Opvious Python SDK
Project description
Opvious Python SDK
This package provides a lightweight SDK for solving optimization models with the Opvious API. Its main features are:
- Seamless data import/export via native support for
pandas
- Powerful built-in debugging capabilities: automatic infeasibility relaxation, variable pinning, and more
- Non-blocking APIs for performant parallel calls
Quickstart
First, install this package and have an API access token handy (these can be generated here).
pip install opvious[aio] # aio is recommended for improved performance
With these steps out of the way, you are ready to optimize!
import opvious
client = opvious.Client.from_token(TOKEN)
# Solve a simple portfolio selection optimization model
response = await client.run_solve(
specification=opvious.InlineSpecification([
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,
},
)
print(f"Problem was {response.status}.") # OPTIMAL, INFEASIBLE, ...
if response.outputs: # Present if the problem was feasible
print(response.outputs.variable("allocation"))
The full API reference is available here: https://opvious.readthedocs.io
Next steps
This SDK is focused on solving optimization models. For convenient access to the rest of Opvious' functionality, consider using the TypeScript SDK and CLI.
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