Optimizers ¶

Contents

Optimizers
- Comparison of aopt and nopt
- Examples

Comparison of aopt and nopt ¶

If your portfolio optimization problem can be expressed as a combination of linear equality constraints and soft equality constraints, then use aopt. The analytical optimizers is much faster and is guaranteed (by math, not by meva) to give the optimal portfolio.

If, however, you need to specify separately the sum of the short and long positions in your portfolio or have some other constraints that aopt does not handle, then use nopt.

attribute	aopt	nopt
fast	$\checkmark$
guaranteed optimal	$\checkmark$
linear constraints	$\checkmark$
soft constraints	$\checkmark$	$\checkmark$
sum longs, shorts		$\checkmark$
position limits		$\checkmark$
turnover limit		$\checkmark$
inequality constraint		$\checkmark$
transaction cost		$\checkmark$

The meva benchmark suite:

>>> meva.bench(nasset=300, nfactor=4, nrepeat=5)
Meva performance benchmark
    Meva 0.0.2dev
    Mean wall clock time in seconds
    nassets=300, nfactor=4, nrepeat=5

  aopt     nopt
  0.0023   0.7002    sum 1
           0.8057    longs sum 1, shorts -1
           0.8027    longs sum 1, shorts -1; linear transaction costs
           0.0551    longs sum 1, shorts -1; position limit +- 2/nasset
           0.1255    longs sum 1, shorts -1; position limit +- 4/nasset

Examples ¶