sf_quant.performance.generate_leverage_from_weights#

sf_quant.performance.generate_leverage_from_weights(weights: PortfolioSchema) → LeverageSchema#

Calculate leverage from portfolio weights.

Parameters#

weights (PortfolioSchema): Portfolio weights validated against PortfolioSchema.

Must include the following columns: - date (date): The observation date. - barrid (str): Asset identifier. - weight (float): Position weight.

Returns#

LeverageSchema: A validated DataFrame with columns:

  • date (date): The observation date.

  • leverage (float): Sum of absolute weights.

Notes#

  • Leverage is calculated as the sum of absolute weights per date.

  • Leverage = 1.0 means fully invested with no shorting.

  • Leverage > 1.0 indicates use of margin or shorting.

Examples#

>>> import polars as pl
>>> import sf_quant.performance as sfp
>>> import datetime as dt
>>> weights = pl.DataFrame(
...     {
...         'date': [dt.date(2024, 1, 2), dt.date(2024, 1, 2), dt.date(2024, 1, 3), dt.date(2024, 1, 3)],
...         'barrid': ['USA06Z1', 'USA0771', 'USA06Z1', 'USA0771'],
...         'weight': [0.5, 0.5, 0.3, 0.7]
...     }
... )
>>> leverage = sfp.generate_leverage_from_weights(weights)
>>> leverage
shape: (2, 2)
┌────────────┬──────────┐
│ date       ┆ leverage │
│ ---        ┆ ---      │
│ date       ┆ f64      │
╞════════════╪══════════╡
│ 2024-01-02 ┆ 1.0      │
│ 2024-01-03 ┆ 1.0      │
└────────────┴──────────┘