E*TRADE Python API Docs | dltHub

ETRADE's REST API uses OAuth for authorization, and developers need an ETRADE account to request a key. The API allows third-party platforms to access E*TRADE account and market data. To get started, sign the API agreement and complete the Developer Agreement and User Intent Survey. The REST API base URL is Production: https://api.etrade.com/v1, Sandbox: https://apisb.etrade.com/v1 and All requests require OAuth 1.0a credentials (consumer key/secret plus access token/secret) signed with HMAC‑SHA1..

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 E*TRADE data in under 10 minutes.

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What data can I load from E*TRADE?

Here are some of the endpoints you can load from E*TRADE:

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How do I authenticate with the E*TRADE API?

E*TRADE uses OAuth 1.0a (HMAC‑SHA1). Obtain a consumer key/secret and perform the OAuth 1.0a flow (request token → authorize → access token). Include OAuth parameters in the Authorization header.

1. Get your credentials

1. Sign the E*TRADE developer agreement and complete the API user intent survey on developer.etrade.com.
2. Generate a consumer key and secret via the Sandbox key generator or request them through the developer portal (e.g., https://us.etrade.com/etx/ris/apikey).
3. Implement the OAuth 1.0a flow: call the Request Token endpoint, redirect the user to the Authorize Application endpoint, then call the Access Token endpoint with the verifier to obtain the access token and secret.
4. Store consumer_key, consumer_secret, access_token, and access_token_secret in your secrets.toml.

2. Add them to .dlt/secrets.toml

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[sources.etrade_source]
consumer_key = "your_consumer_key"
consumer_secret = "your_consumer_secret"
access_token = "user_access_token"
access_token_secret = "user_access_token_secret"
callback_url = "https://yourapp/callback"

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 E*TRADE 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 etrade_pipeline.py

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

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Pipeline etrade_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset etrade_data
The duckdb destination used duckdb:/etrade.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 etrade_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 list_accounts and get_quotes from the E*TRADE 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 etrade_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Production: https://api.etrade.com/v1, Sandbox: https://apisb.etrade.com/v1",
            "auth": {
                "type": "oauth1",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "list_accounts", "endpoint": {"path": "accounts/list", "data_selector": "accounts"}},
            {"name": "get_quotes", "endpoint": {"path": "market/quote/{symbol}", "data_selector": "quoteData"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="etrade_pipeline",
        destination="duckdb",
        dataset_name="etrade_data",
    )
    load_info = pipeline.run(etrade_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("etrade_pipeline").dataset()
sessions_df = data.list_accounts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM etrade_data.list_accounts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("etrade_pipeline").dataset()
data.list_accounts.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 E*TRADE 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 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