Coinbase Prime Python API Docs | dltHub

Coinbase Prime is an institutional-grade platform for managing cryptocurrency trading, custody, and portfolio operations. The REST API base URL is https://api.prime.coinbase.com/v1 and all requests require custom HMAC-signed headers.

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 add "dlt[hub]" and start loading Coinbase Prime data in under 10 minutes.

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What data can I load from Coinbase Prime?

Here are some of the endpoints you can load from Coinbase Prime:

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How do I authenticate with the Coinbase Prime API?

All requests require HMAC SHA-256 signature authentication via custom HTTP headers: X-CB-ACCESS-KEY, X-CB-ACCESS-PASSPHRASE, X-CB-ACCESS-SIGNATURE, and X-CB-ACCESS-TIMESTAMP. The signature is generated by HMAC SHA-256 hashing a concatenated string of the timestamp, method, path, and request body with the secret key, then base64-encoding the result.

1. Get your credentials

1. Sign in to your Coinbase Prime account. 2. Navigate to the lower-left corner and click the Gear (Settings) icon. 3. Select 'APIs'. 4. Click 'Create API Key'. 5. Enter the API name, access type, and expiration date, then click Continue. 6. Follow the on-screen prompts to verify your identity (usually via YubiKey) and perform any required consensus approvals. 7. Once the status shows as 'Pending', find your key under 'Pending Keys' and click 'Activate Key' to finalize. Note: Keep your API key, secret key, and passphrase secure as they will be required for authentication.

2. Add them to .dlt/secrets.toml

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[sources.coinbase_prime_source]
access_key = "your_access_key"
passphrase = "your_passphrase"
signing_key = "your_signing_key"
portfolio_id = "your_portfolio_id"

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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Coinbase Prime 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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uv run python coinbase_prime_pipeline.py

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

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Pipeline coinbase_prime_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset coinbase_prime_data
The duckdb destination used duckdb:/coinbase_prime.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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uv run dlthub 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 /v1/portfolios and /v1/accounts from the Coinbase Prime 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 coinbase_prime_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.prime.coinbase.com/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "portfolios", "endpoint": {"path": "v1/portfolios"}},
            {"name": "order_fills", "endpoint": {"path": "v1/portfolios/{portfolio_id}/orders/{order_id}/fills"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="coinbase_prime_pipeline",
        destination="duckdb",
        dataset_name="coinbase_prime_data",
    )
    load_info = pipeline.run(coinbase_prime_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("coinbase_prime_pipeline").dataset()
sessions_df = data.portfolios.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM coinbase_prime_data.portfolios LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("coinbase_prime_pipeline").dataset()
data.portfolios.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 Coinbase Prime 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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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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