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/halving.py

@@ -32,3 +32,10 @@ def cycle_position(dates) -> tuple[np.ndarray, np.ndarray]:
     index = CYCLE_STARTS.searchsorted(dates, side="right") - 1
     days = (dates - CYCLE_STARTS[index]).days
     return np.asarray(index), np.asarray(days)
+
+
+def cycle_fraction(dates) -> tuple[np.ndarray, np.ndarray]:
+    """For each date, return (cycle index, fraction of that cycle elapsed, in [0, 1))."""
+    index, days = cycle_position(dates)
+    lengths = (CYCLE_STARTS[index + 1] - CYCLE_STARTS[index]).days
+    return index, days / np.asarray(lengths)