Quick start

Installation

Install using pip

pip install pyoptimum

Hello World

Try the following code

from pyoptimum import Client
username = 'demo@optimize.vicbee.net'
password = 'optimize'
client = Client(username, password)

to create a client object then call the pyoptimum.Client.call() to solve a small portfolio problem

Q = [[0.01, 0.001],[0.001, 0.02]]
r = [0.1, 0.3]
mu = 0.3
data = {'Q': Q, 'r': r, 'mu': mu}
response = client.call('portfolio', data)

in which Q is the model’s covariance matrix, r is the vector of expected returns, and mu the portfolio desired return. The response object response contains the optimized portfolio:

response = {
    'obj': 0.019999999998211167,
    'status': 'optimal',
    'x': [-5.532633539658773e-11, 0.9999999999580456]
}

in which obj is the portfolio variance, and x the optimal portfolio weights. The returned portfolio is “optimal,” as shown in status.

Basic error handling

Running the code below

Q = [[0.01, 0.001],[0.001, 0.02]]
r = [0.1, 0.3, 0.4]
mu = 0.3
data = {'Q': Q, 'r': r, 'mu': mu}
response = client.call('portfolio', data)

will raise the exception:

Traceback (most recent call last):
  ...
  File ".../pyoptimum/__init__.py", line 115, in call
    raise PyOptimumException(self.detail)
pyoptimum.PyOptimumException: 'Q' must be an array of size 3 x 3
    Dimensions: Q[2, 2]!=[3, 3], r[3]==[3]

The details of the exception can be retrieved from the client:

print(client.detail)

to produce:

'Q' must be an array of size 3 x 3
Dimensions: Q[2, 2]!=[3, 3], r[3]==[3]

which indicates a mismatch between the dimension of the covariance matrix Q and the vector of expected returns r.

Asynchronous Client

pyoptimum.AsyncClient is an an asynchronous version of pyoptimum.Client with basically the same interface and some additional features.

from pyoptimum import AsyncClient
client = AsyncClient(username, password)

one can then handle calls asynchronously as in

response = await client.call('portfolio', data)

Besides the automatic token renewal feature, the pyoptimum.AsyncClient:call() will also poll the APIs for asynchronous resources when computations are deferred. See this case study for more details on the architecture.

Models and Portfolio

The classes pyoptimum.portfolio.Portfolio and pyoptimum.model.Model implement additional functionality that further simplifies working with portfolios and mean variance models. Details are provided in the Reference section.

Learn more

• Checkout the detailed documentation in Reference.

• See Optimize API and Models API for a complete API documentation.

• Checkout vicbee.net and run the following jupiter notebook

  

  for more details on the features of the API.