Alpha Vantage Python API Docs | dltHub

Alpha Vantage is a free API providing real‑time and historical market data, forex, cryptocurrency and technical indicators. The REST API base URL is https://www.alphavantage.co/query and All requests require an API key passed as the apikey query parameter..

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 Alpha Vantage data in under 10 minutes.

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What data can I load from Alpha Vantage?

Here are some of the endpoints you can load from Alpha Vantage:

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How do I authenticate with the Alpha Vantage API?

Authentication is performed by including the API key as a query parameter named apikey on every request; no additional headers are needed.

1. Get your credentials

1. Visit https://www.alphavantage.co.
2. Click “Get Your Free API Key”.
3. Fill in the registration form (name, email, password).
4. Submit the form; the dashboard will display your API key.
5. Copy the key and store it securely for use in dlt.

2. Add them to .dlt/secrets.toml

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[sources.alpha_vantage_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 Alpha Vantage 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 alpha_vantage_pipeline.py

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

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Pipeline alpha_vantage_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset alpha_vantage_data
The duckdb destination used duckdb:/alpha_vantage.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 alpha_vantage_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 global_quote and symbol_search from the Alpha Vantage 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 alpha_vantage_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.alphavantage.co/query",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "global_quote", "endpoint": {"path": "query", "data_selector": "Global Quote"}},
            {"name": "symbol_search", "endpoint": {"path": "query", "data_selector": "bestMatches"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="alpha_vantage_pipeline",
        destination="duckdb",
        dataset_name="alpha_vantage_data",
    )
    load_info = pipeline.run(alpha_vantage_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("alpha_vantage_pipeline").dataset()
sessions_df = data.global_quote.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM alpha_vantage_data.global_quote LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("alpha_vantage_pipeline").dataset()
data.global_quote.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 Alpha Vantage 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 Errors

If the apikey is missing or invalid, the API returns a JSON object with an Error Message field, e.g. { "Error Message": "Invalid API key" }.

Rate Limits

Free tier usage is limited to 25 requests per day (as stated on the support page). When the limit is exceeded the API responds with a Note field, e.g. { "Note": "Thank you for using Alpha Vantage! Our standard API call frequency is 5 calls per minute and 500 calls per day" } or HTTP 429.

Data Format Issues

If an unsupported datatype is requested, the response includes an Error Message indicating the problem.

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