MarketStack Python API Docs | dltHub

MarketStack is a RESTful JSON API that provides global stock market data including real-time, intraday, and historical end-of-day (EOD) prices, tickers, exchanges, splits, dividends, currencies and related market metadata. The REST API base URL is https://api.marketstack.com/v2 and all requests require an API access key supplied via a 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 MarketStack data in under 10 minutes.

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What data can I load from MarketStack?

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

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How do I authenticate with the MarketStack API?

The API uses an API key (access key). Include your key in every request as the access_key query parameter, e.g. ?access_key=YOUR_KEY. No special headers are needed.

1. Get your credentials

1. Sign up or log in at https://marketstack.com (or the apilayer account portal). 2) Open your account dashboard and navigate to the API Access / Keys section. 3) Create a new access key or copy the existing MarketStack "access key" shown. 4) If the key is ever compromised, rotate or reset it from the same dashboard.

2. Add them to .dlt/secrets.toml

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[sources.market_stack_source]
api_key = "YOUR_MARKETSTACK_ACCESS_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.

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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 MarketStack 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 market_stack_pipeline.py

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

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Pipeline market_stack_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset market_stack_data
The duckdb destination used duckdb:/market_stack.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 market_stack_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 eod and tickers from the MarketStack 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 market_stack_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.marketstack.com/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "eod", "endpoint": {"path": "eod", "data_selector": "data"}},
            {"name": "tickers", "endpoint": {"path": "tickers", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="market_stack_pipeline",
        destination="duckdb",
        dataset_name="market_stack_data",
    )
    load_info = pipeline.run(market_stack_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("market_stack_pipeline").dataset()
sessions_df = data.eod.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM market_stack_data.eod LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("market_stack_pipeline").dataset()
data.eod.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 MarketStack 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 Unauthorized or an error indicating an invalid API key, verify that the access_key query parameter is included and that the key is active in your dashboard.

Rate limits and quota

The free tier is limited to 5 requests per second; paid plans increase the monthly quota. A 429 Too Many Requests response includes error codes such as too_many_requests or rate_limit_reached. Implement exponential back‑off and use limit/offset for pagination.

Endpoint access restrictions

Some endpoints (e.g., intraday intervals shorter than 15 minutes or real‑time tick data) are only available on higher‑level plans. A 403 response with function_access_restricted or https_access_restricted indicates a plan limitation.

Pagination and data selector notes

List endpoints return an object containing a top‑level data array and a pagination object (limit, offset, count, total). Use limit (max 1000) and offset to page through results.

Common error response format

Errors are returned under a top‑level error object:

{
  "error": {
    "code": "validation_error",
    "message": "Request failed with validation error",
    "context": { ... }
  }
}

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