text: #     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
  #
  #     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.
  #     See the License for the specific language governing permissions and
  #     limitations under the License.
  
  from typing import Union, Sequence
  
  from numpy import ndarray
  
  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
  
  
  class MaxExcessReturnPortfolio(Portfolio):
      """
      Class used for constructing a portfolio which is optimized considering its excess return (maximized). Excess
      return is defined as: portfolio volatility - 0.5 * weighted variance of individual assets.
      """
  
      def __init__(self, cov_matrix: QFDataFrame, variance_of_assets: QFSeries,
                   upper_constraint: Union[float, Sequence[float]] = None):
          self.cov_matrix = cov_matrix
          self.variance_of_assets = variance_of_assets
          self.upper_constraint = upper_constraint
  
      def get_weights(self) -> QFSeries:
          P = self.cov_matrix.values
          q = -0.5 * self.variance_of_assets.values  # type: ndarray
          weights = QuadraticOptimizer.get_optimal_weights(P, q, upper_constraints=self.upper_constraint)
  
          return QFSeries(data=weights, index=self.cov_matrix.columns)
  