The Nobel Prize-winning science of portfolio optimization, available as a Web API.
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
The Portfolio Optimizer API allows you to build advanced financial models and investment strategies directly within Pipedream. With this API, you gain access to portfolio analysis tools that can optimize asset allocations, calculate efficient frontiers, and perform risk assessments. Leverage the power of Pipedream's serverless platform to automate these tasks, integrate with other financial data sources, and much more, facilitating smarter investment decisions and real-time portfolio management.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
portfolio_optimizer: {
type: "app",
app: "portfolio_optimizer",
}
},
async run({ steps, $ }) {
const { data } = await axios({
method: "POST",
url: `https://${this.portfolio_optimizer.$auth.api_server}.portfoliooptimizer.io/v1/factors/residualization`,
headers: {
"Content-type": "application/json",
"X-API-Key": this.portfolio_optimizer.$auth.api_key
},
//Residualizes the returns of the first factor among a set of 2 factors, observed during 3 time periods
data: {
"factors": [
{
"factorReturns": [
0.01,
0.02,
-0.01
]
},
{
"factorReturns": [
0.025,
0.005,
-0.02
]
}
],
"residualizedFactor": 1
},
})
return data
},
})
Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.
This includes installing PyPI packages, within your code without having to manage a requirements.txt
file or running pip
.
Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:
def handler(pd: "pipedream"):
# Reference data from previous steps
print(pd.steps["trigger"]["context"]["id"])
# Return data for use in future steps
return {"foo": {"test":True}}