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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+# mypy: allow-untyped-defs
+import math
+
+import torch
+from torch import Tensor
+from torch.distributions import constraints
+from torch.distributions.transformed_distribution import TransformedDistribution
+from torch.distributions.transforms import AffineTransform, ExpTransform
+from torch.distributions.uniform import Uniform
+from torch.distributions.utils import broadcast_all, euler_constant
+from torch.types import _Number
+
+
+__all__ = ["Gumbel"]
+
+
+class Gumbel(TransformedDistribution):
+    r"""
+    Samples from a Gumbel Distribution.
+
+    Examples::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Gumbel(torch.tensor([1.0]), torch.tensor([2.0]))
+        >>> m.sample()  # sample from Gumbel distribution with loc=1, scale=2
+        tensor([ 1.0124])
+
+    Args:
+        loc (float or Tensor): Location parameter of the distribution
+        scale (float or Tensor): Scale parameter of the distribution
+    """
+
+    arg_constraints = {"loc": constraints.real, "scale": constraints.positive}
+    # pyrefly: ignore [bad-override]
+    support = constraints.real
+
+    def __init__(
+        self,
+        loc: Tensor | float,
+        scale: Tensor | float,
+        validate_args: bool | None = None,
+    ) -> None:
+        self.loc, self.scale = broadcast_all(loc, scale)
+        finfo = torch.finfo(self.loc.dtype)
+        if isinstance(loc, _Number) and isinstance(scale, _Number):
+            base_dist = Uniform(finfo.tiny, 1 - finfo.eps, validate_args=validate_args)
+        else:
+            base_dist = Uniform(
+                torch.full_like(self.loc, finfo.tiny),
+                torch.full_like(self.loc, 1 - finfo.eps),
+                validate_args=validate_args,
+            )
+        transforms = [
+            ExpTransform().inv,
+            AffineTransform(loc=0, scale=-torch.ones_like(self.scale)),
+            ExpTransform().inv,
+            AffineTransform(loc=loc, scale=-self.scale),
+        ]
+        super().__init__(base_dist, transforms, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Gumbel, _instance)
+        new.loc = self.loc.expand(batch_shape)
+        new.scale = self.scale.expand(batch_shape)
+        return super().expand(batch_shape, _instance=new)
+
+    # Explicitly defining the log probability function for Gumbel due to precision issues
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        y = (self.loc - value) / self.scale
+        return (y - y.exp()) - self.scale.log()
+
+    @property
+    def mean(self) -> Tensor:
+        return self.loc + self.scale * euler_constant
+
+    @property
+    def mode(self) -> Tensor:
+        return self.loc
+
+    @property
+    def stddev(self) -> Tensor:
+        return (math.pi / math.sqrt(6)) * self.scale
+
+    @property
+    def variance(self) -> Tensor:
+        return self.stddev.pow(2)
+
+    def entropy(self):
+        return self.scale.log() + (1 + euler_constant)