Opvious Python SDK
Project description
Opvious Python SDK
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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
# Instantiate an API client from an API token
client = opvious.Client.from_token(TOKEN)
# Solve a simple portfolio selection optimization model
outputs = await client.solve(
sources=[
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 the optimal allocation, if any
if isinstance(outputs.outcome, opvious.FeasibleOutcome):
print(outputs.data.variable("allocation"))
else:
print(f"Problem was {outputs.status}.") # INFEASIBLE, UNBOUNDED
Environments
Clients are compatible with Pyodide environments, for example JupyterLite
kernels. Simply install the package as usual in a notebook, omitting the aio
optional dependencies:
import piplite
await piplite.install('opvious')
In other environments, prefer using the aiohttp
-powered clients as they are
more performant (this is the default if the aio
dependencies were specified).
Next steps
This SDK is focused on solving optimization models. For convenient access to the rest of Opvious API's functionality, consider using the TypeScript SDK and CLI.
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