Weiner process

class quantflow.sp.weiner.WeinerProcess(*, sigma: Annotated[float, Ge(ge=0)] = 1)

Methods:

analytical_cdf	Analytical cdf of the process at time t
analytical_mean	Analytical mean of the process at time t
analytical_pdf	Analytical pdf of the process at time t
analytical_variance	Analytical variance of the process at time t
characteristic_exponent	Characteristic exponent at time t for a given input parameter
sample	Generate random Paths from the process.
sample_from_draws	Sample Paths from the process given a set of draws

Attributes:

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

analytical_cdf(t: ndarray[tuple[int, ...], dtype[floating[Any]]] | float, x: ndarray[tuple[int, ...], dtype[floating[Any]]] | float) → ndarray[tuple[int, ...], dtype[floating[Any]]] | float

Analytical cdf of the process at time t

Implement if available

analytical_mean(t: ndarray[tuple[int, ...], dtype[floating[Any]]] | float) → ndarray[tuple[int, ...], dtype[floating[Any]]] | float

Analytical mean of the process at time t

Implement if available

analytical_pdf(t: ndarray[tuple[int, ...], dtype[floating[Any]]] | float, x: ndarray[tuple[int, ...], dtype[floating[Any]]] | float) → ndarray[tuple[int, ...], dtype[floating[Any]]] | float

Analytical pdf of the process at time t

Implement if available

analytical_variance(t: ndarray[tuple[int, ...], dtype[floating[Any]]] | float) → ndarray[tuple[int, ...], dtype[floating[Any]]] | float

Analytical variance of the process at time t

Implement if available

characteristic_exponent(t: int | float | complex | ndarray | Series, u: int | float | complex | ndarray | Series) → int | float | complex | ndarray | Series

Characteristic exponent at time t for a given input parameter

sample(n: int, time_horizon: float = 1, time_steps: int = 100) → Paths

Generate random Paths from the process.

Parameters:

• n – number of paths

• time_horizon – time horizon

• time_steps – number of time steps to arrive at horizon

sample_from_draws(draws: Paths, *args: Paths) → Paths

Sample Paths from the process given a set of draws

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].