FactSet Analytics Python API Docs | dltHub

FactSet Analytics API provides access to portfolio analytics and financial calculation data via a REST interface. The REST API base URL is https://api.factset.com and all requests require a Basic authentication 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 FactSet Analytics data in under 10 minutes.

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What data can I load from FactSet Analytics?

Here are some of the endpoints you can load from FactSet Analytics:

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How do I authenticate with the FactSet Analytics API?

FactSet APIs use Basic HTTP authentication where the Authorization header is set to 'Basic <base64_encoded_username-serial

>'. The username is the FactSet username-serial and the password is the API key.

1. Get your credentials

1. Log in to the FactSet Developer Portal. 2. Navigate to Profile > API Authentication. 3. Click 'Create', select 'API Key', and click 'Next'. 4. Enter the required details (account information and IP address ranges for your requests), then click 'Submit'. 5. Copy the generated API key immediately, as it will not be displayed again; use your FactSet username-serial as the username and this key as the password for HTTP Basic Authentication.

2. Add them to .dlt/secrets.toml

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[sources.factset_analytics_source]
factset_username_serial = "YOUR_USERNAME-SERIAL"
factset_api_key = "YOUR_GENERATED_API_KEY"

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 harness:

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dlthub 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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dlthub 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 FactSet Analytics 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 factset_analytics_pipeline.py

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

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Pipeline factset_analytics_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset factset_analytics_data
The duckdb destination used duckdb:/factset_analytics.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 factset_analytics_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 /analytics/engines/pa/v3/calculations and /analytics/accounts/v3/models from the FactSet Analytics 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 factset_analytics_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.factset.com",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key},
        },
        "resources": [
            {"name": "user_defined_field_groups", "endpoint": {"path": "analytics/fields/v1/user-defined-field-group", "data_selector": "data"}},
            {"name": "analytics_calculations", "endpoint": {"path": "analytics/engines/pa/v3/calculations", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="factset_analytics_pipeline",
        destination="duckdb",
        dataset_name="factset_analytics_data",
    )
    load_info = pipeline.run(factset_analytics_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("factset_analytics_pipeline").dataset()
sessions_df = data.user_defined_field_groups.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM factset_analytics_data.user_defined_field_groups LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("factset_analytics_pipeline").dataset()
data.user_defined_field_groups.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 FactSet Analytics 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 harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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dlthub ai toolkit data-exploration install
dlthub ai toolkit dlthub-platform 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