Finnworlds Python API Docs | dltHub

Finnworlds is a provider of financial data APIs delivering real-time stock prices, company financial statements, SEC filings, and other market/financial datasets in JSON or XML. The REST API base URL is https://api.finnworlds.com/api/v1 and all requests require an API key passed as a query parameter (key).

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 Finnworlds data in under 10 minutes.

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What data can I load from Finnworlds?

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

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How do I authenticate with the Finnworlds API?

Authentication is performed using a per‑account API key which must be supplied as the key query parameter (e.g. ?key=YOUR-KEY) on all requests.

1. Get your credentials

1. Visit https://finnworlds.com/pricing or https://finnworlds.com/documentation/ and sign up for an account or contact sales.
2. After signup or purchase, log into the Finnworlds dashboard.
3. Navigate to the API / Developer or API Keys section and generate a new API key.
4. Copy the provided key and use it as the value for the key query parameter in requests.

2. Add them to .dlt/secrets.toml

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[sources.finnworlds_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 Finnworlds 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 finnworlds_finance_pipeline.py

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

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Pipeline finnworlds_finance_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset finnworlds_finance_data
The duckdb destination used duckdb:/finnworlds_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 finnworlds_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 real_time_stock_prices and sec_filings from the Finnworlds 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 finnworlds_finance_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.finnworlds.com/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "real_time_stock_prices", "endpoint": {"path": "real-time-stock-price", "data_selector": "results"}},
            {"name": "sec_filings", "endpoint": {"path": "secfilings", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="finnworlds_finance_pipeline",
        destination="duckdb",
        dataset_name="finnworlds_finance_data",
    )
    load_info = pipeline.run(finnworlds_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("finnworlds_finance_pipeline").dataset()
sessions_df = data.real_time_stock_prices.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM finnworlds_finance_data.real_time_stock_prices LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("finnworlds_finance_pipeline").dataset()
data.real_time_stock_prices.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 Finnworlds 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 get 401 or responses indicating missing/invalid key, ensure the API key is included as the key query parameter (e.g. ?key=YOUR-KEY). Check for accidental whitespace or truncation of the key.

Rate limits and call‑count multipliers

Finnworlds uses API call count multipliers for certain APIs; an endpoint may multiply each request’s usage count. Monitor your usage in the dashboard and contact support if you hit limits.

Pagination and large responses

Most documented examples return a top‑level status object and a results array. For large result sets use available filters (ticker, year, form type, etc.) to narrow queries. If paging parameters exist on an endpoint, use them; otherwise filter by date or other parameters.

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