Load Share India data to DuckDB

Share India is a trading platform that provides a REST API for algorithmic trading integration. Everything needed to build a working Share India → 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 Share India 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 Share India 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 Share India 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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Share India API at a glance

These values come from the Share India API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Share India API?

Authentication is performed using API keys that must be included in request headers; the specific header name is not publicly documented.

1. Get your credentials

To obtain your API credentials for the Share India REST API: 1. Ensure you have an active Share India trading account and have completed the KYC verification process. 2. Log in to the official Share India website or your user dashboard. 3. Navigate to the developer or API section within the dashboard. 4. Submit a request for API access if required by your account plan. 5. Once approved/activated, the system will generate and display your API key. Store this key securely.

2. Add them to .dlt/secrets.toml

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[sources.share_india_source]
api_key = "REPLACE_ME"

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 Share India data can I load into DuckDB?

These are the Share India endpoints dlt can load into DuckDB:

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How do I load only new Share India records?

Share India exposes updated_at on orders, 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": "orders", "endpoint": {
    "path": "orders",
    "data_selector": "data",
    "incremental": {"cursor_path": "updated_at", "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 Share India pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading orders and positions from the Share India API into DuckDB:

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

@dlt.source
def share_india_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Not publicly documented.",
            "auth": {"type": "api_key", "api_key": api_key},
        },
        "resources": [
            {"name": "orders", "endpoint": {"path": "orders", "data_selector": "data"}},
            {"name": "positions", "endpoint": {"path": "positions", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_share_india_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="share_india_pipeline",
        destination="duckdb",
        dataset_name="share_india_data",
    )
    load_info = pipeline.run(share_india_source())
    print(load_info)


if __name__ == "__main__":
    load_share_india_to_duckdb()

Run it with python share_india_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 Share India 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("share_india_pipeline").dataset()
df = data.orders.df()
print(df.head())

SQL:

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SELECT * FROM share_india_data.orders LIMIT 10;

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

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How do I deploy the Share India 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 Share India 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 Share India 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