from math import ceil
from typing import Optional
from typing import Tuple
import torch
from torch import Tensor
from pfhedge._utils.doc import _set_attr_and_docstring
from pfhedge._utils.doc import _set_docstring
from pfhedge._utils.str import _format_float
from pfhedge._utils.typing import TensorOrScalar
from pfhedge.stochastic import generate_heston
from .base import BasePrimary
[docs]class HestonStock(BasePrimary):
r"""A stock of which spot price and variance follow Heston process.
.. seealso::
- :func:`pfhedge.stochastic.generate_heston`:
The stochastic process.
Args:
kappa (float, default=1.0): The parameter :math:`\kappa`.
theta (float, default=0.04): The parameter :math:`\theta`.
sigma (float, default=2.0): The parameter :math:`\sigma`.
rho (float, default=-0.7): The parameter :math:`\rho`.
cost (float, default=0.0): The transaction cost rate.
dt (float, default=1/250): The intervals of the time steps.
dtype (torch.device, optional): Desired device of returned tensor.
Default: If None, uses a global default
(see :func:`torch.set_default_tensor_type()`).
device (torch.device, optional): Desired device of returned tensor.
Default: if None, uses the current device for the default tensor type
(see :func:`torch.set_default_tensor_type()`).
``device`` will be the CPU for CPU tensor types and
the current CUDA device for CUDA tensor types.
Buffers:
- spot (:class:`torch.Tensor`): The spot price of the instrument.
This attribute is set by a method :meth:`simulate()`.
The shape is :math:`(N, T)` where
:math:`N` is the number of simulated paths and
:math:`T` is the number of time steps.
- variance (:class:`torch.Tensor`): The variance of the instrument.
Note that this is different from the realized variance of the spot price.
This attribute is set by a method :meth:`simulate()`.
The shape is :math:`(N, T)`.
Examples:
>>> from pfhedge.instruments import HestonStock
>>>
>>> _ = torch.manual_seed(42)
>>> stock = HestonStock()
>>> stock.simulate(n_paths=2, time_horizon=5/250)
>>> stock.spot
tensor([[1.0000, 0.9902, 0.9823, 0.9926, 0.9968, 1.0040],
[1.0000, 0.9826, 0.9891, 0.9898, 0.9851, 0.9796]])
>>> stock.variance
tensor([[0.0400, 0.0408, 0.0411, 0.0417, 0.0422, 0.0393],
[0.0400, 0.0457, 0.0440, 0.0451, 0.0458, 0.0472]])
>>> stock.volatility
tensor([[0.2000, 0.2020, 0.2027, 0.2041, 0.2054, 0.1982],
[0.2000, 0.2138, 0.2097, 0.2124, 0.2140, 0.2172]])
"""
spot: Tensor
variance: Tensor
def __init__(
self,
kappa: float = 1.0,
theta: float = 0.04,
sigma: float = 0.2,
rho: float = -0.7,
cost: float = 0.0,
dt: float = 1 / 250,
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
) -> None:
super().__init__()
self.kappa = kappa
self.theta = theta
self.sigma = sigma
self.rho = rho
self.cost = cost
self.dt = dt
self.to(dtype=dtype, device=device)
@property
def default_init_state(self) -> Tuple[float, ...]:
return (1.0, self.theta)
@property
def volatility(self) -> Tensor:
"""An alias for ``self.variance.sqrt()``."""
return self.get_buffer("variance").clamp(min=0.0).sqrt()
[docs] def simulate(
self,
n_paths: int = 1,
time_horizon: float = 20 / 250,
init_state: Optional[Tuple[TensorOrScalar, ...]] = None,
) -> None:
"""Simulate the spot price and add it as a buffer named ``spot``.
The shape of the spot is :math:`(N, T)`, where
:math:`N` is the number of simulated paths and
:math:`T` is the number of time steps.
The number of time steps is determinded from ``dt`` and ``time_horizon``.
Args:
n_paths (int, default=1): The number of paths to simulate.
time_horizon (float, default=20/250): The period of time to simulate
the price.
init_state (tuple[torch.Tensor | float], optional): The initial
state of the instrument.
This is specified by a tuple :math:`(S(0), V(0))` where
:math:`S(0)` and :math:`V(0)` are the initial values of
spot and variance, respectively.
If ``None`` (default), it uses the default value
(See :attr:`default_init_state`).
"""
if init_state is None:
init_state = self.default_init_state
output = generate_heston(
n_paths=n_paths,
n_steps=ceil(time_horizon / self.dt + 1),
init_state=init_state,
kappa=self.kappa,
theta=self.theta,
sigma=self.sigma,
rho=self.rho,
dt=self.dt,
dtype=self.dtype,
device=self.device,
)
self.register_buffer("spot", output.spot)
self.register_buffer("variance", output.variance)
def extra_repr(self) -> str:
params = [
"kappa=" + _format_float(self.kappa),
"theta=" + _format_float(self.theta),
"sigma=" + _format_float(self.sigma),
"rho=" + _format_float(self.rho),
]
if self.cost != 0.0:
params.append("cost=" + _format_float(self.cost))
params.append("dt=" + _format_float(self.dt))
return ", ".join(params)
# Assign docstrings so they appear in Sphinx documentation
_set_docstring(HestonStock, "default_init_state", BasePrimary.default_init_state)
_set_attr_and_docstring(HestonStock, "to", BasePrimary.to)