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.

docs/2024-ideas.md

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+# Ideas in the 2024 model's history
+
+A catalogue of the modeling ideas in the old `model.py` and its branches
+(September 2026). Labels are referenced from `btcmodel/experiments.py`.
+"Fudge" means the constant or rule existed to hit backtest coverage/MAPE
+targets rather than to express a hypothesis.
+
+## Drift
+
+- **D1. Mean return by halving-cycle position.** The core idea throughout
+  (initial commit, `Switch to simpler log-based projection`, `Implement trend
+  smoothing`, `tuning-b`). Smoothing went Savitzky-Golay → 60-day centred mean
+  → Gaussian kernel with recency weights. The old code stretched every cycle
+  to 1460 days (phase = fraction of the halving interval). Bugs: simple
+  returns compounded as log returns (+~25%/yr bias) in the initial commit;
+  genesis date off by a year; `tuning-3` regressed log price on row index
+  across cycles. The "blend ratio of 0 is best?" commit was blending the
+  simple- and log-return versions of the same quantity.
+- **D2. Drift damping.** Constants everywhere (×0.6–0.9, asymmetric, era- and
+  cycle-position-keyed, a 3%/day cap, the fundamentals ×0.65–0.75). Fudge as
+  implemented; the hypothesis underneath (the cycle drift is overfit) is real.
+- **D3. Diminishing returns.** Initial commit decayed drift by 0.9^(t/365);
+  `old/backtests-trend-enhancement-1` regressed per-cycle returns on cycle
+  number, but added that on top of the cycle drift (double counting) and
+  clipped paths.
+- **D4. "Skew".** `loc += sign(μ)·0.087σ`, tuned so 68% coverage hit 68.1%.
+  A fudge; its honest cousin is momentum.
+- **D5. Stock-to-flow.** A 30% blend of the daily % change in S2F into the
+  drift. Broken: mismatched units, and S2F "halvings" on the wrong dates
+  created a large fake post-halving drift.
+
+## Volatility
+
+- **V1. EWMA blend.** 30/90/180-day spans, weights .5/.3/.2 then .2/.5/.3,
+  times a 1.2 fudge.
+- **V2. Adaptive windows / regime weights.** Short/long vol ratio rescales
+  window lengths and blend weights; over-parameterised, and after the
+  fundamentals rewrite the adaptive windows were computed but unused.
+- **V3. Cycle-position volatility.** `old/backtest-vol-cycle`.
+- **V4. Market maturity.** Volume growth, inverse vol, autocorrelation and a
+  post-futures dummy, min-max normalised in-sample. Removed as "complexity
+  without clear benefit".
+- **V5. "Fundamentals".** Supply growth, volume/supply and "depth" combined
+  and clipped to [0.65, 0.75]: effectively a constant, grid-searched to match
+  the era constants it replaced.
+- **V6. Market conditions.** Vol ratio, MA50−MA200 trend strength (≈30× too
+  large from a units bug) and drawdown widening uncertainty.
+
+## Calibration (all fudges)
+
+- **C1. Era scaling keyed to the backtest's training start date**, with stacked
+  factors (net ≈0.45× vol) and hand-picked era boundaries.
+- **C2. Horizon uncertainty multipliers**, caps and floors on daily vol.
+- **C3. Changing the quantile levels**: a "95%" band read off the ~81%
+  quantiles past a year.
+
+## Shape
+
+- **S1. Skewed innovations**, with skew set per era (`tuning-1`).
+
+## Also
+
+Plumbing (backtest framework, multiprocessing, output handling, cycle and
+CDPR plots). The old NOTES.md described "machine learning" and "macro
+indicator" experiments; no such code ever existed.