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Load OneTick data to DuckDB

Build a OneTick to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the OneTick API base URL, auth, endpoints, and incremental loading.

SourceOneTickOneTick API DocumentationDestinationDuckDBIn-process analytical database. The default local destination for dlt pipelines.

OneTick Cloud provides HTTPS REST access to market data and analytics queries for capital markets data. Everything needed to build a working OneTick → 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.


Build your OneTick to DuckDB pipeline

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

Prompt
Run uvx dlthub-init@latest to build a pipeline from OneTick 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 OneTick 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.


OneTick API at a glance

Base URLhttps://rest.cloud.onetick.com
Example endpointPOST /
AuthenticationAll requests require an OAuth2 client-credentials Bearer token — sent in the Authorization header, prefixed Bearer
Also requiredContent-Type
PaginationNot paginated
API referencehttps://www.onetick.com/cloud-services/rest-api-end-point-documentation

These values come from the OneTick API reference — the authoritative source if anything here looks out of date.


How do I authenticate with the OneTick API?

Requests are authenticated using an OAuth2 Bearer token passed in the Authorization header. The token is obtained by providing a client ID and secret to the OneTick Keycloak realm.

1. Get your credentials

To obtain your API credentials, navigate to the OneTick Cloud dashboard at https://authdash.cloud.onetick.com/web_dashboard/. After logging in, verify your account/email as required, then locate the section dedicated to profile or credentials to retrieve your Client ID and Client Secret.

2. Add them to .dlt/secrets.toml

[sources.onetick_source] OTP_CLIENT_ID = "your_client_id_here" OTP_CLIENT_SECRET = "your_client_secret_here" OTP_HTTP_ADDRESS = "https://rest.cloud.onetick.com" OTP_ACCESS_TOKEN_URL = "https://cloud-auth.parent.onetick.com/realms/OMD/protocol/openid-connect/token"

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.


What OneTick data can I load into DuckDB?

These are the OneTick endpoints dlt can load into DuckDB:

ResourceEndpointMethodData selectorDescription
oauth_tokenrealms/OMD/protocol/openid-connect/tokenPOSTaccess_tokenObtain OAuth2 token via client-credentials grant
rest_query/POSTMain entry point for SQL and graph queries; response format depends on parameters
api_documentation/docs/GETRoot of the interactive per-endpoint reference
token_introspection/realms/OMD/protocol/openid-connect/token/introspectPOSTIntrospect OAuth2 tokens
health_check/healthGETBasic service health and connectivity check

How do I load only new OneTick records?

The OneTick 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.

{"name": "rest_query", "endpoint": { "path": "/", # 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.


What does the generated OneTick pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading otq.run and otq.get_access_token from the OneTick API into DuckDB:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def onetick_source(client_secret=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://rest.cloud.onetick.com", "auth": {"type": "bearer", "token": client_secret}, }, "resources": [ {"name": "rest_query", "endpoint": {"path": "/"}}, {"name": "oauth_token", "endpoint": {"path": "realms/OMD/protocol/openid-connect/token", "data_selector": "access_token"}} ], } yield from rest_api_resources(config) def load_onetick_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="onetick_pipeline", destination="duckdb", dataset_name="onetick_data", ) load_info = pipeline.run(onetick_source()) print(load_info) if __name__ == "__main__": load_onetick_to_duckdb()

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


How do I query OneTick 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):

import dlt data = dlt.pipeline("onetick_pipeline").dataset() df = data.rest_query.df() print(df.head())

SQL:

SELECT * FROM onetick_data.rest_query LIMIT 10;

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


How do I deploy the OneTick 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 OneTick loads into governed, documented models.
  • Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →


What other destinations can I load OneTick data to?

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

DestinationExample value
PostgreSQL"postgres"
BigQuery"bigquery"
Snowflake"snowflake"
Redshift"redshift"
Databricks"databricks"
Filesystem (S3, GCS, Azure)"filesystem"

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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