Dollar neutral (and general case)

Task

Optimize the trade given various utilities and some simple constraints for the case where the net value is close to zero.

For the general case, the only difference is that the net value is not constrained to be near zero.

Preparation

  • vector of asset prices
  • vector of expected returns
  • variance matrix for the assets
  • current portfolio (if it exists)
  • Portfolio Probe

You need the prices at which assets trade, and a variance matrix of the asset returns.  You also need an expected return for each asset in the universe.

The holdings of the current portfolio need to be in a vector with names that are the asset identifiers.

You also need to have the Portfolio Probe package loaded into your R session:

require(PortfolioProbe)

If you don’t have Portfolio Probe, see “Demo or Buy”.

Doing the example

You need to have the package loaded into your R session:

require(pprobeData)

Doing it

We’ll do a few optimizations:

  • Maximize the information ratio
  • Maximize a mean-variance utility
  • Maximize expected return given a maximum volatility

Preliminaries

The inputs we need in order to get our optimal portfolio are:

  • vector of prices at which the assets may be traded
  • variance matrix of the asset returns
  • vector of expected returns
  • desired value of the new portfolio
  • appropriate constraints
  • current portfolio (optional)

prices

We start by naming the vector of prices that we want to use:

priceVector <- xassetPrices[251,]

These are the prices at the close of the last trading day of 2006. The first few values are:

> head(priceVector)
 XA101  XA103  XA105  XA107  XA108  XA111 
 33.56  72.25  74.39 192.06   5.91  15.98

The requirement for the prices is that it be a vector of positive numbers with names (that are the asset identifiers).

expected returns

We can get the MACD signal value for the same time point as the prices to use as the expected returns:

> expRet <- xaMACD[251,] / 100
> head(expRet)
       XA101        XA103        XA105 
 0.009178291  0.009114743  0.011844481 
       XA107        XA108        XA111 
 0.008825741  0.014268342 -0.003350204

current portfolio

We create an object to serve as the current portfolio:

dnCur <- (1:10) * 1000
names(dnCur) <- head(names(priceVector), 10)
dnCur[c(4,5,7,9)] <- -dnCur[c(4,5,7,9)]

What is expected is a numeric vector of the number of units of each asset in the portfolio. The names of the vector are the identifiers of the assets that are used in the price vector and the variance matrix.

> dnCur
XA101 XA103 XA105 XA107 XA108 XA111 XA113 XA115 
 1000  2000  3000 -4000 -5000  6000 -7000  8000 
XA120 XA126 
-9000 10000

portfolio value

We can see the valuation of the current portfolio with:

> valuation(dnCur, priceVector)$total
  gross     net    long   short 
3013430  -45170 1484130 1529300

We can see more clearly by formatting the numbers:

> format(valuation(dnCur, priceVector)$total, 
+    nsmall=2, big.mark=",")
         gross            net           long 
"3,013,430.00" "  -45,170.00" "1,484,130.00" 
         short 
"1,529,300.00"

Suppose that the net asset value and the desired gearing means that we want the gross value to be about $2,900,000.

> dnGross <- 2.9e6
> dnGross
[1] 2900000

Suppose also that we want the net value to be within $20,000 of zero:

> dnNet <- c(-20000, 20000)
> dnNet
[1] -20000  20000

Optimization: information ratio

We’re now ready to do an optimization. The only constraint that we impose besides the gross and net values is that no more than 10 assets may be in the portfolio.

dnMaxInfo <- trade.optimizer(priceVector, 
   variance=xaLWvar06, expected.return=expRet,
   existing=dnCur, gross=dnGross, net=dnNet, 
   port.size=10)

We are not specifying what utility to use, so it will use the default which is to maximize the information ratio.

Optimization: mean-variance utility

If we are using a mean-variance utility, we need to decide what risk aversion to use.

dnMeanVar <- trade.optimizer(priceVector, 
   variance=xaLWvar06, expected.return=expRet, 
   existing=dnCur, gross=dnGross, net=dnNet, 
   port.size=10, utility="mean-variance", 
   risk.aversion=10)

We’ve added two arguments to the previous optimization.  We use the utility argument to state the form of utility to use, and we use the risk.aversion argument to state the risk aversion that we want to use.

Note that the form of the utility is: expected return minus risk aversion times variance.  Some have a one-half in the last term.

Optimization: maximum return with volatility constraint

An often convenient form of optimization is to constrain the volatility to some maximum value and then maximize the expected return.  Here we constrain volatility to 5%.

dnMaxRetVC <- trade.optimizer(priceVector, 
   variance=xaLWvar06, expected.return=expRet, 
   existing=dnCur, gross=dnGross, net=dnNet, 
   port.size=10, utility="maximum return", 
   var.constraint=.05^2/252)

We need to translate our 5% volatility into the scale of the variance, which is daily.

