feat(replay): R2 outcomes + calibration metrics

Scores every archived estimate against reality — the sample multiplier
the phase plan calls for: Brier/log-loss of estimated_prob benchmarked
against the market price (prior_prob) on the same rows, over ALL
evaluations with a resolved outcome, not just executed trades.

- schema.sql: market_outcomes (one row per resolved market; outcome =
  final YES price 1.0/0.0, UMA-final only)
- bot/outcomes.py: CLI (python -m bot.outcomes) with two phases —
  fetch resolutions for archived markets via the existing
  get_market_resolution() (open/disputed/ambiguous markets simply retry
  next invocation; no data-loss urgency, Gamma reports past resolutions
  at any time), then compute calibration: Brier micro (per evaluation) /
  macro (per market — the honest sample size given ~1 eval/min
  autocorrelation), log-loss with 1e-9 clipping, per-category breakdown.
  --run-id scores a replay run's re-estimates instead of the archive
  (counterfactual calibration).
- db.py: 4 accessors (pending markets, outcome upsert, coverage,
  calibration rows for archive or run)
- tests: 12 new (116 total green); compute_calibration is a pure
  function driven by literals

No prod behavior change: the bot never imports bot.outcomes; the only
shared surface is the idempotent schema migration (dry-run BEGIN/ROLLBACK
clean against prod).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

bot/data/schema.sql

@@ -431,3 +431,24 @@ CREATE TABLE IF NOT EXISTS replay_decisions (
 
 CREATE INDEX IF NOT EXISTS idx_replay_decisions_run ON replay_decisions(run_id);
 CREATE INDEX IF NOT EXISTS idx_replay_decisions_mkt ON replay_decisions(market_id);
+
+-- ─────────────────────────────────────────────────────────────────────────────
+-- Replay R2: outcomes + calibration metrics
+--
+-- One row per resolved market, fetched from the Gamma API via
+-- get_market_resolution() (UMA-final only: a market closed but still in
+-- proposal/dispute is not stored).  outcome is the final YES price:
+-- 1.0 = YES won, 0.0 = NO won.
+--
+-- Joining signals (or replay_decisions) to market_outcomes scores every
+-- archived estimate against reality — Brier / log-loss of estimated_prob
+-- benchmarked against the market price (prior_prob) on the same rows,
+-- answering "does the model add value over the market?" across ALL
+-- evaluations, not just executed trades.
+-- ─────────────────────────────────────────────────────────────────────────────
+CREATE TABLE IF NOT EXISTS market_outcomes (
+    market_id   TEXT PRIMARY KEY,
+    outcome     DOUBLE PRECISION NOT NULL,
+    resolved_at TIMESTAMPTZ,
+    fetched_at  TIMESTAMPTZ DEFAULT NOW()
+);