# Copyright 2016-present CERN – European Organization for Nuclear Research
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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from typing import Union, Sequence
from qf_lib.containers.dataframe.qf_dataframe import QFDataFrame
from qf_lib.containers.series.qf_series import QFSeries
from qf_lib.portfolio_construction.optimizers.quadratic_optimizer import QuadraticOptimizer
from qf_lib.portfolio_construction.portfolio_models.portfolio import Portfolio
[docs]class EfficientFrontierPortfolio(Portfolio):
"""
Class used for constructing a portfolio, for which the weight of assets' mean returns can be adjusted against
the weight of the covariance of assets.
"""
def __init__(self, cov_matrix: QFDataFrame, mean_returns: QFSeries, k: float,
upper_constraint: Union[float, Sequence[float]] = None):
self.upper_constraint = upper_constraint
self.cov_matrix = cov_matrix
self.k = k
self.mean_returns = mean_returns
[docs] def get_weights(self) -> QFSeries:
P = self.cov_matrix.values
q = - self.mean_returns.values * self.k
weights = QuadraticOptimizer.get_optimal_weights(P, q, self.upper_constraint)
return QFSeries(data=weights, index=self.cov_matrix.columns)