Portfolio Optimizer¶

The PortfolioOptimizer inherits the BaseOptimizer to add several convenience methods. These methods include common optimization programs which would be tedious to craft with the BaseOptimizer over and over again. Of course, as an extension, it can do anything that the BaseOptimizer can. However, if that’s the goal, it would be better to stick with the BaseOptimizer to reduce confusion when reading the code.

Using the PortfolioOptimizer assumes that there is a returns stream from which all other asset classes are benchmarked against. This is the first index in the assets axis.

For example, if you have a benchmark (beta) returns stream, 9 other asset classes over 10000 trials and 40 periods, the simulation tensor will be 40 x 10000 x 10 with the first asset axis being the returns of the benchmark. In such a case, the active portfolio optimizer can be used to optimize the portfolio relative to the benchmark.

The PortfolioOptimizer houses the following convenience methods:

maximize_returns: Maximize the returns of the portfolio. You may put in volatility or CVaR constraints for this procedure.
minimize_volatility: Minimizes the total portfolio volatility
minimize_cvar: Minimizes the conditional value at risk (expected shortfall of the portfolio)
maximize_sharpe_ratio: Maximizes the Sharpe ratio of the portfolio.

class allopy.optimize.PortfolioOptimizer(data, algorithm=40, cvar_data=None, rebalance=False, time_unit='quarterly', sum_to_1=True, *args, **kwargs)[source]¶

__init__(data, algorithm=40, cvar_data=None, rebalance=False, time_unit='quarterly', sum_to_1=True, *args, **kwargs)[source]¶

PortfolioOptimizer houses several common pre-specified optimization routines.

PortfolioOptimizer assumes that the optimization model has no uncertainty. That is, the portfolio is expected to undergo a single fixed scenario in the future. By default, the PortfolioOptimizer will automatically add an equality constraint that forces the portfolio weights to sum to 1.

Parameters

data ({ndarray, OptData}) – The data used for optimization
algorithm ({int, string}) – The algorithm used for optimization. Default is Sequential Least Squares Programming
cvar_data ({ndarray, OptData}) – The cvar_data data used as constraint during the optimization. If this is not set, will default to being a copy of the original data that is trimmed to the first 3 years. If an array like object is passed in, the data must be a 3D array with axis representing time, trials and assets respectively. In that instance, the horizon will not be cut at 3 years, rather it’ll be left to the user.
rebalance (bool, optional) – Whether the weights are rebalanced in every time instance. Defaults to False
time_unit ({int, 'monthly', 'quarterly', 'semi-annually', 'yearly'}, optional) – Specifies how many units (first axis) is required to represent a year. For example, if each time period represents a month, set this to 12. If quarterly, set to 4. Defaults to 12 which means 1 period represents a month. Alternatively, specify one of ‘monthly’, ‘quarterly’, ‘semi-annually’ or ‘yearly’
sum_to_1 – If True, portfolio weights must sum to 1.
args – other arguments to pass to the BaseOptimizer
kwargs – other keyword arguments to pass into OptData (if you passed in a numpy array for data) or into the BaseOptimizer