FASE 1 — market_family_key() general election fix
General elections now group by office, not by party, so complementary
markets ("Republicans win Ohio governor" / "Democrats win Ohio governor")
share the same family key (ohio-gubernatorial-2026). The second market
is blocked by the occupied_families check rather than traded as independent.
Primaries still keep the party (texas-republican-2026) because each party
runs its own separate primary race.
FASE 2 — Manifold party inversion guard
_detect_party() identifies the winning side in both the Polymarket question
and the matched Manifold title. If they are confirmed opposites (republican
vs democrat), the probability is inverted (1 - prob) before use.
Full audit log per query:
poly_question / manifold_title / manifold_url / match_score /
prob_raw / inverted / prob_final
Root cause of Ohio Manifold:0.95 on both sides: both queries matched the
same Manifold market ("Republicans win Ohio governor" prob=0.95). For the
"Democrats win" query the inversion now produces prob_final=0.05 instead of
blindly applying 0.95 to the wrong direction.
FASE 4 — startup contradiction scan
get_open_position_details() added to db.py. main.py checks all open
positions at startup, warns on any family with >1 position, and recommends
keeping the one with the highest edge_net. No auto-close.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Signal 5: ManifoldClient queries Manifold Markets API for a matching binary
market by keyword overlap (threshold 0.25) and applies a log-odds adjustment
proportional to the divergence from the Polymarket prior.
manifold_log_adj = (log_odds(manifold_prob) - log_odds(prior)) × 0.6
A 30pp divergence (Manifold 0.75 vs Poly 0.45) produces edge_gross ≈ 0.19,
clearing the politics far-horizon regime_min=0.12 after costs. Confidence
boosted +0.08 when Manifold match found.
Per-feature observability: every SKIP_EDGE_NET and TRADE log line now includes
fg=±X.XXX mom=±X.XXX mfld=±X.XXXX news=±X.XXXX
so the contribution of each signal to edge is auditable per market.
Files: bot/data/manifold.py (new), bot/strategy/bayesian.py, bot/main.py
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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