Composable models and A/B tests of the 2024 ideas; add powerlaw.

Models are now a Composite of drift, volatility and (optional) shape
components, so an experiment can swap one part against a fixed control.

btcmodel/experiments.py holds seven experiments built from the ideas in the
old branches (catalogued in docs/2024-ideas.md), each with its hypothesis
and source, and a verdict rule fixed before anything ran. `just ab` runs
them on development data. Results:

- Shrinking the cycle drift, and a power-law trend (plain or reverting),
  beat their controls. The power law beats the random walk by 53-63% at
  3-4 years with unbiased outcomes, so it is promoted to MODELS.
- Every alternative volatility estimate (EWMA blends, other windows,
  reversion to a level or trend) is worse than the trailing 365-day window.
  Cycle-dependent volatility, heavy tails and stretched cycle phase show no
  reliable effect.

btcmodel/models/base.py

@@ -0,0 +1,50 @@
+"""
+Models built from parts.
+
+Most ideas about Bitcoin prices say something about either the expected return
+(drift) or the size of the uncertainty (volatility). A Composite pairs one of
+each, so an A/B test can swap exactly one part and hold the other fixed.
+"""
+
+from dataclasses import dataclass
+from typing import Protocol
+
+import numpy as np
+import pandas as pd
+
+from ..forecast import Forecast
+from .shape import Normal
+
+
+class Drift(Protocol):
+    def expected_log_return(self, history: pd.DataFrame, horizons: np.ndarray) -> np.ndarray:
+        """Expected cumulative log return from the origin to each horizon."""
+        ...
+
+
+class Volatility(Protocol):
+    def sd(self, history: pd.DataFrame, horizons: np.ndarray) -> np.ndarray:
+        """Standard deviation of the cumulative log return at each horizon."""
+        ...
+
+
+class Shape(Protocol):
+    def standard_quantiles(self, history: pd.DataFrame, horizons: np.ndarray) -> np.ndarray:
+        """Quantiles at LEVELS of a mean-0, sd-1 distribution, shape (H, N_LEVELS)."""
+        ...
+
+
+@dataclass(frozen=True)
+class Composite:
+    """Log price: mean from `drift`, spread from `volatility`, normal unless `shape` says."""
+
+    name: str
+    drift: Drift
+    volatility: Volatility
+    shape: Shape = Normal()
+
+    def forecast(self, history: pd.DataFrame, horizons: np.ndarray) -> Forecast:
+        mean = np.log(history["close"].iloc[-1]) + self.drift.expected_log_return(history, horizons)
+        sd = self.volatility.sd(history, horizons)
+        z = self.shape.standard_quantiles(history, horizons)
+        return Forecast(history.index[-1], horizons, mean[:, None] + sd[:, None] * z)