f5f1551964
Lucidia Math - Advanced mathematical engines: forge/ - Mathematical Foundations: - consciousness.py (650 lines) - Consciousness modeling - unified_geometry.py (402 lines) - Geometric transformations - advanced_tools.py (356 lines) - Advanced utilities - main.py (209 lines) - CLI orchestration - numbers.py, proofs.py, fractals.py, dimensions.py lab/ - Experimental Mathematics: - unified_geometry_engine.py (492 lines) - Geometry engine - amundson_equations.py (284 lines) - Custom equations - iterative_math_build.py (198 lines) - Iterative construction - trinary_logic.py (111 lines) - Three-valued logic - prime_explorer.py (108 lines) - Prime exploration - quantum_finance.py (83 lines) - Quantum finance models 3,664 lines of Python. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
84 lines
3.0 KiB
Python
84 lines
3.0 KiB
Python
"""Quantum-style finance sandbox."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import List
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import matplotlib.pyplot as plt
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import numpy as np
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from .frameworks import select_backend
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@dataclass
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class QuantumFinanceSimulator:
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price: float
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volatility: float
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history: List[float] = field(default_factory=list)
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backend: str = "numpy"
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device: str = "cpu"
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seed: int | None = None
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def __post_init__(self) -> None:
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self.history = list(self.history)
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self._rng = np.random.default_rng(self.seed)
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self._backend_name = self.backend
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if self.backend == "torch":
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try:
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import torch
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except ImportError as exc: # pragma: no cover - optional dependency
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raise RuntimeError("Torch backend requested but torch is not installed") from exc
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self._torch = torch
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else:
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try:
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backend_cfg = select_backend(self.backend)
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except ValueError:
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backend_cfg = select_backend("numpy")
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self._backend_name = backend_cfg.name
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if backend_cfg.name == "jax": # pragma: no cover - optional dependency
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import jax
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self._jax = jax
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self._key = jax.random.PRNGKey(self.seed or 0)
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def step(self, samples: int = 1000) -> np.ndarray:
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"""Simulate a probabilistic price distribution."""
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if samples <= 0:
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raise ValueError("samples must be positive")
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if self._backend_name == "torch": # pragma: no cover - optional dependency
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dist = self._torch.distributions.Normal(
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loc=self._torch.tensor(self.price, device=self.device),
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scale=self._torch.tensor(self.volatility, device=self.device),
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)
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tensor = dist.sample((samples,))
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distribution = tensor.detach().cpu().numpy()
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elif self._backend_name == "jax": # pragma: no cover - optional dependency
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self._key, subkey = self._jax.random.split(self._key)
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noise = np.asarray(self._jax.random.normal(subkey, shape=(samples,)), dtype=float)
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distribution = self.price + self.volatility * noise
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else:
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distribution = self._rng.normal(self.price, self.volatility, samples)
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self.history.append(float(np.mean(distribution)))
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return np.asarray(distribution, dtype=float)
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def observe(self, distribution: np.ndarray) -> float:
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"""Collapse the distribution to a single price."""
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values = np.asarray(distribution, dtype=float)
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self.price = float(self._rng.choice(values))
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return self.price
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def plot(self, distribution: np.ndarray) -> plt.Figure:
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values = np.asarray(distribution, dtype=float)
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fig, ax = plt.subplots(2, 1, figsize=(6, 6))
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ax[0].hist(values, bins=30)
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ax[0].set_title("Probability distribution")
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ax[1].plot(self.history + [float(self.price)])
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ax[1].set_title("Collapsed price over time")
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return fig
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