Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets

2026-09-24 13:22:23 +07:00
commit 642cc11a9f
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+from typing import Optional
+
+from torch import Tensor
+from torch.distributions import constraints
+from torch.distributions.exponential import Exponential
+from torch.distributions.transformed_distribution import TransformedDistribution
+from torch.distributions.transforms import AffineTransform, ExpTransform
+from torch.distributions.utils import broadcast_all
+from torch.types import _size
+
+
+__all__ = ["Pareto"]
+
+
+class Pareto(TransformedDistribution):
+    r"""
+    Samples from a Pareto Type 1 distribution.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Pareto(torch.tensor([1.0]), torch.tensor([1.0]))
+        >>> m.sample()  # sample from a Pareto distribution with scale=1 and alpha=1
+        tensor([ 1.5623])
+
+    Args:
+        scale (float or Tensor): Scale parameter of the distribution
+        alpha (float or Tensor): Shape parameter of the distribution
+    """
+
+    arg_constraints = {"alpha": constraints.positive, "scale": constraints.positive}
+
+    def __init__(
+        self,
+        scale: Tensor | float,
+        alpha: Tensor | float,
+        validate_args: bool | None = None,
+    ) -> None:
+        self.scale, self.alpha = broadcast_all(scale, alpha)
+        base_dist = Exponential(self.alpha, validate_args=validate_args)
+        transforms = [ExpTransform(), AffineTransform(loc=0, scale=self.scale)]
+        # pyrefly: ignore [bad-argument-type]
+        super().__init__(base_dist, transforms, validate_args=validate_args)
+
+    def expand(
+        self, batch_shape: _size, _instance: Optional["Pareto"] = None
+    ) -> "Pareto":
+        new = self._get_checked_instance(Pareto, _instance)
+        new.scale = self.scale.expand(batch_shape)
+        new.alpha = self.alpha.expand(batch_shape)
+        return super().expand(batch_shape, _instance=new)
+
+    @property
+    def mean(self) -> Tensor:
+        # mean is inf for alpha <= 1
+        a = self.alpha.clamp(min=1)
+        return a * self.scale / (a - 1)
+
+    @property
+    def mode(self) -> Tensor:
+        return self.scale
+
+    @property
+    def variance(self) -> Tensor:
+        # var is inf for alpha <= 2
+        a = self.alpha.clamp(min=2)
+        return self.scale.pow(2) * a / ((a - 1).pow(2) * (a - 2))
+
+    @constraints.dependent_property(is_discrete=False, event_dim=0)
+    def support(self) -> constraints.Constraint:
+        return constraints.greater_than_eq(self.scale)
+
+    def entropy(self) -> Tensor:
+        return (self.scale / self.alpha).log() + (1 + self.alpha.reciprocal())