimize.portfolio with optimize_method="DEoptim" and then extract the efficient frontier with extract.efficient.frontier.
"random": This can handle more complex constraints and objectives than the simple mean-var and mean-ETL cases. For this type, we actually call optimize.portfolio with optimize_method="random" and then extract the efficient frontier with extract.efficient.frontier.