exercise_1.py

# Copyright 2021 D-Wave Systems Inc.
#
# 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.

# TODO: Import any required packages here


import utilities


def define_variables(stockcodes):
    """Define the variables to be used for the CQM.
    Args:
        stockcodes (list): List of stocks under consideration
    
    Returns:
        stocks (list): 
            List of variables named 's_{stk}' for each stock stk in stockcodes, where stk is replaced by the stock code.
    """

    # TODO: Define your list of variables and call it stocks
    ## Hint: Remember to import the required package at the top of the file for Binary variables

    return stocks

def define_cqm(stocks, num_stocks_to_buy, returns):
    """Define a CQM for the exercise. 
    Requirements:
        Objective: Maximize returns
        Constraints:
            - Choose exactly num_stocks_to_buy stocks
            
    Args:
        stocks (list):
            List of variables named 's_{stk}' for each stock in stockcodes
        num_stocks_to_buy (int): Number of stocks to purchase
        returns (list):
            List of average monthly returns for each stock in stocks
                where returns[i] is the average returns for stocks[i]

        
    Returns:
        cqm (ConstrainedQuadraticModel)
    """

    # TODO: Initialize the ConstrainedQuadraticModel called cqm
    ## Hint: Remember to import the required package at the top of the file for ConstrainedQuadraticModels
    

    # TODO: Add a constraint to choose exactly num_stocks_to_buy stocks
    ## Important: Use the label 'choose k stocks', this label is case sensitive


    # TODO: Add an objective function maximize returns
    ## Hint: Use the information in returns, and remember to convert to minimization


    return cqm

def sample_cqm(cqm):

    # TODO: Define your sampler as LeapHybridCQMSampler
    ## Hint: Remember to import the required package at the top of the file


    # TODO: Sample the ConstrainedQuadraticModel cqm and store the result in sampleset


    return sampleset


if __name__ == '__main__':

    # 10 stocks used in this program
    stockcodes=["T", "SFL", "PFE", "XOM", "MO", "VZ", "IBM", "TSLA", "GILD", "GE"]

    # Compute relevant statistics like price, average returns, and covariance
    price, returns, variance = utilities.get_stock_info()

    # Number of stocks to buy
    num_stocks_to_buy = 2

    # Add binary variables for stocks
    stocks = define_variables(stockcodes)

    # Build CQM
    cqm = define_cqm(stocks, num_stocks_to_buy, returns)

    # Run CQM on hybrid solver
    sampleset = sample_cqm(cqm)
    
    # Process and print solution
    print("\nPart 1 solution:\n")
    utilities.process_sampleset(sampleset, stockcodes)