Take model tuning from config too, not from the saved state

The companion to the Kalman fix. RecursiveLeastSquares.from_dict and
AdaptiveConformal.from_dict restore lambda, delta, alpha, gamma and the
conformal window alongside their data, and load_dict replaces the
config-built heads with those, so every one of those knobs was immutable on
any station that already had state. Editing config.yaml and restarting looks
exactly like a change with no effect, which is the failure mode that cost real
time on the Kalman side of this before it was found.

Only the estimate is state now. Weights, covariances and conformal scores are
restored; tuning is re-applied from config on every load. The conformal deques
are rebuilt when the configured window changes, preserving their contents.

load_dict also skips heads for a target or horizon this build no longer has,
rather than resurrecting them from a stale file.

Found while implementing a damped-trend ensemble member, which was then
abandoned: see the following note.

tests/test_models.py

@@ -346,3 +346,54 @@ def test_refit_phase_rotates_and_survives_serialisation():
     back = NowcastEnsemble(CONFIG.model.targets, CONFIG.model.horizons_s, CONFIG.model)
     back.load_dict(ens.to_dict())
     assert back.refit_phase == 2, "a restart must not reset the stride to phase 0 forever"
+
+
+def test_model_tuning_comes_from_config_not_from_the_state_file():
+    """The same defect that made a Kalman retune silently do nothing.
+
+    RecursiveLeastSquares.from_dict and AdaptiveConformal.from_dict restore
+    lambda, delta, alpha, gamma and the conformal window alongside their data,
+    and load_dict replaces the config-built heads with those. So every one of
+    those knobs was immutable on any station that already had state: edit
+    config.yaml, restart, and the old value comes straight back.
+    """
+    from ashvale.config import load_config
+    from ashvale.models.nowcast import NowcastEnsemble
+
+    cfg = load_config()
+    old = cfg.model
+    ens = NowcastEnsemble(old.targets, old.horizons_s, old)
+    saved = ens.to_dict()
+
+    import copy
+    new = copy.deepcopy(old)
+    new.rls_forgetting = 0.995
+    new.rls_delta = 55.0
+    new.conformal_alpha = 0.20
+    new.conformal_gamma = 0.05
+    new.conformal_window = 250
+
+    back = NowcastEnsemble(new.targets, new.horizons_s, new)
+    back.load_dict(saved)
+    head = next(iter(back.heads.values()))
+    assert head.model.lam == 0.995, "state file pinned the forgetting factor"
+    assert head.model.delta == 55.0, "state file pinned the RLS prior"
+    assert head.conformal.alpha_target == 0.20
+    assert head.conformal.gamma == 0.05
+    assert head.conformal.scores.maxlen == 250
+
+
+def test_loading_state_ignores_heads_this_build_no_longer_has():
+    """A stale state file must not resurrect a retired target or horizon."""
+    from ashvale.config import load_config
+    from ashvale.models.nowcast import NowcastEnsemble
+
+    cfg = load_config().model
+    ens = NowcastEnsemble(cfg.targets, cfg.horizons_s, cfg)
+    saved = ens.to_dict()
+    saved["heads"].append({**saved["heads"][0], "target": "retired_signal"})
+
+    back = NowcastEnsemble(cfg.targets, cfg.horizons_s, cfg)
+    back.load_dict(saved)
+    assert not any(t == "retired_signal" for t, _ in back.heads)
+    assert len(back.heads) == len(cfg.targets) * len(cfg.horizons_s)