Load TAAPI.IO data to DuckDB

TAAPI.IO is a REST and WebSocket API that provides technical indicator values for crypto and stock market data. Everything needed to build a working TAAPI.IO → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your TAAPI.IO to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from TAAPI.IO to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the TAAPI.IO API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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TAAPI.IO API at a glance

These values come from the TAAPI.IO API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the TAAPI.IO API?

TAAPI.IO v2 uses Bearer token authentication, which requires the 'Authorization' header to be set with the format 'Bearer <YOUR_API_KEY>'.

1. Get your credentials

1. Navigate to the TAAPI.IO website and sign up for an account. 2. Once registered, log into your account dashboard. 3. Locate the section for API keys, where you can view or click the 'Generate a new API key' button if one was not automatically provided via email. 4. Copy the generated API key for use in your requests. Note that it may take up to 5 minutes for a newly generated key to become active.

2. Add them to .dlt/secrets.toml

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[sources.taapi_io_source]
taapi_api_key = "your_api_key_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What TAAPI.IO data can I load into DuckDB?

These are the TAAPI.IO endpoints dlt can load into DuckDB:

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How do I load only new TAAPI.IO records?

The TAAPI.IO API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "indicator", "endpoint": {
    "path": "indicator/{name}",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated TAAPI.IO pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /indicator/{name} and /bulk from the TAAPI.IO API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def taapi_io_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://v2.taapi.io",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "indicator", "endpoint": {"path": "indicator/{name}"}},
            {"name": "candles", "endpoint": {"path": "candles"}}
        ],
    }
    yield from rest_api_resources(config)


def load_taapi_io_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="taapi_io_pipeline",
        destination="duckdb",
        dataset_name="taapi_io_data",
    )
    load_info = pipeline.run(taapi_io_source())
    print(load_info)


if __name__ == "__main__":
    load_taapi_io_to_duckdb()

Run it with python taapi_io_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query TAAPI.IO data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("taapi_io_pipeline").dataset()
df = data.indicator.df()
print(df.head())

SQL:

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SELECT * FROM taapi_io_data.indicator LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the TAAPI.IO to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw TAAPI.IO loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load TAAPI.IO data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack