https://github.com/cvxgrp/cptopt
Portfolio Optimization with Cumulative Prospect Theory Utility via Convex Optimization
Science Score: 36.0%
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Portfolio Optimization with Cumulative Prospect Theory Utility via Convex Optimization
Basic Info
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- Stars: 33
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- Open Issues: 1
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Metadata Files
README.md
Portfolio Optimization with Cumulative Prospect Theory Utility via Convex Optimization
This repo accompanies our paper.
Installation
The cptopt package can be installed using pip as follows
python
pip install git+https://github.com/cvxgrp/cptopt.git
Minimum working example
We are unable to provide the full data set used in the paper for licensing reasons. We, therefore, give a minimum working example using simulated data below. ```python import numpy as np from scipy.stats import multivariate_normal as normal
from cptopt.optimizer import MinorizationMaximizationOptimizer, ConvexConcaveOptimizer, \ MeanVarianceFrontierOptimizer, GradientOptimizer from cptopt.utility import CPTUtility
Generate returns
corr = np.array([ [1, -.2, -.4], [-.2, 1, .5], [-.4, .5, 1] ]) sd = np.array([.01, .1, .2]) Sigma = np.diag(sd) @ corr @ np.diag(sd)
np.random.seed(0) r = normal.rvs([.03, .1, .193], Sigma, size=100)
Define utility function
utility = CPTUtility( gammapos=8.4, gammaneg=11.4, deltapos=.77, deltaneg=.79 )
initial_weights = np.array([1/3, 1/3, 1/3])
Optimize
mv = MeanVarianceFrontierOptimizer(utility) mv.optimize(r, verbose=True)
mm = MinorizationMaximizationOptimizer(utility) mm.optimize(r, initialweights=initialweights, verbose=True)
cc = ConvexConcaveOptimizer(utility) cc.optimize(r, initialweights=initialweights, verbose=True)
ga = GradientOptimizer(utility)
ga.optimize(r, initialweights=initialweights, verbose=True)
The optimal weights can then be accessed via the `weights` property.
py
mv.weights
mm.weights
cc.weights
ga.weights
```
Citing
If you want to reference our paper in your research, please consider citing us by using the following BibTeX:
BibTeX
@article{luxenberg2024cptopt,
title={Portfolio Optimization with Cumulative Prospect Theory Utility via Convex Optimization},
author={Luxenberg, Eric and Schiele, Philipp and Boyd, Stephen},
journal={Computational Economics},
pages={1--21},
year={2024},
doi = {https://doi.org/10.1007/s10614-024-10556-x},
publisher={Springer},
url = {https://web.stanford.edu/\%7Eboyd/papers/pdf/cpt_opt.pdf},
}
Owner
- Name: Stanford University Convex Optimization Group
- Login: cvxgrp
- Kind: organization
- Location: Stanford, CA
- Website: www.stanford.edu/~boyd
- Repositories: 102
- Profile: https://github.com/cvxgrp
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