# Scripting with Jupyter Notebook (/scripting-v2/notebook)



`mlab notebook` opens JupyterLab with Market Lab historical data and native studies ready inside the Python kernel.

This workflow is for exploration, visualization, and feature engineering. It does not require a Market Lab Python package, script manifest, or callback such as `on_data`.

```bash
cd my-research
mlab notebook
```

Inside the notebook, Market Lab provides the `mlab` object automatically:

```python
history = mlab.history(
    start="2026-08-01",
    end="2026-08-20",
)

candles = history.source(
    "btc@candles@hyperliquidf:timeframe=3600"
)
```

## Follow the workflow [#follow-the-workflow]

1. [Getting Started](/scripting-v2/notebook/getting-started) — choose Python and launch JupyterLab.
2. [Research Workflow](/scripting-v2/notebook/research) — fetch data, use pandas, run studies, and plot results cell by cell.
3. [How It Works](/scripting-v2/notebook/how-it-works) — understand the injected `mlab` object, local bridge, caching, and current research-only boundary.

For sequential backtests or live deployment, use [Plain Python Files](/scripting-v2/getting-started) instead.