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

2026-09-24 13:22:23 +07:00
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+# mypy: allow-untyped-defs
+
+import torch
+from torch import Tensor
+from torch.distributions import constraints
+from torch.distributions.gamma import Gamma
+from torch.distributions.transformed_distribution import TransformedDistribution
+from torch.distributions.transforms import PowerTransform
+
+
+__all__ = ["InverseGamma"]
+
+
+class InverseGamma(TransformedDistribution):
+    r"""
+    Creates an inverse gamma distribution parameterized by :attr:`concentration` and :attr:`rate`
+    where::
+
+        X ~ Gamma(concentration, rate)
+        Y = 1 / X ~ InverseGamma(concentration, rate)
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> m = InverseGamma(torch.tensor([2.0]), torch.tensor([3.0]))
+        >>> m.sample()
+        tensor([ 1.2953])
+
+    Args:
+        concentration (float or Tensor): shape parameter of the distribution
+            (often referred to as alpha)
+        rate (float or Tensor): rate = 1 / scale of the distribution
+            (often referred to as beta)
+    """
+
+    arg_constraints = {
+        "concentration": constraints.positive,
+        "rate": constraints.positive,
+    }
+    # pyrefly: ignore [bad-override]
+    support = constraints.positive
+    has_rsample = True
+    # pyrefly: ignore [bad-override]
+    base_dist: Gamma
+
+    def __init__(
+        self,
+        concentration: Tensor | float,
+        rate: Tensor | float,
+        validate_args: bool | None = None,
+    ) -> None:
+        base_dist = Gamma(concentration, rate, validate_args=validate_args)
+        neg_one = -base_dist.rate.new_ones(())
+        super().__init__(
+            base_dist, PowerTransform(neg_one), validate_args=validate_args
+        )
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(InverseGamma, _instance)
+        return super().expand(batch_shape, _instance=new)
+
+    @property
+    def concentration(self) -> Tensor:
+        return self.base_dist.concentration
+
+    @property
+    def rate(self) -> Tensor:
+        return self.base_dist.rate
+
+    @property
+    def mean(self) -> Tensor:
+        result = self.rate / (self.concentration - 1)
+        return torch.where(self.concentration > 1, result, torch.inf)
+
+    @property
+    def mode(self) -> Tensor:
+        return self.rate / (self.concentration + 1)
+
+    @property
+    def variance(self) -> Tensor:
+        result = self.rate.square() / (
+            (self.concentration - 1).square() * (self.concentration - 2)
+        )
+        return torch.where(self.concentration > 2, result, torch.inf)
+
+    def entropy(self):
+        return (
+            self.concentration
+            + self.rate.log()
+            + self.concentration.lgamma()
+            - (1 + self.concentration) * self.concentration.digamma()
+        )