Twelve Data Python API Docs | dltHub

Twelve Data is a financial market data API that provides real-time and historical stock, forex, cryptocurrency, technical indicators, and fundamentals data. The REST API base URL is https://api.twelvedata.com and All requests require an API key (query param or 'Authorization: apikey ')..

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 Twelve Data data in under 10 minutes.

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What data can I load from Twelve Data?

Here are some of the endpoints you can load from Twelve Data:

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How do I authenticate with the Twelve Data API?

Twelve Data authenticates requests with an API key. You may pass the key as the 'apikey' query parameter or using the HTTP header 'Authorization: apikey YOUR_KEY' (header method is recommended). A demo key 'apikey=demo' is available for testing.

1. Get your credentials

1. Sign up at https://twelvedata.com and log into your account dashboard; 2) Navigate to API Keys or Profile -> API Keys; 3) Copy the provided API key (or create a new key if allowed by your plan); 4) Use that key as the 'apikey' query parameter or set header 'Authorization: apikey <YOUR_KEY>'.

2. Add them to .dlt/secrets.toml

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[sources.twelve_data_source]
api_key = "your_api_key_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 Twelve Data 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 twelve_data_pipeline.py

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

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Pipeline twelve_data_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset twelve_data_data
The duckdb destination used duckdb:/twelve_data.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 twelve_data_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 time_series and quote from the Twelve Data 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 twelve_data_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.twelvedata.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "time_series", "endpoint": {"path": "time_series", "data_selector": "values"}},
            {"name": "cryptocurrencies", "endpoint": {"path": "cryptocurrencies", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="twelve_data_pipeline",
        destination="duckdb",
        dataset_name="twelve_data_data",
    )
    load_info = pipeline.run(twelve_data_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("twelve_data_pipeline").dataset()
sessions_df = data.time_series.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM twelve_data_data.time_series LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("twelve_data_pipeline").dataset()
data.time_series.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 Twelve Data 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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Troubleshooting

Authentication failures

If you receive a 401/403 or an error indicating invalid API key, verify you use a valid API key and pass it as either 'apikey' query parameter or 'Authorization: apikey ' header. The demo key 'apikey=demo' has limited access.

Rate limits and throttling

Twelve Data enforces rate limits per plan. You can get HTTP 429 Too Many Requests when exceeding limits. Implement exponential backoff retries, respect dashboard limits, and cache frequent queries. Use batch endpoint to reduce per-request overhead.

Pagination / output size

Time series endpoints support 'outputsize' and explicit start_date/end_date. Default outputsize is 30 when date params are omitted; maximum per request is documented (e.g., up to 5000). For large ranges, page by date ranges or use multiple requests.

Batch requests and partial failures

When using /batch, individual requests may fail without affecting others; inspect the returned 'data' map for per-request error objects.

Common error format

Errors are returned as JSON objects with 'code', 'message', and 'status' keys. Handle non-200 responses and parse these fields to present user-friendly errors.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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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