Polygon Python API Docs | dltHub

Polygon is a platform offering market data and blockchain APIs for accessing financial and on‑chain information. The REST API base URL is https://api.polygon.io and All requests require an API key (api_key) for authentication..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Polygon data in under 10 minutes.

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What data can I load from Polygon?

Here are some of the endpoints you can load from Polygon:

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How do I authenticate with the Polygon API?

Polygon.io accepts the API key via the apiKey query parameter or Authorization header; Tatum uses an x-api-key header; Polygon Research requires the apikey query parameter.

1. Get your credentials

1. Visit the provider's website (polygon.io, tatum.io, or polygon-rest-api.herokuapp.com) and sign up for an account. 2) After email verification, log in and navigate to the API Keys or Dashboard section. 3) Copy the generated API key. 4) For Polygon Research, email api@polygonresearch.com to request an API key as described in the documentation.

2. Add them to .dlt/secrets.toml

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[sources.polygon_finance_source]
api_key = "your_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Polygon API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python polygon_finance_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline polygon_finance_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset polygon_finance_data
The duckdb destination used duckdb:/polygon_finance.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline polygon_finance_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads tickers and transactions from the Polygon API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def polygon_finance_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.polygon.io",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "tickers", "endpoint": {"path": "v3/reference/tickers", "data_selector": "results"}},
            {"name": "transactions", "endpoint": {"path": "v3/transactions/{transaction_hash}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="polygon_finance_pipeline",
        destination="duckdb",
        dataset_name="polygon_finance_data",
    )
    load_info = pipeline.run(polygon_finance_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("polygon_finance_pipeline").dataset()
sessions_df = data.tickers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM polygon_finance_data.tickers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("polygon_finance_pipeline").dataset()
data.tickers.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Polygon data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401 or 403 responses, verify that your API key is correct and sent in the expected location (Polygon.io: apiKey query parameter or header; Tatum: x-api-key header; Polygon Research: ?apikey= query). Incorrect or missing keys cause this error.

Rate limits

When the request volume exceeds the plan limits, the API returns HTTP 429. Check your dashboard for quota details and implement exponential back‑off or retry logic.

Pagination

Many GET endpoints return a top‑level results array and include pagination fields such as next_url or a cursor token. Use limit/offset or the provided cursor to retrieve subsequent pages.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack