Mudrex Python API Docs | dltHub

Mudrex is a programmatic crypto futures execution platform providing REST APIs for account management, fund transfers, order placement, position management, and market discovery. The REST API base URL is https://trade.mudrex.com and All requests require an API Key and API Secret; include the secret in the X-Authentication request header..

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 Mudrex data in under 10 minutes.

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What data can I load from Mudrex?

Here are some of the endpoints you can load from Mudrex:

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How do I authenticate with the Mudrex API?

Mudrex issues an API Key and an API Secret; the secret is shown only once on generation and must be sent with every API request in the X-Authentication header (e.g., -H "X-Authentication: ").

1. Get your credentials

1. Create a Mudrex account and complete KYC (PAN & Aadhaar verification) as required. 2) Enable TOTP 2FA in your account settings. 3) In the API Key Management / Generate API Key section, provide a name and click Generate Key. 4) Copy both the API Key and the API Secret; the secret is displayed only once. 5) Store credentials securely; rotate or revoke via dashboard if compromised.

2. Add them to .dlt/secrets.toml

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[sources.mudrex_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.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

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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:

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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 Mudrex 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:

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python mudrex_pipeline.py

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

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

Inspect your pipeline and data:

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dlt pipeline mudrex_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.

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Python pipeline example

This example loads wallet_futures_transfer and futures_order from the Mudrex API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def mudrex_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://trade.mudrex.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "wallet_futures_transfer", "endpoint": {"path": "fapi/v1/wallet/futures/transfer"}},
            {"name": "futures_order", "endpoint": {"path": "fapi/v1/futures/{symbol}/order"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mudrex_pipeline",
        destination="duckdb",
        dataset_name="mudrex_data",
    )
    load_info = pipeline.run(mudrex_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.

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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):

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import dlt

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

SQL (DuckDB example):

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SELECT * FROM mudrex_data.wallet_futures_transfer LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mudrex_pipeline").dataset()
data.wallet_futures_transfer.df().head()

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

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What destinations can I load Mudrex 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.

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Troubleshooting

Authentication failures

If requests return 401/403, verify you are sending the API Secret in the X-Authentication header exactly as provided. Ensure API Secret has not been revoked and 2FA/TOTP requirements for key creation were met.

Missing secret / key issues

The API Secret is shown only once at generation. If lost, revoke the key and generate a new one via dashboard.

Rate limits & errors

Documentation does not publish public rate limits in the accessible Quickstart; handle 429 responses by backing off and retrying with exponential backoff. For 4xx errors, inspect response body for error details.

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

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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.

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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