Financial Modeling Prep Python API Docs | dltHub

Financial Modeling Prep is a financial-data API platform that provides real-time and historical market data, company financial statements, profiles, and other financial datasets. The REST API base URL is https://financialmodelingprep.com/stable/ and all requests require an API key (query parameter or 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 Financial Modeling Prep data in under 10 minutes.

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What data can I load from Financial Modeling Prep?

Here are some of the endpoints you can load from Financial Modeling Prep:

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How do I authenticate with the Financial Modeling Prep API?

Authenticate by adding your API key either as a query parameter ?apikey=YOUR_API_KEY or as a request header apikey: YOUR_API_KEY on every request.

1. Get your credentials

1. Sign up / log in at https://site.financialmodelingprep.com. 2) Open Dashboard → API Keys (or Developer → API Keys). 3) Copy an existing key or click to create a new API key. 4) Use that key in requests as ?apikey=YOUR_API_KEY or header apikey: YOUR_API_KEY.

2. Add them to .dlt/secrets.toml

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[sources.financial_modeling_prep_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 Financial Modeling Prep 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 financial_modeling_prep_pipeline.py

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

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Pipeline financial_modeling_prep_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset financial_modeling_prep_data
The duckdb destination used duckdb:/financial_modeling_prep.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 financial_modeling_prep_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 quote and income_statement from the Financial Modeling Prep 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 financial_modeling_prep_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://financialmodelingprep.com/stable/",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "quote", "endpoint": {"path": "quote?symbol={symbol}"}},
            {"name": "income_statement", "endpoint": {"path": "income-statement?symbol={symbol}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="financial_modeling_prep_pipeline",
        destination="duckdb",
        dataset_name="financial_modeling_prep_data",
    )
    load_info = pipeline.run(financial_modeling_prep_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("financial_modeling_prep_pipeline").dataset()
sessions_df = data.income_statement.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM financial_modeling_prep_data.income_statement LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("financial_modeling_prep_pipeline").dataset()
data.income_statement.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 Financial Modeling Prep 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 403 Forbidden or responses indicating "Invalid or missing API key", verify your apikey is correct and included on every request either as ?apikey=YOUR_API_KEY or header apikey: YOUR_API_KEY. Ensure you are using the correct key from Dashboard and it hasn’t been revoked.

Rate limits (429)

The API enforces rate limits (free tier example: up to 250 requests/day; higher tiers increase limits). If you receive HTTP 429 Too Many Requests, reduce request frequency, add exponential backoff, or upgrade your plan. Check your plan limits in the Dashboard.

Pagination and large results

Many statement endpoints return arrays of historical records (annual and quarterly). Use endpoint parameters like limit, page or date-range (where supported) to control result size. For bulk endpoints use dedicated bulk endpoints (e.g., balance-sheet-statement-bulk) to fetch large datasets by year/period.

Common server errors (5xx)

HTTP 500 indicates internal server errors; retry with backoff and check API Status page. If persistent, contact FMP support with request details and timestamp.

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