Returns Attribution

perfana.monte_carlo.returns.returns_attr(data, weights, freq, geometric=True, rebalance=True)[source]

Derives the returns attribution given a data cube and weights.

Notes

The return values are defined as follows:

  • marginal

    The absolute marginal contribution of the asset class towards the portfolio returns. It is essentially the percentage attribution multiplied by the portfolio returns.

  • percentage

    The percentage contribution of the asset class towards the portfolio returns. This number though named in percentage is actually in decimals. Thus 0.01 represents a 1% contribution.

Parameters

  • data (ndarray) – Monte carlo simulation data. This must be 3 dimensional with the axis representing time, trial and asset respectively.

  • weights (Union[Iterable[Union[int, float]], ndarray, Series]) – Weights of the portfolio. This must be 1 dimensional and must match the dimension of the data’s last axis.

  • freq (Union[str, int]) – Frequency of the data. Can either be a string (‘week’, ‘month’, ‘quarter’, ‘semi-annual’, ‘year’) or an integer specifying the number of units per year. Week: 52, Month: 12, Quarter: 4, Semi-annual: 6, Year: 1.

  • geometric (bool) – If True, calculates the geometric mean, otherwise, calculates the arithmetic mean.

  • rebalance (bool) – If True, portfolio is assumed to be rebalanced at every step.

Returns

A named tuple of marginal and percentage returns attribution respectively. The marginal attribution is the returns of the simulated data over time multiplied by the percentage attribution.

Return type

Attribution

Examples

>>> from perfana.datasets import load_cube
>>> from perfana.monte_carlo import returns_attr
>>> cube = load_cube()[..., :3]
>>> weights = [0.33, 0.34, 0.33]
>>> freq = "quarterly"
>>> attr = returns_attr(cube, weights, freq)
>>> attr.marginal
array([0.00996204, 0.00733369, 0.00963802])
>>> attr.percentage
array([0.36987203, 0.27228623, 0.35784174])
>>> attr.marginal is attr[0]
True
>>> attr.percentage is attr[1]
True