Contemporary Portfolio Optimization

Description

Simplify your portfolio optimization process by applying a contemporary modeling way to model and solve your portfolio problems. While most approaches and packages are rather complicated this one tries to simplify things and is agnostic regarding risk measures as well as optimization solvers. Some of the methods implemented are described by Konno and Yamazaki (1991) <doi:10.1287/mnsc.37.5.519>, Rockafellar and Uryasev (2001) <doi:10.21314/JOR.2000.038> and Markowitz (1952) <doi:10.1111/j.1540-6261.1952.tb01525.x>.

Author(s)

Ronald Hochreiter, ronald@hochreiter.net

References

http://www.finance-r.com/

See Also

Useful links:

• http://www.finance-r.com/

Enable active extension portfolios

Description

active.extension adds corresponding long/short constraints for a diverse set of active extension portfolios (e.g. 130/30 portfolios)

Usage

active.extension(model, up = 130, down = 30)

Arguments

Value

portfolio.model with active extension enabled

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Set new alpha of a portfolio.model

Description

alpha sets a new alpha for VaR and Expected Shortfall

Usage

alpha(model, alpha)

Arguments

Value

the adapted portfolio.model

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Examples

data(sp100w17av30s)
model <- optimal.portfolio(scenario.set)
cvar95 <- optimal.portfolio(objective(model, "expected.shortfall"))
cvar90 <- optimal.portfolio(alpha(cvar95, 0.1))

Set portfolio.model default values

Description

aux_portfolio.default sets portfolio.model default values

Usage

aux_portfolio.default(model)

Arguments

Value

a portfolio.model with all default values set

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Convert risk alias names to internal names

Description

aux_risk.alias converts risk alias names to internal names

Usage

aux_risk.alias(risk)

Arguments

Value

the standardized risk name (if any)

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Simulate a multivariate-normal scenario.set

Description

aux_simulate.scenarios simulates a scenario.set given a mean vector and a covariance matrix using mvrnorm of the MASS package

Usage

aux_simulate.scenarios(mu, Sigma, n = 1000, seed = 280277)

Arguments

Value

A scenario set 'simulation' with mean 'mu' and covariance 'Sigma'

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Create or update a vector-based linear equality constraint set

Description

linear.constraint.eq creates a vector-based linear equality constraint: Aeq(range) * factors == beq

Usage

linear.constraint.eq(constraints.linear, range, beq, factors = NULL)

Arguments

Value

the new (updated) set of equality constraints

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Create or update a vector-based linear inequality constraint set

Description

linear.constraint.iq creates a vector-based linear inequality constraint: Aeq(range) * factors <= beq

Usage

linear.constraint.iq(constraints.linear, range, b, factors = NULL,
  leq = TRUE)

Arguments

Value

the new (updated) set of inequality constraints

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Disable active extension portfolios

Description

long.only switches a portfolio.model back to long-only by disabling the active extension

Usage

long.only(model)

Arguments

Value

portfolio.model with active extension disabled

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Set lower bounds on assets

Description

lower.bound sets lower bounds on assets within a portfolio.model

Usage

lower.bound(model, v1 = NULL, v2 = NULL)

Arguments

Value

portfolio.model with new lower bounds

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Set momentum parameters for a portfolio.model

Description

momentum sets a new alpha for VaR and Expected Shortfall

Usage

momentum(model, n_momentum, n_momentum.short = NULL)

Arguments

Value

the adapted portfolio.model

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Set new objective of a portfolio.model

Description

objective sets a new objective for VaR and Expected Shortfall

Usage

objective(model, objective = "markowitz")

Arguments

Value

the adapted portfolio.model

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Examples

data(sp100w17av30s)
model <- portfolio.model(scenario.set)
mad <- optimal.portfolio(objective(model, "mad"))

Meta-function to optimize portfolios given a portfolio.model instance

Description

optimal.portfolio optimizes the portfolio of a model given the current specification

Usage

optimal.portfolio(input = NULL, ...)

p.opt(input = NULL, ...)

opt.p(input = NULL, ...)

Arguments

Value

an S3 object of class portfolio.model with the optimized portfolio.

