feat(api): add /api/metrics/manifold-coverage by-category endpoint
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Measure real Manifold coverage per semantic market category counted by UNIQUE market (not audit rows, which are inflated by repeated re-evaluation). Base table is manifold_match_audit filtered to v3_outcome_guard; each poly_market_id is collapsed to one row, LEFT JOIN trades for family_key, category inferred from family_key when present else from poly_question (gubernatorial / mayoral / senate / primary-republican / primary-democrat / big-tech / geopolitics / other). Per category: unique_evaluated / unique_accepted / unique_rejected / unique_no_results / coverage_rate, ordered by unique_evaluated DESC. Summary adds total_unique_evaluated, total_unique_accepted, overall_coverage_rate, categories_with_coverage. Read-only: new db method + endpoint only; matcher, scheduler, cooldowns, thresholds and exposure untouched. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
chemavx
co-authored by
Claude Opus 4.8
Claude Opus 4.8
parent
98abd96fd2
commit
823914789d
+26
@@ -243,6 +243,32 @@ async def get_manifold_matches():
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return data
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@app.get("/api/metrics/manifold-coverage")
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async def get_manifold_coverage():
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"""Manifold coverage by semantic market category, counted by UNIQUE market.
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Unlike the raw audit counters (which count per-attempt and are inflated by the
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trading loop re-evaluating the same markets), this measures real coverage:
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how many DISTINCT markets in each category the matcher found an accepted
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Manifold counterpart for. Base table is manifold_match_audit filtered to the
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current matcher (v3_outcome_guard); category is inferred from trade family_key
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when available, otherwise from the question text.
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coverage_by_category — one entry per category, ordered by unique_evaluated DESC:
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category — gubernatorial | mayoral | senate | primary-republican |
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primary-democrat | big-tech | geopolitics | other
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unique_evaluated — distinct markets audited in this category
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unique_accepted — distinct markets with at least one accepted match
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unique_rejected — distinct markets with at least one rejected match
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unique_no_results — distinct markets with at least one no_results outcome
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coverage_rate — unique_accepted / unique_evaluated (null if evaluated=0)
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summary — total_unique_evaluated, total_unique_accepted, overall_coverage_rate
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(null if nothing evaluated), categories_with_coverage (categories with
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unique_accepted > 0).
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"""
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return await db.get_manifold_coverage_by_category()
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@app.get("/api/summary")
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async def get_summary():
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"""Dashboard summary card data.
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+107
@@ -688,6 +688,113 @@ class Database:
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"recent_matches": [dict(r) for r in rows],
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}
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async def get_manifold_coverage_by_category(self) -> dict:
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"""Manifold coverage by semantic market category, counted by UNIQUE market.
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Base table is manifold_match_audit filtered to the current matcher
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(v3_outcome_guard). Each poly_market_id is collapsed to one row first, so
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a market is counted once regardless of how many audit attempts or trades it
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has — this measures coverage, not retry volume.
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Category is inferred from the market's trade family_key when available, else
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from poly_question (LEFT JOIN: audited markets that never produced a trade
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are kept). Buckets (accepted/rejected/no_results) are not mutually exclusive
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at the market level — a market that was no_results then later rejected counts
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in both — matching COUNT(DISTINCT CASE WHEN status=...) semantics.
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"""
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async with self._pool.acquire() as conn:
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rows = await conn.fetch("""
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WITH audit AS (
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SELECT
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poly_market_id,
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MAX(poly_question) AS poly_question,
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bool_or(match_status = 'accepted') AS has_accepted,
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bool_or(match_status = 'rejected') AS has_rejected,
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bool_or(match_status = 'no_results') AS has_no_results
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FROM manifold_match_audit
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WHERE matcher_version = 'v3_outcome_guard'
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GROUP BY poly_market_id
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),
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fam AS (
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SELECT market_id, MAX(family_key) AS family_key
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FROM trades
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GROUP BY market_id
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),
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categorized AS (
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SELECT
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a.has_accepted, a.has_rejected, a.has_no_results,
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CASE
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WHEN f.family_key ILIKE '%gubernatorial%' THEN 'gubernatorial'
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WHEN f.family_key ILIKE '%mayoral%' THEN 'mayoral'
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WHEN f.family_key ILIKE '%senate%' THEN 'senate'
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WHEN f.family_key ILIKE '%republican%' THEN 'primary-republican'
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WHEN f.family_key ILIKE '%democrat%' THEN 'primary-democrat'
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WHEN f.family_key ILIKE '%openai%'
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OR f.family_key ILIKE '%nvidia%'
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OR f.family_key ILIKE '%anthropic%' THEN 'big-tech'
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-- family_key NULL or unmatched → infer from question
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WHEN a.poly_question ILIKE '%governor%'
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OR a.poly_question ILIKE '%gubernatorial%' THEN 'gubernatorial'
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WHEN a.poly_question ILIKE '%mayor%'
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OR a.poly_question ILIKE '%mayoral%' THEN 'mayoral'
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WHEN a.poly_question ILIKE '%senate%' THEN 'senate'
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WHEN a.poly_question ILIKE '%republican primary%' THEN 'primary-republican'
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WHEN a.poly_question ILIKE '%democratic primary%'
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OR a.poly_question ILIKE '%democrat primary%' THEN 'primary-democrat'
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WHEN a.poly_question ILIKE '%openai%'
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OR a.poly_question ILIKE '%nvidia%'
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OR a.poly_question ILIKE '%anthropic%' THEN 'big-tech'
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WHEN a.poly_question ILIKE '%russia%'
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OR a.poly_question ILIKE '%ukraine%'
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OR a.poly_question ILIKE '%israel%'
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OR a.poly_question ILIKE '%ceasefire%'
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OR a.poly_question ILIKE '%military%' THEN 'geopolitics'
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ELSE 'other'
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END AS category
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FROM audit a
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LEFT JOIN fam f ON f.market_id = a.poly_market_id
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)
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SELECT
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category,
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COUNT(*) AS unique_evaluated,
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COUNT(*) FILTER (WHERE has_accepted) AS unique_accepted,
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COUNT(*) FILTER (WHERE has_rejected) AS unique_rejected,
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COUNT(*) FILTER (WHERE has_no_results) AS unique_no_results
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FROM categorized
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GROUP BY category
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ORDER BY unique_evaluated DESC
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""")
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coverage_by_category = []
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total_evaluated = 0
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total_accepted = 0
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categories_with_coverage = 0
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for r in rows:
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evaluated = int(r["unique_evaluated"] or 0)
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accepted = int(r["unique_accepted"] or 0)
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total_evaluated += evaluated
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total_accepted += accepted
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if accepted > 0:
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categories_with_coverage += 1
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coverage_by_category.append({
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"category": r["category"],
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"unique_evaluated": evaluated,
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"unique_accepted": accepted,
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"unique_rejected": int(r["unique_rejected"] or 0),
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"unique_no_results": int(r["unique_no_results"] or 0),
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"coverage_rate": (accepted / evaluated) if evaluated else None,
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})
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return {
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"coverage_by_category": coverage_by_category,
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"summary": {
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"total_unique_evaluated": total_evaluated,
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"total_unique_accepted": total_accepted,
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"overall_coverage_rate": (total_accepted / total_evaluated) if total_evaluated else None,
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"categories_with_coverage": categories_with_coverage,
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},
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}
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def _f(v) -> Optional[float]:
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"""None-safe float cast for asyncpg Decimal/None values."""
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