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.

tests/test_forecast.py

@@ -0,0 +1,44 @@
+import numpy as np
+import pandas as pd
+from scipy.stats import norm
+
+from btcmodel.forecast import Forecast, crps, pit
+
+
+def normal_crps(mu, sigma, y):
+    """Closed-form CRPS of N(mu, sigma^2) at y."""
+    z = (y - mu) / sigma
+    return sigma * (z * (2 * norm.cdf(z) - 1) + 2 * norm.pdf(z) - 1 / np.sqrt(np.pi))
+
+
+def test_crps_matches_closed_form_for_normal():
+    f = Forecast.normal("2020-01-01", np.array([1, 2, 3]), mean=[0.0, 1.0, 2.0], sd=[1.0, 0.5, 2.0])
+    y = np.array([0.3, -0.2, 5.0])
+    expected = normal_crps(np.array([0.0, 1.0, 2.0]), np.array([1.0, 0.5, 2.0]), y)
+    np.testing.assert_allclose(crps(f.log_quantiles, y), expected, rtol=0.02)
+
+
+def test_crps_prefers_the_right_forecast():
+    y = np.array([0.0])
+    good = Forecast.normal("2020-01-01", np.array([1]), 0.0, 0.1)
+    biased = Forecast.normal("2020-01-01", np.array([1]), 0.5, 0.1)
+    vague = Forecast.normal("2020-01-01", np.array([1]), 0.0, 2.0)
+    assert crps(good.log_quantiles, y) < crps(biased.log_quantiles, y)
+    assert crps(good.log_quantiles, y) < crps(vague.log_quantiles, y)
+
+
+def test_pit_and_intervals():
+    f = Forecast.normal("2020-01-01", np.array([1, 1, 1]), 0.0, 1.0)
+    np.testing.assert_allclose(
+        pit(f.log_quantiles, [0.0, 1.0, -10.0]), [0.5, 0.841, 0.0], atol=0.01
+    )
+    lo, hi = f.interval(0.95)
+    np.testing.assert_allclose(hi, 1.96, atol=0.01)
+    np.testing.assert_allclose(lo, -1.96, atol=0.01)
+
+
+def test_from_samples_recovers_quantiles():
+    rng = np.random.default_rng(0)
+    f = Forecast.from_samples("2020-01-01", np.array([10]), rng.normal(0, 1, size=(200_000, 1)))
+    np.testing.assert_allclose(f.quantile(0.5), 0.0, atol=0.02)
+    assert f.dates[0] == pd.Timestamp("2020-01-11")