Using optimize.portfolio
Thank you Alexios. That is very helpful.
On Sun, Jun 7, 2020 at 8:44 PM alexios galanos <alexios at 4dscape.com> wrote:
Roger,
The portfolio.optim function calculates the minimum risk portfolio given
a desired level of portfolio return (the default pm input is mean(x)).
Here is an example versus parma:
LB <- rep(0, ncol(returns))
UB <- rep(1, ncol(returns))
popt <- portfolio.optim(x = returns, reslow = LB, reshigh = UB)
library(parma)
# optimal reward to risk (covariance matrix)
parmspec <- parmaspec(scenario = returns, risk = "EV", forecast =
colMeans(returns), riskType = "minrisk", LB = LB, UB = UB, target =
mean(returns))
parm <- parmasolve(parmspec)
round(cbind(weights(parm), popt$pw), 3)
[,1] [,2]
MSFT 0.253 0.253
AAPL 0.208 0.208
AMZN 0.055 0.055
NVDA 0.020 0.020
CSCO 0.000 0.000
ADBE 0.033 0.033
AMGN 0.253 0.253
ORCL 0.002 0.002
QCOM 0.000 0.000
GILD 0.177 0.177
Exact same results.
Regards,
Alexios
On 6/7/20 5:14 PM, Roger Bos wrote:
Thank you to everyone for your suggestions, but I am still having
trouble.
I see that there are two ways to pass custom moments into optimize portfolio, a custom function and using momentargs list. The example code at the end of this email uses both of those methods, as well as the
default
method. I also compare those to portfolio.optim and parma. I get quite different results for each method. I know that they will not be exactly the same, but surely I am doing something wrong given the results I am getting. I would hope that all three optimize portfolio methods would give me the same results. Here are the results I am
getting:
opt.portf opt.portf.fun opt.portf.args portfolio.optim
parma
MSFT 0.372 0.190 0.448 0.253 0.000 AAPL 0.008 0.094 0.044 0.208 0.357 AMZN 0.000 0.076 0.000 0.055 0.119 NVDA 0.020 0.000 0.000 0.020 0.169 CSCO 0.000 0.004 0.002 0.000 0.000 ADBE 0.004 0.040 0.002 0.033 0.032 AMGN 0.298 0.348 0.382 0.253 0.000 ORCL 0.068 0.072 0.010 0.002 0.000 QCOM 0.072 0.000 0.000 0.000 0.000 GILD 0.158 0.176 0.112 0.177 0.322 opt.portf is optimize.portfolio with internal mu and sigma opt.portf.fun is optimize.portfolio with mu and sigma provided in
momentFUN
opt.portf.args is optimize.portfolio with mu and sigma provided in momentargs So if optimize portfolio just uses the column means for mu and cov for sigma, why am I getting different results than when I use a custom
function
or pass in the moments? Obviously I am doing something wrong since I get
different results when using momentFUN and momentargs. Thanks in advance
for any help, Roger.
###
library(tidyquant)
symbols <-
c("MSFT","AAPL","AMZN","NVDA","CSCO","ADBE","AMGN","ORCL","QCOM","GILD")
getYahooReturns <- function(symbols, return_column = "Ad") {
returns <- list()
for (symbol in symbols) {
getSymbols(symbol, from = '2000-01-01', adjustOHLC = TRUE, env =
.GlobalEnv, auto.assign = TRUE)
return <- Return.calculate(Ad(get(symbol)))
colnames(return) <- gsub("\\.Adjusted", "", colnames(return))
returns[[symbol]] <- return
}
returns <- do.call(cbind, returns)
return(returns)
}
returns <- getYahooReturns(symbols)
returns <- returns[-1, ]
returns[is.na(returns)] <- 0
# portfolio.optim from tseries package
library(tseries)
LB <- rep(0, ncol(returns))
UB <- rep(1, ncol(returns))
popt <- portfolio.optim(x = returns, covmat = sigma, reslow = LB,
reshigh =
UB)
library(parma)
# optimal reward to risk (covariance matrix)
parmspec <- parmaspec(S = cov(returns), risk = "EV", forecast =
colMeans(returns), riskType = "optimal", LB = LB, UB = UB)
parm <- parmasolve(parmspec)
library(PortfolioAnalytics)
simple.moments <- function(R, ...) {
num_assets = ncol(R)
out <- list()
out$mu <- matrix(colMeans(R), ncol = 1)
out$sigma <- cov(R)
# out$m3 <- PerformanceAnalytics:::M3.MM(R)
# out$m4 <- PerformanceAnalytics:::M4.MM(R)
out$m3 <- matrix(0, nrow = num_assets, ncol = num_assets^2)
out$m4 <- matrix(0, nrow = num_assets, ncol = num_assets^3)
out
