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 torch
+from torch import Tensor
+from torch.distributions import constraints
+from torch.distributions.dirichlet import Dirichlet
+from torch.distributions.exp_family import ExponentialFamily
+from torch.distributions.utils import broadcast_all
+from torch.types import _Number, _size
+
+
+__all__ = ["Beta"]
+
+
+class Beta(ExponentialFamily):
+    r"""
+    Beta distribution parameterized by :attr:`concentration1` and :attr:`concentration0`.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Beta(torch.tensor([0.5]), torch.tensor([0.5]))
+        >>> m.sample()  # Beta distributed with concentration concentration1 and concentration0
+        tensor([ 0.1046])
+
+    Args:
+        concentration1 (float or Tensor): 1st concentration parameter of the distribution
+            (often referred to as alpha)
+        concentration0 (float or Tensor): 2nd concentration parameter of the distribution
+            (often referred to as beta)
+    """
+
+    # pyrefly: ignore [bad-override]
+    arg_constraints = {
+        "concentration1": constraints.positive,
+        "concentration0": constraints.positive,
+    }
+    support = constraints.unit_interval
+    has_rsample = True
+
+    def __init__(
+        self,
+        concentration1: Tensor | float,
+        concentration0: Tensor | float,
+        validate_args: bool | None = None,
+    ) -> None:
+        if isinstance(concentration1, _Number) and isinstance(concentration0, _Number):
+            concentration1_concentration0 = torch.tensor(
+                [float(concentration1), float(concentration0)]
+            )
+        else:
+            concentration1, concentration0 = broadcast_all(
+                concentration1, concentration0
+            )
+            concentration1_concentration0 = torch.stack(
+                [concentration1, concentration0], -1
+            )
+        self._dirichlet = Dirichlet(
+            concentration1_concentration0, validate_args=validate_args
+        )
+        super().__init__(self._dirichlet._batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Beta, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new._dirichlet = self._dirichlet.expand(batch_shape)
+        super(Beta, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    @property
+    def mean(self) -> Tensor:
+        return self.concentration1 / (self.concentration1 + self.concentration0)
+
+    @property
+    def mode(self) -> Tensor:
+        return self._dirichlet.mode[..., 0]
+
+    @property
+    def variance(self) -> Tensor:
+        total = self.concentration1 + self.concentration0
+        return self.concentration1 * self.concentration0 / (total.pow(2) * (total + 1))
+
+    def rsample(self, sample_shape: _size = ()) -> Tensor:
+        return self._dirichlet.rsample(sample_shape).select(-1, 0)
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        heads_tails = torch.stack([value, 1.0 - value], -1)
+        return self._dirichlet.log_prob(heads_tails)
+
+    def entropy(self):
+        return self._dirichlet.entropy()
+
+    @property
+    def concentration1(self) -> Tensor:
+        result = self._dirichlet.concentration[..., 0]
+        if isinstance(result, _Number):
+            return torch.tensor([result])
+        else:
+            return result
+
+    @property
+    def concentration0(self) -> Tensor:
+        result = self._dirichlet.concentration[..., 1]
+        if isinstance(result, _Number):
+            return torch.tensor([result])
+        else:
+            return result
+
+    @property
+    def _natural_params(self) -> tuple[Tensor, Tensor]:
+        return (self.concentration1, self.concentration0)
+
+    # pyrefly: ignore [bad-override]
+    def _log_normalizer(self, x, y):
+        return torch.lgamma(x) + torch.lgamma(y) - torch.lgamma(x + y)