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Examples

data(sp100w17av30s)
model <- optimal.portfolio(scenario.set)

1 over N portfolio

Description

optimal.portfolio.1overN adds a 1 over N portfolio to the portfolio.model

Usage

optimal.portfolio.1overN(model)

Arguments

Value

the portfolio.model including the newly computed optimal portfolio

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Portfolio Optimization minimizing Conditional Value at Risk (CVaR)

Description

optimal.portfolio.expected.shortfall conducts a Portfolio Optimization minimizing Conditional Value at Risk (CVaR) based on Rockafellar and Uryasev (2001)

Usage

optimal.portfolio.expected.shortfall(model)

Arguments

Value

the portfolio.model including the newly computed optimal portfolio

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Portfolio Optimization minimizing Conditional Value at Risk (CVaR) with active extensions

Description

optimal.portfolio.expected.shortfall.long.short conducts a Portfolio Optimization minimizing Conditional Value at Risk (CVaR) based on Rockafellar and Uryasev (2001) with active extensions

Usage

optimal.portfolio.expected.shortfall.long.short(model)

Arguments

Value

the portfolio.model including the newly computed optimal portfolio

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Portfolio Optimization minimizing MAD

Description

optimal.portfolio.mad conducts a Portfolio Optimization minimizing Mean Absolute Deviation (MAD) based on Konno and Yamazaki (1991)

Usage

optimal.portfolio.mad(model)

Arguments

Value

the portfolio.model including the newly computed optimal portfolio

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Portfolio Optimization minimizing MAD (Active Extension)

Description

optimal.portfolio.mad.long.short conducts a Portfolio Optimization minimizing Mean Absolute Deviation (MAD) based on Konno and Yamazaki (1991) including an active extension

Usage

optimal.portfolio.mad.long.short(model)

Arguments

Value

the portfolio.model including the newly computed optimal portfolio

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Portfolio Optimization minimizing Standard Deviation

Description

portfolio.weights conducts a Portfolio Optimization minimizing Standard Deviation based on Markowitz (1952).

Usage

optimal.portfolio.markowitz(model)

Arguments

Value

the portfolio.model including the newly computed optimal portfolio

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Momentum portfolio including momentum for active extensions

Description

optimal.portfolio.momentum adds a momentum portfolio to the portfolio.model

Usage

optimal.portfolio.momentum(model)

Arguments

Value

the portfolio.model including the newly computed optimal portfolio

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Compute maximum/minimum return portfolio given the constraints

Description

optimal.portfolio.reward computes a maximum/minimum return portfolio given the constraints

Usage

optimal.portfolio.reward(model)

Arguments

Value

the portfolio.model including the newly computed optimal portfolio

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Open a specific portfolio.optimization package tutorial

Description

po.tutorial returns the filename of a specific portfolio.optimization package tutorial. If no tutorial is given or the tutorial is missspelled, a list of available tutorials is printed.

Usage

po.tutorial(tutorial = "")

Arguments

Value

Nothing if no tutorial specified, otherwise the path to the tutorial.

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Examples

## Not run: 
file.edit(po.tutorial("101"))
file.edit(po.tutorial("compare"))

## End(Not run)

Return the loss distribution of the portfolio.model

Description

portfolio.loss return the loss distribution of the portfolio.model

Usage

portfolio.loss(model)

l(model)

Arguments

Value

nothing

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Create a portfolio.model instance (or fix an existing one)

Description

portfolio.model creates a new S3 portfolio.model instance or fixes an existing one.

Usage

portfolio.model(input = NULL)

p.mo(input = NULL)

Arguments

Value

an S3 object of class portfolio.model

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Return the portfolio weights of a portfolio.model

Description

portfolio.weights return the portfolio weights of a portfolio.model

Usage

portfolio.weights(model)

portfolio(model)

w(model)

weights(model)

x(model)

Arguments

Value

a vector of portfolio weights or NULL if no weights are available yet.

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

Examples

data(sp100w17av30s)
portfolio.weights(optimal.portfolio(scenario.set))

Overload print() for portfolio.model

Description

print.portfolio.model overloads print() and outputs some information about the portfolio.model object

Usage

## S3 method for class 'portfolio.model'
print(x, ...)

Arguments

Value

nothing

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance

S&P 100 weekly stock returns 2017

Description

A dataset sp100w17 containing the (crude) weekly returns of (almost) all S&P 100 stocks of 2017, daily basis (101 stocks, 251 returns).

Usage

data(sp100w17)

Format

One xts time series object with 251 rows and 101 columns.

Details

Furthermore contains a vector sp100w17av with the average trading volume of all stocks in 2017 - to be used for a subselection.

S&P 100 average trading volume over the whole year 2017

Description

A vector sp100w17av with the average trading volume of all stocks in 2017 - to be used e.g. for a subselection.

Usage

data(sp100w17)

Format

One named numeric vector of length 101.

S&P 100 weekly stock returns 2017 of 30 stocks with the highest average trading volume over the whole year

Description

A sceario sp100w17 containing the (crude) weekly returns of (almost) all S&P 100 stocks of 2017, daily basis (101 stocks, 251 returns).

Usage

data(sp100w17av30s)

Format

A named matrix scenario.set with 251 rows and 30 columns.

Set upper bounds on assets

Description

upper.bound sets lower bounds on assets within a portfolio.model

Usage

upper.bound(model, v1 = NULL, v2 = NULL)

Arguments

Value

portfolio.model with new upper bounds

Author(s)

Ronald Hochreiter, ronald@algorithmic.finance