Scripting with Jupyter 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.

cd my-research
mlab notebook

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

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

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

Follow the workflow

1. Getting Started — choose Python and launch JupyterLab.
2. Research Workflow — fetch data, use pandas, run studies, and plot results cell by cell.
3. 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 instead.