Nb

systematica.portfolio.metrics.nb

get_total_returns_nb

get_total_returns_nb(
    close: numpy.ndarray,
    sim_out: vectorbtpro.portfolio.enums.SimulationOutput,
)

Calculate the total returns.

Parameters:

Name	Type	Default	Description
close	tp.Array2d	--	Array of close prices.
sim_out	SimulationOutputT	--	Simulation output containing order records.

Returns:

Type	Description
float	Total returns.

get_expectancy_nb

get_expectancy_nb(
    close: numpy.ndarray,
    sim_out: vectorbtpro.portfolio.enums.SimulationOutput,
)

Calculate the expectancy.

Parameters:

Name	Type	Default	Description
close	tp.Array2d	--	Array of close prices.
sim_out	SimulationOutputT	--	Simulation output containing order records.

Returns:

Type	Description
float	Expectancy.

get_sharpe_nb

get_sharpe_nb(
    close: numpy.ndarray,
    sim_out: <function NamedTuple at 0x104ff8040>,
    ann_factor: int,
) ‑> float

Calculate the Sharpe ratio.

Note on ddof:

• Use ddof=0 for population variance, where you have data for the entire population.
• Use ddof=1 for sample variance, to correct for bias when estimating the variance of a population from a sample.

Parameters:

Name	Type	Default	Description
close	tp.Array2d	--	Array of close prices.
sim_out	tp.NamedTuple	--	Simulation output containing returns.
ann_factor	int	--	Annualization factor.

Returns:

Type	Description
float	Sharpe ratio.