numeraire.WeightsOutput#

class numeraire.WeightsOutput(weights: DataFrame, realized: DataFrame, method: str, config_hash: str, data_vintage: str, run_id: str, capability: str = 'to_weights', meta: dict[str, ~typing.Any]=<factory>)[source]#

Bases: object

OOS output for a to_weights method: realized weights aligned with realized returns.

weights and realized are both (date x asset) indexed by the prediction dates, where realized.loc[t] is the return over (t, t+h] (so strategy_returns is the realized, no-look-ahead P&L of holding weights.loc[t] over that period).

__init__(weights: DataFrame, realized: DataFrame, method: str, config_hash: str, data_vintage: str, run_id: str, capability: str = 'to_weights', meta: dict[str, ~typing.Any]=<factory>) → None#

Methods

`__init__`(weights, realized, method, ...)
`strategy_returns`()	Realized portfolio return per date: `sum_a weights[a] * realized[a]`.

Attributes

`capability`
`universe`	Compact universe label (`n=<#assets>` for panels, the name for a single asset).
`weights`
`realized`
`method`
`config_hash`
`data_vintage`
`run_id`
`meta`

property universe: str#: Compact universe label (n=<#assets> for panels, the name for a single asset).

strategy_returns() → Series[source]#: Realized portfolio return per date: sum_a weights[a] * realized[a].