Tradovate Python API Docs | dltHub

The Tradovate API provides RESTful services for accessing trading data, including positions, contracts, and market information. It uses OpenAPI definitions and supports streaming data via websockets. Documentation and testing tools are available on their official site. The REST API base URL is https://demo.tradovateapi.com/v1 (use https://live.tradovateapi.com/v1 for live; md and websocket domains: https://md.tradovateapi.com and wss://live.tradovateapi.com/v1/websocket) and All requests require a Bearer access token (accessToken) in the Authorization 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 Tradovate data in under 10 minutes.

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What data can I load from Tradovate?

Here are some of the endpoints you can load from Tradovate:

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How do I authenticate with the Tradovate API?

Obtain an access token via POST /auth/accessTokenRequest (or OAuth /auth/oauthtoken); include Authorization: Bearer on subsequent GET/POST calls. Access tokens include accessToken, mdAccessToken and expirationTime in the JSON response.

1. Get your credentials

1. Sign up for a Tradovate demo or live account. 2) If using client credentials with 2FA, obtain a cid (client id) and sec (secret) from Tradovate (or use appId/appVersion/deviceId). 3) Call POST /auth/accessTokenRequest with JSON body {name,password,appId,appVersion,cid,deviceId,sec} to receive accessToken. 4) For OAuth, follow Tradovate OAuth example (github.com/tradovate/example-api-oauth).

2. Add them to .dlt/secrets.toml

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[sources.tradovate_source]
access_token = "your_access_token_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 Tradovate 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 tradovate_pipeline.py

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

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Pipeline tradovate_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset tradovate_data
The duckdb destination used duckdb:/tradovate.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 tradovate_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 account_list and position_list from the Tradovate 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 tradovate_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://demo.tradovateapi.com/v1 (use https://live.tradovateapi.com/v1 for live; md and websocket domains: https://md.tradovateapi.com and wss://live.tradovateapi.com/v1/websocket)",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "account_list", "endpoint": {"path": "account/list"}},
            {"name": "position_list", "endpoint": {"path": "position/list"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="tradovate_pipeline",
        destination="duckdb",
        dataset_name="tradovate_data",
    )
    load_info = pipeline.run(tradovate_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("tradovate_pipeline").dataset()
sessions_df = data.account_list.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM tradovate_data.account_list LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("tradovate_pipeline").dataset()
data.account_list.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 Tradovate 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