Opvious Python SDK
Project description
Opvious Python SDK
An optimization SDK for solving linear, mixed-integer, and quadratic models
import opvious
client = opvious.Client.from_environment()
# Solve a portfolio selection optimization model
response = await client.run_solve(
specification=opvious.LocalSpecification.inline(
r"""
We find an allocation of assets which minimizes risk while satisfying
a minimum expected return:
+ A collection of assets: $\S^d_{asset}: A$
+ Covariances: $\S^p_{covariance}: c \in \mathbb{R}^{A \times A}$
+ Expected return: $\S^p_{expectedReturn}: m \in \mathbb{R}^A$
+ Minimum desired return: $\S^p_{desiredReturn}: r \in \mathbb{R}$
The only output is the allocation per asset
$\S^v_{allocation}: \alpha \in [0,1]^A$ chosen to minimize risk:
$\S^o_{risk}: \min \sum_{a, b \in A} c_{a,b} \alpha_a \alpha_b$.
Subject to the following constraints:
+ $\S^c_{atLeastMinimumReturn}: \sum_{a \in A} m_a \alpha_a \geq r$
+ $\S^c_{totalAllocation}: \sum_{a \in A} \alpha_a = 1$
"""
),
parameters={
"covariance": {
("AAPL", "AAPL"): 0.08,
# ...
},
"expectedReturn": {
"AAPL": 0.07,
# ..
},
"desiredReturn": 0.05,
},
assert_feasible=True,
)
optimal_allocation = response.outputs.variable("allocation")
Take a look at https://opvious.readthedocs.io for the full documentation or these notebooks to see it in action.
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