Research Recipes

This guide records notebook-shaped patterns that Alphaforge should support without bespoke helper layers.

Volatility recipe

The canonical volatility workflow is:

1. build a DataContext from adapters with DataContext.from_adapters(...)
2. group reusable features with FeatureRequestGroup
3. use built-in market templates for lagged returns and trailing volatility
4. keep custom code focused on the target definition rather than feature plumbing

The built-in feature family now covers the common market-price pieces:

• LagReturnsTemplate for lagged simple or log returns
• RollingVolatilityTemplate for trailing realized volatility windows

Example feature group:

from alphaforge.features import LagReturnsTemplate, RollingVolatilityTemplate
from alphaforge.features.dataset_spec import FeatureRequest, FeatureRequestGroup

features = [
    FeatureRequestGroup(
        key="volatility",
        tags={"recipe": "volatility"},
        requests=[
            FeatureRequest(
                template=LagReturnsTemplate(),
                key="returns",
                params={
                    "dataset": "market.ohlcv",
                    "source": "market",
                    "price_col": "close",
                    "lags": [1, 5, 10],
                },
            ),
            FeatureRequest(
                template=RollingVolatilityTemplate(),
                key="trailing_vol",
                params={
                    "dataset": "market.ohlcv",
                    "source": "market",
                    "price_col": "close",
                    "windows": [5, 10, 21],
                    "lag": 1,
                    "annualization_factor": 252,
                },
            ),
        ],
    )
]

This removes the repeated notebook work of:

• fetching prices manually for each feature family
• writing ad hoc lag-return helper cells
• rebuilding the trailing-volatility calculation in each notebook
• hand-annotating request metadata in the catalog

For a full runnable example, see examples/volatility_dataset_recipe.py.