Rewrite as a probabilistic model with walk-forward evaluation.

Replace the 2024 model (model.py, ~2000 lines) with the btcmodel package, the
baseline for future work:

- Forecasts are quantiles of log price at each horizon, scored with CRPS in a
  walk-forward backtest (origins every 30 days from 2014, horizons 1 month to
  4 years). Skill is relative to a zero-drift random walk, with circular
  block-bootstrap intervals and a count of independent windows.
- Development data stops at 2024-11-26, the last day the 2024 model saw.
  Later outcomes are a holdout, scored only by `backtest --holdout`.
- Models: random_walk, drift_rw, and cycle (the 2024 model's cycle-position
  drift, now kernel-smoothed and recency-weighted). On development data
  nothing beats the random walk with confidence; cycle loses at every horizon.
- Prices: the Investing.com archive moves to data/ (cut at 2024-11-26; its
  last row was intraday) and is extended with Coinbase daily closes by
  `update`.

Also: Nix flake dev shell (Python 3.13, pandas 3), ruff in place of black,
pytest suite, and a rewritten README. NOTES.md is removed as inaccurate, and
poetry is dropped.

btcmodel/models/__init__.py

@@ -0,0 +1,15 @@
+"""
+Candidate models.
+
+A model is any object with a `name` and a
+`forecast(history: pd.DataFrame, horizons: np.ndarray) -> Forecast` method.
+`history` holds every row up to and including the forecast origin and nothing
+after it; the harness guarantees that, so models can use all of it freely.
+"""
+
+from .baselines import DriftRandomWalk, RandomWalk
+from .cycle import CycleModel
+
+# Order is fixed: it sets each model's colour in every chart.
+MODELS = {m.name: m for m in (RandomWalk(), DriftRandomWalk(), CycleModel())}
+BASELINE = RandomWalk.name