A Python Package for Portfolio Optimization using the Critical Line Algorithm

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Deep Learning PyCLA
Overview

PyCLA

A Python Package for Portfolio Optimization using the Critical Line Algorithm

Getting started

To use PyCLA, clone the repo and install the required dependencies.

git clone https://github.com/phschiele/PyCLA

Dependency installation using poetry:

poetry install

Dependency installation using pip:

pip install -r requirements.txt

Using PyCLA - An Example

import numpy as np

from pycla import PyCLA

n_sec = 10

# Expected returns and covariance matrix
np.random.seed(1)
mu = np.random.random(n_sec)
random_mat = np.random.rand(n_sec, n_sec)
C = np.dot(random_mat, random_mat.transpose())

# Lower and upper bounds
lb = np.ones(n_sec) * 0.05
ub = np.ones(n_sec) * 0.7

# Equality constraints
A = np.ones((1, n_sec))
b = np.array([1])

# Inequality constraints
first_n = 5  # combined weight of first five assets <= 50%
A_in = np.array([[1]*first_n + [0]*(n_sec-first_n)])
b_in = np.array([0.5])

# Create the PyCLA object and trace the frontier
pycla = PyCLA(mu, C, A, b, A_in, b_in, lb, ub)
pycla.trace_frontier()
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Comments
  • Fix unimport linter repo URL.

    Fix unimport linter repo URL.

    Thank you for using Unimport, there was a slight change in the URL, the old URL works because it is redirected to the new one, but I changed it to the new one just in case.

    opened by hakancelikdev 1
  • Weights given volatility target

    Weights given volatility target

    Hi,

    Thank you for developing this package. I was playing a bit with it and I am wondering how to increase the number of points computed to draw the efficient frontier? With my input, only 9 points are computed, which is too low to achieve my goal of finding the optimal weights for a given volatility target (let say 10%). Is there a way to achieve what I am looking for?

    opened by HedgeShot 2
Owner
Scalable Capital | Ludwig Maximilian University of Munich
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