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with Token Metrics and Python?
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Popular Token Metrics and Python Actions#
Retrieve AI-generated reports providing comprehensive analyses of cryptocurrency tokens, including deep dives, investment analyses, and code reviews. See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Get the Top 10 and Bottom 10 correlation of tokens with the top 100 market cap tokens. See the documentation
Get the latest list of crypto investors and their scores. See the documentation
Get daily OHLCV (Open, High, Low, Close, Volume) data for tokens. See the documentation
Overview of Token Metrics#
The Token Metrics API integration for Pipedream provides comprehensive access to cryptocurrency data, analytics, and AI-powered insights. This component enables seamless integration of advanced token metrics, market analysis, and trading intelligence into your automated workflows.
Token Metrics delivers institutional-grade cryptocurrency analytics, including AI-generated reports, trading signals, fundamental and technical analysis, market metrics, and performance data across thousands of digital assets.
Connect Token Metrics#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
token_metrics: {
type: "app",
app: "token_metrics",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.tokenmetrics.com/v2/tokens`,
headers: {
"accept": `application/json`,
"x-api-key": `${this.token_metrics.$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