Load Vertex Protocol data to DuckDB

Vertex Protocol is a decentralized exchange protocol that provides APIs for trading and market data access across multiple blockchain networks. Everything needed to build a working Vertex Protocol → 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 Vertex Protocol 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 Vertex Protocol 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 Vertex Protocol 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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Vertex Protocol API at a glance

These values come from the Vertex Protocol API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Vertex Protocol API?

Vertex Protocol API interactions typically require EIP-712 cryptographic signatures rather than traditional token-based authentication. The SDK manages these signatures internally when a signer (such as a private key or LocalAccount) is provided.

1. Get your credentials

Vertex Protocol does not use traditional API keys or a dashboard for API key generation. Instead, access is authenticated via blockchain-based cryptographic signatures. To obtain credentials, you must possess a private key (for an Ethereum or compatible EVM wallet) that holds an account on the protocol. Your private key acts as your identity and credential for signing requests. Keep this private key highly secure and never share it. In a development environment, you typically store your private key securely as an environment variable (e.g., SIGNER_PRIVATE_KEY) to be accessed by your application.

2. Add them to .dlt/secrets.toml

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[sources.vertex_protocol_source]
SIGNER_PRIVATE_KEY = "0x..."

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 Vertex Protocol data can I load into DuckDB?

These are the Vertex Protocol endpoints dlt can load into DuckDB:

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How do I load only new Vertex Protocol records?

Vertex Protocol exposes start_idx on subaccounts, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "subaccounts", "endpoint": {
    "path": "subaccounts",
    "incremental": {"cursor_path": "start_idx", "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 Vertex Protocol pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /query and /execute (implied through client interaction) from the Vertex Protocol API into DuckDB:

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

@dlt.source
def vertex_protocol_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://gateway.prod.vertexprotocol.com/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "subaccounts", "endpoint": {"path": "subaccounts"}},
            {"name": "interest_and_funding", "endpoint": {"path": "interest_and_funding_payments"}}
        ],
    }
    yield from rest_api_resources(config)


def load_vertex_protocol_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="vertex_protocol_pipeline",
        destination="duckdb",
        dataset_name="vertex_protocol_data",
    )
    load_info = pipeline.run(vertex_protocol_source())
    print(load_info)


if __name__ == "__main__":
    load_vertex_protocol_to_duckdb()

Run it with python vertex_protocol_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 Vertex Protocol 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("vertex_protocol_pipeline").dataset()
df = data.subaccounts.df()
print(df.head())

SQL:

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SELECT * FROM vertex_protocol_data.subaccounts LIMIT 10;

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

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How do I deploy the Vertex Protocol 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 Vertex Protocol 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 Vertex Protocol 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