Alpaca Trade API Python API Docs | dltHub

Build a Alpaca Trade API-to-database pipeline in Python using dlt with AI Workbench support for Claude Code, Cursor, and Codex.

Last updated: Mar 13, 2026

The Alpaca Trade API is a Python library for commission-free trading, supporting REST and streaming data. It enables rapid development of trading algorithms. The latest documentation is available at https://docs.alpaca.markets/. The REST API base URL is Live trading: https://api.alpaca.markets; Paper trading: https://paper-api.alpaca.markets and All requests require API Key ID and Secret Key passed in request 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 pip install "dlt[workspace]" and start loading Alpaca Trade API data in under 10 minutes.

What data can I load from Alpaca Trade API?

Here are some of the endpoints you can load from Alpaca Trade API:

Resource	Endpoint	Method	Data selector	Description
assets	/v2/assets	GET	(top-level array)	Returns the list of assets available on Alpaca.
account	/v2/account	GET	(object)	Returns account information for the authenticated account.
positions	/v2/positions	GET	(top-level array)	Returns current positions in the account (or /v2/positions/{symbol}).
orders	/v2/orders	GET	(top-level array)	Returns orders for the account (query params for status/date range available).
activities	/v2/account/activities	GET	(top-level array)	Returns account activity history (trades, dividends, fills, etc.).
clock	/v2/clock	GET	(object)	Returns market clock (is_open, next_open, next_close, timestamp).
calendar	/v2/calendar	GET	(top-level array)	Returns market calendar entries.
account_configurations	/v2/account/configurations	GET	(object)	Returns account configurations.

How do I authenticate with the Alpaca Trade API API?

Alpaca uses API Key ID and API Secret Key for authentication. Include headers APCA-API-KEY-ID: <YOUR_KEY_ID> and APCA-API-SECRET-KEY: <YOUR_SECRET_KEY> on every request. For paper environment use the paper-api host.

1. Get your credentials

Sign in to your Alpaca account dashboard (https://app.alpaca.markets) or create an account. 2. Navigate to the API Keys section (typically under Settings or API). 3. Create/generate a new API Key (choose paper or live keys as needed). 4. Copy the API Key ID and Secret Key and store them securely. Secret Key is shown only once.

2. Add them to .dlt/secrets.toml


[sources.alpaca_trade_api_source]
api_key = "YOUR_API_KEY_ID"
api_secret = "YOUR_API_SECRET_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.

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:


uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:


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:


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 Alpaca Trade API 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:


python alpaca_trade_api_pipeline.py

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


Pipeline alpaca_trade_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset alpaca_trade_api_data
The duckdb destination used duckdb:/alpaca_trade_api.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:


dlt pipeline alpaca_trade_api_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.

Python pipeline example

This example loads assets and orders from the Alpaca Trade API API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:


import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def alpaca_trade_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Live trading: https://api.alpaca.markets; Paper trading: https://paper-api.alpaca.markets",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "assets", "endpoint": {"path": "v2/assets"}},
            {"name": "orders", "endpoint": {"path": "v2/orders"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="alpaca_trade_api_pipeline",
        destination="duckdb",
        dataset_name="alpaca_trade_api_data",
    )
    load_info = pipeline.run(alpaca_trade_api_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.

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):


import dlt

data = dlt.pipeline("alpaca_trade_api_pipeline").dataset()
sessions_df = data.assets.df()
print(sessions_df.head())

SQL (DuckDB example):


SELECT * FROM alpaca_trade_api_data.assets LIMIT 10;

In a marimo or Jupyter notebook:


import dlt

data = dlt.pipeline("alpaca_trade_api_pipeline").dataset()
data.assets.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

What destinations can I load Alpaca Trade API data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Destination	Example value
DuckDB (local, default)	`"duckdb"`
PostgreSQL	`"postgres"`
BigQuery	`"bigquery"`
Snowflake	`"snowflake"`
Redshift	`"redshift"`
Databricks	`"databricks"`
Filesystem (S3, GCS, Azure)	`"filesystem"`

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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.


dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

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