Nasdaq Data Link (Time Series and Table data)
Nasdaq Data Link is a powerful, centralized, cloud-based technology platform providing access to more than 250 trusted data sets, available via API. Search, discover and build.
Integrate the Nasdaq Data Link (Time Series and Table data) API with the Python API
Setup the Nasdaq Data Link (Time Series and Table data) API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Nasdaq Data Link (Time Series and Table data) and Python remarkably fast. Free for developers.
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Popular Nasdaq Data Link (Time Series and Table data) and Python Actions#
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Overview of Nasdaq Data Link (Time Series and Table data)#
The Nasdaq Data Link API provides access to financial, economic, and alternative data that powers investment decisions, research, and more. Within Pipedream, you can use this API to automate workflows involving time series and table data. This might include fetching stock prices, economic indicators, or data for quantitative analysis. By creating workflows that trigger on schedules or events, you can efficiently process and act upon this data. Combine it with other apps to gain insights, notify stakeholders, or integrate with your databases.
Connect Nasdaq Data Link (Time Series and Table data)#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
nasdaq_data_link_time_series_and_table_data_: {
type: "app",
app: "nasdaq_data_link_time_series_and_table_data_",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://data.nasdaq.com/api/v3/datatables/ETFG/FUND.json`,
params: {
api_key: `${this.nasdaq_data_link_time_series_and_table_data_.$auth.api_key}`,
},
})
},
})
Overview of Python#
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:
Connect Python#
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}}Trusted by 1,000,000+ developers from startups to Fortune 500 companies