[R-SIG-Finance] Using optimize.portfolio
Roger Bos
roger@bo@ @end|ng |rom gm@||@com
Mon Jun 8 13:17:47 CEST 2020
Thank you Brian and Alexios. In case anyone is interested, here is the
code that produces the exact same results for all five methods:
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 = "minrisk", LB = LB, UB = UB, target =
mean(returns))
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, use = "pairwise.complete.obs")
# 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, use = "pairwise.complete.obs")
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")
pspec <- add.constraint(portfolio=pspec, type = "return", return_target =
mean(returns))
opt <- optimize.portfolio(R = returns, portfolio = pspec,
optimize_method = "ROI")
opt.fun <- optimize.portfolio(R = returns, portfolio = pspec,
optimize_method = "ROI", momentFUN = "simple.moments")
opt.args <- optimize.portfolio(R = returns, portfolio = pspec,
optimize_method = "ROI", momentargs = momentargs)
data.frame(opt.portf = round(opt$weights, 3),
opt.portf.fun = round(opt.fun$weights, 3),
opt.portf.args = round(opt.args$weights, 3),
portfolio.optim = round(popt$pw, 3),
parma = round(weights(parm), 3))
On Sun, Jun 7, 2020 at 8:59 PM Brian G. Peterson <brian using braverock.com>
wrote:
> Roger,
>
> If no Return cleaning method is specified, the default portfolio moments
> function will use pairwise complete observations:
>
> cov(tmpR, use = "pairwise.complete.obs")
>
> if you pass momentargs, the internal calculations for mu and sigma will be
> replaced by momentargs$mu and momentargs$sigma
>
> Alexios has already pointed out the return target, which is not specified
> in your objectives for optimize.portfolio.
>
> I also note that you're using DEoptim, which is unecessarily slow (and may
> not always converge to the same result) for a simple mean variance
> optimization. You probably want optimize.method='ROI', which will use the
> same direct quadratic approach. DEoptim (or 'random' or 'GEnSA', or 'pso')
> make sense for more complex objectives that aren't amenable to convex
> solvers.
>
> Regards,
>
> Brian
>
>
>
> --
>
> Brian G. Peterson
> ph: +1.773.459.4973
> im: bgpbraverock
>
> On Sun, 2020-06-07 at 20:14 -0400, 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 using 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:
>
>
>
>
> https://stackoverflow.com/questions/58293991/how-to-use-fportfolio-package-in-r-for-non-time-series-input/58302451#58302451
>
>
>
>
> 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:
>
>
>
> https://cran.r-project.org/web/packages/PortfolioAnalytics/vignettes/custom_moments_objectives.pdf
>
>
>
>
> and, of course, in the manual.
>
>
>
>
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>
>
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>
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>
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