}
num_assets = ncol(returns)
momentargs <- list()
momentargs$mu <- matrix(colMeans(returns), ncol = 1)
momentargs$sigma <- cov(returns)
momentargs$m3 <- matrix(0, nrow = num_assets, ncol = num_assets^2)
momentargs$m4 <- matrix(0, nrow = num_assets, ncol = num_assets^3)
pspec <- portfolio.spec(assets = symbols)
pspec <- add.constraint(portfolio=pspec, type="box", min = 0, max = 1,
min_sum = 0.99, max_sum = 1.01)
pspec <- add.objective(portfolio=pspec, type="return", name="mean")
pspec <- add.objective(portfolio=pspec, type="risk", name="var")
opt <- optimize.portfolio(R = returns, portfolio = pspec,
optimize_method = "DEoptim")
opt.fun <- optimize.portfolio(R = returns, portfolio = pspec,
optimize_method = "DEoptim", momentFUN = "simple.moments")
opt.args <- optimize.portfolio(R = returns, portfolio = pspec,
optimize_method = "DEoptim", momentargs = momentargs)
data.frame(opt.portf = opt$weights,
opt.portf.fun = opt.fun$weights,
opt.portf.args = opt.args$weights,
portfolio.optim = round(popt$pw, 3),
parma = round(weights(parm), 3))
On Sat, Jun 6, 2020 at 8:38 AM Brian G. Peterson <brian at braverock.com>
wrote:
On Sat, 2020-06-06 at 14:33 +0200, Enrico Schumann wrote:
On Fri, 05 Jun 2020, Roger Bos writes:
All,
I am comparing optimize.portfolio from the PortfolioAnalytics
package to
portfolio.optim from the tseries package. portfolio.optim seems a
bit
easier to use, but I like the set up of optimize.portfolio. I have
created
a minimal reprex below that compares the output of both in case
that helps
answer my questions.
Here are my two primary questions:
1) What if I wanted to pass a custom covariance matrix to
optimize.portfolio, like from a risk model. Is that possible? I
can pass
it to portfolio.optim because covmat is one of the parameters.
2) What if I wanted to pass forecasted returns to
optimize.portfolio? How
would that be done.
If there is anything that can be improved in this example, that
would be
helpful as well. Thank you in advance for any assistance, Roger.
###
library(PortfolioAnalytics)
library(tidyquant)
symbols <-
c("MSFT","AAPL","AMZN","NVDA","CSCO","ADBE","AMGN","ORCL","QCOM","G
ILD")
getYahooReturns <- function(symbols, return_column = "Ad") {
returns <- list()
for (symbol in symbols) {
getSymbols(symbol, from = '2000-01-01', adjustOHLC = TRUE, env
=
.GlobalEnv, auto.assign = TRUE)
return <- Return.calculate(Ad(get(symbol)))
colnames(return) <- gsub("\\.Adjusted", "", colnames(return))
returns[[symbol]] <- return
}
returns <- do.call(cbind, returns)
return(returns)
}
returns <- getYahooReturns(symbols)
returns <- returns[-1, ]
returns[is.na(returns)] <- 0
# portfolio.optim from tseries package
library(tseries)
sigma <- cov(returns)
reslow <- rep(0, ncol(returns))
reshigh <- rep(1, ncol(returns))
popt <- portfolio.optim(x = returns, covmat = sigma, reslow =
reslow,
reshigh = reshigh)
popt$pw
pspec <- portfolio.spec(assets = symbols)
pspec <- add.constraint(portfolio=pspec, type="box",
min = 0, max = 1, min_sum = 0.99, max_sum =
1.01)
pspec <- add.objective(portfolio=pspec,
type="return",
name="mean")
pspec <- add.objective(portfolio=pspec,
type="risk",
name="var")
opt <- optimize.portfolio(R = returns,
portfolio = pspec,
optimize_method = "DEoptim", )
data.frame(optimize.portfolio = opt$weights, portfolio.optim =
round(popt$pw, 3))
If all else fails, and supposing that 'PortfolioAnalytics' per default computes means and covariances in the standard way, you could create input data (time series) that have exactly the desired covariances and means:
per default, PortfolioAnalytics uses sample moments as most users would expect. As I already told the OP, the user may pass mu and sigma and m3 and m4 directly, or may construct custom moment functions to compute the moments using any method they choose. This is outlined in section 2 of the vignette:
and, of course, in the manual.
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