https://github.com/vitorebatista/po-avemh-nsga-ii

Solving Portfolio Optimization Problems Using AVEMH (Adaptive Vector Multiobjective Heuristic) and NSGA-II
https://github.com/vitorebatista/po-avemh-nsga-ii

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Solving Portfolio Optimization Problems Using AVEMH (Adaptive Vector Multiobjective Heuristic) and NSGA-II

• Host: GitHub
• URL: https://github.com/vitorebatista/po-avemh-nsga-ii
• Owner: vitorebatista
• License: mit
• Created: 2020-10-28T17:31:05.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2020-12-09T23:47:50.000Z (about 4 years ago)
• Last Synced: 2024-04-14T13:05:05.549Z (9 months ago)
• Language: Python
• Homepage:
• Size: 426 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# [WIP] Portfolio optimisation with AVEMH and NSGA-II

## AVEMH

Adaptive Vector Multiobjective Heuristics is... (wip)

## NSGA-II

NSGA-II is one of the most popular multi objective optimization algorithms with three special characteristics, fast non-dominated sorting approach, fast crowded distance estimation procedure and simple crowded comparison operator

## Instances

There are currently 5 data files of [OR-Library](http://people.brunel.ac.uk/~mastjjb/jeb/orlib/portinfo.html).

These data files are the test problems used in the paper:

Chang, T.-J., Meade, N., Beasley, J.E. and Sharaiha, Y.M.,

"Heuristics for cardinality constrained portfolio optimisation"

Comp. & Opns. Res. 27 (2000) 1271-1302.

The test problems are the files:

port1, port2, ..., port5

The format of these data files is:

number of assets (N)

for each asset i (i=1,...,N):

mean return, standard deviation of return

for all possible pairs of assets:

i, j, correlation between asset i and asset j

The unconstrained efficient frontiers for each of these

data sets are available in the files:

portef1, portef2, ..., portef5

The format of these files is:

for each of the calculated points on the unconstrained frontier:

mean return, variance of return

## Python version and libraries

- Python 3.7.4 are used in the experiments.

- Check alias for python3 and edit `.sh` files. (If the alias is `python3`, change `python` to `python3`. **A better way is to use `pyenv` and set `pyenv global 3.7.4`.**)

- Following libraries are required,

  - numpy

  - scipy

  - pandas

  - matplotlib

  - seaborn

## How to use

1. Open terminal

2. Run `run.sh`

3. Program will create a folder named `tmp` and save simulation results in this folder

4. Enter `process_script`

5. Run file `run.sh` in `process_script` folder

6. Program will create `images`, `num_res` and `report` folders, containing images, metrics and statistical issues, respectively.

## Images

![](./images/image1.png)

![](./images/image2.png)

### Disclamer

One more analysis of new approach used in portfolio optimization. Others can be [found here](https://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132020000100404&tlng=en#c01)

This program was largely inspired by the project [Solving Portfolio Optimization Problems Using MOEA / D and Lévy Flight](https://github.com/Y1fanHE/po_with_moead-levy)