SimFin Python API Docs | dltHub

SimFin is a financial data platform and REST API that provides standardized fundamental data (financial statements, share counts, prices and company metadata) for global equities. The REST API base URL is https://simfin.com/api/v1 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 SimFin data in under 10 minutes.

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What data can I load from SimFin?

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

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How do I authenticate with the SimFin API?

SimFin uses a per-user API key included in requests (commonly passed as the api-key query parameter; some client libraries also accept it as a header). Include ?api-key=YOUR_KEY on each request or provide the key via the client library configuration.

1. Get your credentials

1. Register / sign in at https://simfin.com/login or https://app.simfin.com/login.
2. After logging in, navigate to the API / data access page (https://simfin.com/data/access/api or https://simfin.com/data/api).
3. Copy the displayed API key and store it securely.

2. Add them to .dlt/secrets.toml

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[sources.simfin_data_source]
api_key = "your_simfin_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 SimFin 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 simfin_data_pipeline.py

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

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Pipeline simfin_data_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset simfin_data_data
The duckdb destination used duckdb:/simfin_data.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 simfin_data_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 info_find_id_by_ticker and companies_statements_standardised from the SimFin 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 simfin_data_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://simfin.com/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "info_find_id_by_ticker", "endpoint": {"path": "info/find-id/ticker/{ticker}"}},
            {"name": "companies_statements_standardised", "endpoint": {"path": "companies/id/{id}/statements/standardised", "data_selector": "values"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="simfin_data_pipeline",
        destination="duckdb",
        dataset_name="simfin_data_data",
    )
    load_info = pipeline.run(simfin_data_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("simfin_data_pipeline").dataset()
sessions_df = data.companies_statements_standardised.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM simfin_data_data.companies_statements_standardised LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("simfin_data_pipeline").dataset()
data.companies_statements_standardised.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 SimFin 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 "error" field in responses, verify your api-key is correct and included as ?api-key=YOUR_KEY (or configured in the client). Ensure the key has not expired and that you are using the correct account (free vs SimFin+ affects dataset availability).

Rate limits and quotas

SimFin enforces rate limits and dataset access based on free vs SimFin+ subscription. If you hit rate limits, you will see HTTP rate‑limit responses; slow requests or add retry/backoff.

Pagination and large responses

Many endpoints return full arrays; when fetching large date ranges (prices, statements) request smaller time windows or use available query parameters (fyear, ptype, stype) to narrow results. If results are truncated, page by date ranges.

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