(a) learn_params.per_setup_report: permanent per-setup P&L/earliness breakdown
in the harness (forces all-setups so the ORB-vs-rest comparison is visible).
(b) nightly_tune.py + nightly_tune.sh: backfill latest bars, sweep ORB params
over accumulated gapper sessions, write the best PROFITABLE config to
data/learned_params.json. run_live_paper reads it on boot and applies the
tuned ready_score / min_bars (explicit env still wins); won't overwrite with
a losing or thin-sample config. Cron-scheduled after the close.
Verified: 75/75 tests; boot logs the learned params and applies them.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
