example.py

#!/usr/bin/python

# The MIT License (MIT)
#
# Copyright (c) 2015 Christian Zielinski
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULtAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

import numpy as np
import pandas as pd

import portfolioopt as pfopt


def section(caption):
    print('\n\n' + str(caption))
    print('-' * len(caption))


def print_portfolio_info(returns, avg_rets, weights):
    """
    Print information on expected portfolio performance.
    """
    ret = (weights * avg_rets).sum()
    std = (weights * returns).sum(1).std()
    sharpe = ret / std
    print("Optimal weights:\n{}\n".format(weights))
    print("Expected return:   {}".format(ret))
    print("Expected variance: {}".format(std**2))
    print("Expected Sharpe:   {}".format(sharpe))


def main():
    returns, cov_mat, avg_rets = pfopt.create_test_data()
    
    section("Example returns")
    print(returns.head(10))
    print("...")

    section("Average returns")
    print(avg_rets)

    section("Covariance matrix")
    print(cov_mat)

    section("Minimum variance portfolio (long only)")
    weights = pfopt.min_var_portfolio(cov_mat)
    print_portfolio_info(returns, avg_rets, weights)

    section("Minimum variance portfolio (long/short)")
    weights = pfopt.min_var_portfolio(cov_mat, allow_short=True)
    print_portfolio_info(returns, avg_rets, weights)

    # Define some target return, here the 70% quantile of the average returns
    target_ret = avg_rets.quantile(0.7)

    section("Markowitz portfolio (long only, target return: {:.5f})".format(target_ret))
    weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret)
    print_portfolio_info(returns, avg_rets, weights)

    section("Markowitz portfolio (long/short, target return: {:.5f})".format(target_ret))
    weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret, allow_short=True)
    print_portfolio_info(returns, avg_rets, weights)

    section("Markowitz portfolio (market neutral, target return: {:.5f})".format(target_ret))
    weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret, allow_short=True,
                                                                       market_neutral=True)
    print_portfolio_info(returns, avg_rets, weights)

    section("Tangency portfolio (long only)")
    weights = pfopt.tangency_portfolio(cov_mat, avg_rets)
    weights = pfopt.truncate_weights(weights)   # Truncate some tiny weights
    print_portfolio_info(returns, avg_rets, weights)

    section("Tangency portfolio (long/short)")
    weights = pfopt.tangency_portfolio(cov_mat, avg_rets, allow_short=True)
    print_portfolio_info(returns, avg_rets, weights)


if __name__ == '__main__':
    main()