Finage Python API Docs | dltHub

Finage is a market data API platform providing real-time and historical financial market data (stocks, Forex, crypto) via REST endpoints. The REST API base URL is https://api.finage.co.uk and all requests require an API key sent as a query parameter (apikey) or as header..

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 Finage data in under 10 minutes.

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What data can I load from Finage?

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

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How do I authenticate with the Finage API?

Finage uses an API key. Common usage shows supplying apikey as a query parameter (apikey=YOUR_API_KEY) in request URLs; some docs and playgrounds also accept an apikey header. Include your API key in every request.

1. Get your credentials

1. Create an account at Finage (https://moon.finage.co.uk/register?subscribe=API00). 2) Log into the Finage dashboard. 3) Navigate to API Keys / API Access to view or generate your API key. 4) Copy the key and place it in requests as apikey query parameter or header per docs.

2. Add them to .dlt/secrets.toml

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[sources.finage_finance_source]
api_key = "your_finage_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 Finage 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 finage_finance_pipeline.py

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

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Pipeline finage_finance_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset finage_finance_data
The duckdb destination used duckdb:/finage_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 finage_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 last_stock and snapshot_stock from the Finage 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 finage_finance_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.finage.co.uk",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "last_stock", "endpoint": {"path": "last/stock/{symbol}"}},
            {"name": "snapshot_stock", "endpoint": {"path": "snapshot/stock", "data_selector": "lastQuotes"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="finage_finance_pipeline",
        destination="duckdb",
        dataset_name="finage_finance_data",
    )
    load_info = pipeline.run(finage_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("finage_finance_pipeline").dataset()
sessions_df = data.last_stock.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM finage_finance_data.last_stock LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("finage_finance_pipeline").dataset()
data.last_stock.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 Finage 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 401/403 or an authentication error, verify you included your API key as apikey=YOUR_API_KEY in the query string (or in the supported header). Ensure the key is active in your Finage dashboard.

Rate limits and quota

Finage enforces request limits depending on your plan. If you receive HTTP 429 Too Many Requests, slow request rate or implement backoff and upgrade plan if needed.

Pagination and large responses

Some endpoints return totalResults and paged arrays (e.g., snapshot endpoints). Use provided query parameters (symbols, limit, offset/time ranges) to reduce result size.

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