feat(polymarket): outcome-aware scoring and synthesis instructions
- _compute_text_similarity() now checks outcome names with bidirectional substring matching (0.85) and token overlap (0.7), not just event titles - Collect outcomes from ALL active markets per event, filter to >1% price - Reorder outcome_prices to surface topic-matching outcome before top-3 truncation - Add SKILL.md "Prediction Markets" synthesis section with structural/long-term market preference, domain examples, citation format, and narrative weaving - Add Polymarket to citation priority list between HN and Web - Update stats box template to show up to 5 market odds - Fix render.py "vol24h" label to "volume" - Add NCAA seed fixture event for outcome-only matching tests - 82 polymarket tests pass (14 new) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Matt Van Horn
+105
-8
@@ -507,6 +507,79 @@ class TestTextSimilarity(unittest.TestCase):
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score = polymarket._compute_text_similarity("last 7 days Arizona", "Will Arizona win?")
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self.assertEqual(score, 1.0)
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def test_outcome_substring_match(self):
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"""Topic 'Arizona' should match outcome 'Arizona' even when title has no overlap."""
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score = polymarket._compute_text_similarity(
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"Arizona",
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"Who will be the #1 overall seed?",
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outcomes=["Duke", "Arizona", "Houston"],
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)
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self.assertEqual(score, 0.85)
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def test_outcome_bidirectional_match(self):
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"""Topic 'Arizona Basketball' should match outcome 'Arizona' (outcome in core)."""
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score = polymarket._compute_text_similarity(
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"Arizona Basketball",
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"Who will be the #1 overall seed?",
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outcomes=["Duke", "Arizona", "Houston"],
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)
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self.assertEqual(score, 0.85)
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def test_outcome_token_overlap(self):
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"""Partial token overlap with outcome gets 0.7 when no substring match."""
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score = polymarket._compute_text_similarity(
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"Iran War",
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"Unrelated geopolitics title",
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outcomes=["War continues", "Peace deal"],
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)
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self.assertEqual(score, 0.7)
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def test_outcome_no_match(self):
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"""No outcome match falls through to title token overlap."""
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score = polymarket._compute_text_similarity(
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"Arizona Basketball",
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"Will AI regulation pass in 2026?",
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outcomes=["Yes", "No"],
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)
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self.assertEqual(score, 0.0)
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def test_outcome_low_price_filtered_by_caller(self):
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"""Outcomes with price <= 1% should be filtered by the caller, not this function."""
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# This function doesn't filter - it trusts the caller to pass only relevant outcomes
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score = polymarket._compute_text_similarity(
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"Arizona",
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"Unrelated title",
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outcomes=["Arizona"],
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)
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self.assertEqual(score, 0.85)
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def test_title_match_still_beats_outcome(self):
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"""Title substring match (1.0) takes priority over outcome match (0.85)."""
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score = polymarket._compute_text_similarity(
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"Arizona",
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"Will Arizona win the tournament?",
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outcomes=["Arizona", "Duke"],
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)
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self.assertEqual(score, 1.0)
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def test_empty_outcomes(self):
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"""Empty outcomes list falls through to title token overlap."""
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score = polymarket._compute_text_similarity(
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"Arizona Basketball",
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"Unrelated title",
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outcomes=[],
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)
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self.assertEqual(score, 0.0)
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def test_none_outcomes(self):
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"""None outcomes falls through to title token overlap."""
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score = polymarket._compute_text_similarity(
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"Arizona Basketball",
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"Unrelated title",
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outcomes=None,
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)
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self.assertEqual(score, 0.0)
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class TestQualityRanking(unittest.TestCase):
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"""Verify quality-signal ranking: high-volume matching events rank above tangential ones."""
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@@ -520,22 +593,23 @@ class TestQualityRanking(unittest.TestCase):
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"""Arizona markets should rank above AI regulation when topic is 'Arizona Basketball'."""
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items = polymarket.parse_polymarket_response(self.sample, topic="Arizona Basketball")
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titles = [item["title"] for item in items]
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# Arizona events should come before tangential AI regulation event
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arizona_indices = [i for i, t in enumerate(titles) if "Arizona" in t or "Big 12" in t]
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# Arizona events (including outcome-matched ones like NCAA seed) should come before tangential
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arizona_indices = [i for i, t in enumerate(titles) if "Arizona" in t or "Big 12" in t or "NCAA" in t]
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tangential_indices = [i for i, t in enumerate(titles) if "AI regulation" in t]
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if tangential_indices:
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self.assertTrue(max(arizona_indices) < min(tangential_indices),
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f"Arizona markets should rank above tangential. Order: {titles}")
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def test_high_volume_ranks_above_low_volume(self):
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"""Among matching events, higher volume should rank higher."""
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"""Among title-matched events, higher volume should rank higher."""
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items = polymarket.parse_polymarket_response(self.sample, topic="Arizona Basketball")
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# Arizona Big 12 has $3.5M monthly volume, Arizona NCAA has $800K
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# Arizona Big 12 Championship has $3.5M monthly volume, Arizona NCAA Tournament has $800K
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# Both have "Arizona" in the title (text_score=1.0), so volume breaks the tie
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big12 = [i for i, item in enumerate(items) if "Big 12 Championship" in item["title"]]
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ncaa = [i for i, item in enumerate(items) if "NCAA Tournament" in item["title"]]
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if big12 and ncaa:
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self.assertLess(big12[0], ncaa[0],
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"Higher volume Big 12 should rank above lower volume NCAA")
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ncaa_win = [i for i, item in enumerate(items) if item["title"] == "Will Arizona win the NCAA Tournament?"]
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if big12 and ncaa_win:
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self.assertLess(big12[0], ncaa_win[0],
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"Higher volume Big 12 Championship should rank above lower volume NCAA Tournament win")
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def test_result_cap_applied(self):
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"""Parse should respect the _cap from search response."""
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@@ -558,6 +632,29 @@ class TestQualityRanking(unittest.TestCase):
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relevances = [item["relevance"] for item in items]
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self.assertEqual(relevances, sorted(relevances, reverse=True))
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def test_ncaa_seed_found_via_outcome_matching(self):
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"""NCAA seed market should be found when Arizona is an outcome but not in title."""
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items = polymarket.parse_polymarket_response(self.sample, topic="Arizona Basketball")
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titles = [item["title"] for item in items]
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self.assertIn("Who will be the #1 overall seed in the 2026 NCAA Tournament?", titles)
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def test_ncaa_seed_ranks_above_tangential(self):
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"""NCAA seed market (outcome match) should rank above AI regulation (no match)."""
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items = polymarket.parse_polymarket_response(self.sample, topic="Arizona Basketball")
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titles = [item["title"] for item in items]
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seed_idx = titles.index("Who will be the #1 overall seed in the 2026 NCAA Tournament?")
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tangential = [i for i, t in enumerate(titles) if "AI regulation" in t]
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if tangential:
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self.assertLess(seed_idx, tangential[0],
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f"NCAA seed should rank above tangential. Order: {titles}")
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def test_outcome_reordering_surfaces_topic(self):
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"""Arizona should be surfaced to front of outcome_prices when topic matches."""
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items = polymarket.parse_polymarket_response(self.sample, topic="Arizona Basketball")
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seed_market = [i for i in items if "seed" in i["title"].lower()][0]
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# Arizona should be first in outcome_prices (reordered from position 2)
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self.assertEqual(seed_market["outcome_prices"][0][0], "Arizona")
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class TestNormalizePolymarketVolume1mo(unittest.TestCase):
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"""Verify normalization prefers volume1mo over volume24hr for engagement."""
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