feat(phase6): per-feature signal attribution in log-odds space

Adds feat_fg_lo / feat_mom_lo / feat_news_lo / feat_mfld_lo / feat_btc_dom_lo
to every trade, all normalized to log-odds contribution for direct comparability.

- fg / mom / btc_dom: raw probability-delta × 2 → log-odds
- news / mfld: already log-odds (LOGODDS_WEIGHT already applied), no scaling
- btc_dom tracked separately in bayesian.py instead of bundled in total_adj
- reasoning string updated to fg_lo= / mom_lo= notation for self-documentation

Schema: 5 new DOUBLE PRECISION columns + 2 partial indexes
Stack: TradingSignal → Order → Trade → save_trade all carry feat fields
Startup: backfill_feature_columns() recovers fg/mom/news/mfld from old
  reasoning strings (×2 applied to fg/mom); btc_dom_lo stays NULL for legacy
API: /api/metrics/features — triggered/material split per feature with
  two-level thresholds (0.05 for fg/mom/btc_dom, 0.10 for news/mfld)
API: /api/trades/legacy — exposes pre-Phase-1 trades (edge_net IS NULL)
API: _enrich_trade backward-compat: reads DB columns first, falls back to
  reasoning regex with unit conversion for pre-Phase-6 trades

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

bot/main.py

@@ -369,6 +369,7 @@ async def main() -> None:
     db = Database()
     await db.connect()
     await db.run_migrations()
+    await db.backfill_feature_columns()
 
     poly = PolymarketClient()
     external = ExternalDataClient()