Print results

The resulting (first) object is printed like:

> dnMaxInfo
$new.portfolio
 XA238  XA385  XA481  XA576  XA678  XA699  XA715 
  1676  -2972   6793  -6881  11318  -5698   4786 
 XA814  XA893  XA952 
 -6367  12097 -16065 

$trade
 XA101  XA103  XA105  XA107  XA108  XA111  XA113 
 -1000  -2000  -3000   4000   5000  -6000   7000 
 XA115  XA120  XA126  XA238  XA385  XA481  XA576 
 -8000   9000 -10000   1676  -2972   6793  -6881 
 XA678  XA699  XA715  XA814  XA893  XA952 
 11318  -5698   4786  -6367  12097 -16065 

$results
objective   negutil      cost   penalty 
-6.673668 -6.673668  0.000000  0.000000 

$converged
[1] TRUE

$objective.utility
[1] "information ratio"

$alpha.values
        A0 
0.02634241 

$var.values
          V0 
1.558052e-05 

$utility.values
[1] -6.673668

$existing
XA101 XA103 XA105 XA107 XA108 XA111 XA113 XA115 
 1000  2000  3000 -4000 -5000  6000 -7000  8000 
XA120 XA126 
-9000 10000 

$violated
NULL

$timestamp
[1] "Thu Sep 27 11:22:23 2012"
[2] "Thu Sep 27 11:22:43 2012"

$call
trade.optimizer(prices = priceVector, variance = xaLWvar06, expected.return = expRet, 
    existing = dnCur, gross = dnGross, net = dnNet, port.size = 10)

The first two components are the new (optimal) portfolio and the trade to achieve that. There are some additional components to the object that are not shown.

Explanation

Optimization strategy

The optimization with the volatility constraint is the easiest to do in practice.  This is because we don’t need the variance and the expected returns to be on the same scale.  We merely need to decide what (expected) volatility we are willing to tolerate.

The mean-variance formulation assumes either that the variance and expected returns are matched in scale, or that the risk aversion takes the mismatch into account.  Maximizing the information ratio assumes that the scales are matched.

Technical details

portfolio value

It is mandatory that the value of the resulting portfolio be specified. For long-short portfolios the most likely specification is to use both gross.value and net.value.  These are both ranges.

We gave only one number for the gross value in the examples.  When only one number is given, then that is taken to be the maximum and a slightly smaller number is used as the minimum.  It is advised to always give a range for net.value (though net.value=0 will do something semi-reasonable).

other output components

One component of the output to pay special attention to is ‘violated‘ — this states which constraints, if any, are violated. You want this to be NULL.

It is probably not important whether ‘converged‘ is TRUE or FALSE. The optimization is likely to be good enough with or without convergence.

Further Details

You can see more about the optimization with the summary of the object:

> summary(dnMaxInfo)
$results
objective   negutil      cost   penalty 
-6.673668 -6.673668  0.000000  0.000000 

$objective.utility
[1] "information ratio"

$alpha.values
        A0 
0.02634241 

$var.values
          V0 
1.558052e-05 

$number.of.assets
         existing             trade 
               10                20 
              new              open 
               10                10 
            close    universe.total 
               10               350 
         tradable   select.universe 
              350               350 
positions.notrade 
                0 

$opening.positions
 [1] "XA238" "XA385" "XA481" "XA576" "XA678"
 [6] "XA699" "XA715" "XA814" "XA893" "XA952"

$closing.positions
 [1] "XA101" "XA103" "XA105" "XA107" "XA108"
 [6] "XA111" "XA113" "XA115" "XA120" "XA126"

$value.limits
        lower   upper
gross 2899710 2900000
net    -20000   20000
long  1439855 1460000
short 1439855 1460000

$valuation.new.portfolio
     gross        net       long      short 
2899973.18   19999.76 1459986.47 1439986.71 

$valuation.trade
     gross        net       long      short 
5913403.18   65169.76 2989286.47 2924116.71 

$valuation.trade.fraction.of.gross
     gross        net       long      short 
2.03912340 0.02247254 1.03079797 1.00832543

This has some pieces that are also in the print method, but new information as well. We see that all of the current portfolio was sold off — a trade to make the broker happy.

Troubleshooting

  • The variance matrix needs to contain all of the assets that are in the price vector. It can have additional assets — these will be ignored. The order of the assets in the variance does not matter.
  • All of the prices need to be in the same currency. You have to check that — the code has no way of knowing.
  • It will still work if the object given as the prices is a one-column or one-row matrix. But it will complain about other matrices.  The same is true for expected returns.

See also

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