feat(manifold): add persistent cooldowns to reduce redundant evaluations
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The trading loop re-evaluated the same ~22 politics/tech markets every ~60s,
flooding manifold_match_audit with ~76k rows (~3,500 attempts/market) of which
none carried new information, making the metrics uninterpretable.
Add per-market persistent cooldowns:
- New table manifold_eval_cooldown (poly_market_id PK, last_evaluated_at,
last_status, retry_after, cooldown_reason) created via run_migrations.
- bayesian.evaluate() consults the cooldown BEFORE calling the matcher and skips
the call entirely while now() < retry_after — no matcher call, no audit row,
no signal (equivalent to no_results). After a real evaluation it upserts the
cooldown with a verdict-dependent backoff: no_results/low_score/outcome_mismatch/
ambiguous_inversion -> 24h, conditional_market -> 7d, accepted -> 1h.
- manifold.py stays a pure client/matcher with no DB access.
- New db methods get_manifold_cooldown / upsert_manifold_cooldown.
- /api/metrics/manifold-matches summary gains unique_markets
{evaluated, accepted, coverage_rate} for per-market (not per-attempt) coverage.
Matching logic, MANIFOLD_MATCHER_VERSION, MANIFOLD_LOGODDS_WEIGHT, edge/exposure
thresholds and existing audit rows are unchanged.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Claude Opus 4.8
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@@ -226,6 +226,10 @@ async def get_manifold_matches():
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summary.trades_dominated_by_mfld — non-excluded accepted-match trades where
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feat_mfld_lo is the largest signal (consistent with attribution/features,
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which also exclude excluded_from_metrics trades).
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summary.unique_markets — distinct-market coverage (per-market, not per-attempt):
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evaluated — DISTINCT poly_market_id in manifold_match_audit (all versions)
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accepted — DISTINCT poly_market_id accepted by the current matcher
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coverage_rate — accepted / evaluated (null when evaluated=0)
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recent_matches: last 50 rows from manifold_match_audit, newest first, each
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tagged with matcher_version.
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