Share India Python API Docs | dltHub

Share India is a trading platform that provides a REST API for algorithmic trading integration. The REST API base URL is `` and All requests require an API key for authentication..

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 Share India data in under 10 minutes.

---

What data can I load from Share India?

Here are some of the endpoints you can load from Share India:

---

How do I authenticate with the Share India API?

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

1. Get your credentials

1. Visit the Share India website and create a trading account.
2. Complete the KYC verification process as required.
3. After account activation, navigate to the developer or API section in the user dashboard.
4. Request API access; the system will generate and display your API key.
5. Copy the API key and store it securely for use in API calls.

2. Add them to .dlt/secrets.toml

Copy
[sources.share_india_source]
api_key = "your_api_key_here"

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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Share India 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:

Copy
python share_india_pipeline.py

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

Copy
Pipeline share_india_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset share_india_data
The duckdb destination used duckdb:/share_india.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline share_india_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.

---

Python pipeline example

This example loads from the Share India API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
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": "",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> 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)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

---

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):

Copy
import dlt

data = dlt.pipeline("share_india_pipeline").dataset()
sessions_df = data..df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM share_india_data. LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("share_india_pipeline").dataset()
data..df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Share India 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.

---

Troubleshooting

Authentication Errors

The API requires a valid API key. Requests without the key or with an incorrect key are rejected.

Rate Limiting

The documentation notes that rate‑limiting considerations exist, but exact limits and retry headers are not published.

General API Errors

No detailed list of error codes or response formats is available publicly; users should consult the developer portal or support for specifics.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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.

Copy
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