CODEWITHSUNDEEP
pythonpandascombinationsanalyticsportfolio
Migos
Migos

Reputation: 148

Finding combination of columns which provides best combination based on function return

I have a dataframe with daily returns 6 portfolios (PORT1, PORT2, PORT3, ... PORT6).

I have defined functions for compound annual returns and risk-adjusted returns. I can run this function for any one PORT.

I want to find a combination of portfolios (assume equal weighting) to obtain the highest returns. For example, a combination of PORT1, PORT3, PORT4, and PORT6 may provide the highest risk adjusted return. Is there a method to automatically run the defined function on all combinations and obtain the desired combination?

No code is included as I do not think it is necessary to show the computation used to determine risk adj return.

def returns(PORT):
   val = ... [computation of return here for PORT]
   return val

Upvotes: 1

Views: 489

Answers (2)

Acccumulation
Acccumulation

Reputation: 3591

You can do

import itertools
best_return = 0
for r in range(len(PORTS)):
     for PORT in itertools.combinations(PORTS,r):
          cur_return = returns(PORT)
          if cur_return  > best_return :
                 best_return = cur_return
                 best_PORT = PORT

You can also do

  max([max([PORT for PORT in itertools.combinations(PORTS,r)], key = returns)
         for r in range(len(PORTS))], key = returns)

However, this is more of an economics question than a CS one. Given a set of positions and their returns and risk, there are explicit formulae to find the optimal portfolio without having to brute force it.

Upvotes: 1

Thomas Kimber
Thomas Kimber

Reputation: 11107

Finding the optimal location within a multidimensional space is possible, but people have made fortunes figuring out better ways of achieving exactly this.

The problem at the outset is setting out your possibility space. You've six dimensions, and presumably you want to allocate 1 unit of "stuff" across all those six, such that a vector of the allocations {a,b,c,d,e,f} sums to 1. That's still an infinity of numbers, so maybe we only start off with increments of size 0.10. So 10 increments possible, across 6 dimensions, gives you 10^6 possibilities.

So the simple brute-force method would be to "simply" run your function across the entire parameter space, store the values and pick the best one.

That may not be the answer you want, other methods exist, including randomising your guesses and limiting your results to a more manageable number. But the performance gain is offset with some uncertainty - and some potentially difficult conversations with your clients "What do you mean you did it randomly?!".

To make any guesses at what might be optimal, it would be helpful to have an understanding of the response curves each portfolio has under different circumstances and the sorts of risk/reward profiles you might expect them to operate under. Are they linear, quadratic, or are they more complex? If you can model them mathematically, you might be able to use an algorithm to reduce your search space.

Short (but fundamental) answer is "it depends".

Upvotes: 2

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