WatchData Polygon API Python API Docs | dltHub

The WatchData Polygon API follows JSON-RPC standards and supports methods for developing on Polygon's mainnet. It provides real-time technical analysis data for blockchain applications. The API is ideal for large-scale Ethereum projects. The REST API base URL is https://polygon.api.watchdata.io/node/jsonrpc and All requests require an API key (created in the WatchData dashboard); use the dashboard "HTTP Link" (API key embedded) or include your API key with requests..

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 WatchData Polygon API data in under 10 minutes.

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What data can I load from WatchData Polygon API?

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

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How do I authenticate with the WatchData Polygon API API?

WatchData uses per-account API keys created in the dashboard. The dashboard provides an "HTTP Link" (chain URL with the API key substituted) for quick use; you can also supply your API key with each request as required by your integration (dashboard shows the HTTP Link for the selected chain).

1. Get your credentials

1. Register / sign in at https://dashboard.watchdata.io/login. 2) In your dashboard click 'Create API key'. 3) Name your key and select the chain (Polygon). 4) Copy the generated API key or the provided HTTP Link (URL with API key substituted) for use in requests.

2. Add them to .dlt/secrets.toml

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[sources.watchdata_polygon_api_source]
api_key = "your_watchdata_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 WatchData Polygon API 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 watchdata_polygon_api_pipeline.py

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

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Pipeline watchdata_polygon_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset watchdata_polygon_api_data
The duckdb destination used duckdb:/watchdata_polygon_api.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 watchdata_polygon_api_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 eth_getLogs and eth_getBlockByNumber from the WatchData Polygon API 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 watchdata_polygon_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://polygon.api.watchdata.io/node/jsonrpc",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "eth_get_logs", "endpoint": {"path": "node/jsonrpc (eth_getLogs)", "data_selector": "result"}},
            {"name": "eth_get_block_by_number", "endpoint": {"path": "node/jsonrpc (eth_getBlockByNumber)", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="watchdata_polygon_api_pipeline",
        destination="duckdb",
        dataset_name="watchdata_polygon_api_data",
    )
    load_info = pipeline.run(watchdata_polygon_api_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("watchdata_polygon_api_pipeline").dataset()
sessions_df = data.eth_get_logs.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM watchdata_polygon_api_data.eth_get_logs LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("watchdata_polygon_api_pipeline").dataset()
data.eth_get_logs.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 WatchData Polygon API 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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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