3032 2026 02 25 Feat Polymarket Quality Ranking Plan 2d6937b1

feat: Improve Polymarket Result Ranking with Quality Signals

Overview

When a topic like "OpenAI" returns 163+ Polymarket events, the current implementation ranks results almost entirely by API return position (75% weight on i), with only a tiny volume boost (0-15%). This means the scoring doesn't reflect actual market quality - a $1M/month market and a $5K/month market get nearly identical relevance scores if they're adjacent in the API response.

Fix the ranking so the most actively traded, fastest-moving, most contested markets bubble to the top.

Problem Statement

Current relevance formula in parse_polymarket_response() (line 328-330):

rank_score = max(0.3, 1.0 - (i * 0.03))  # 75% weight on position
engagement_boost = min(0.15, math.log1p(volume24hr) / 60)
relevance = min(1.0, rank_score * 0.75 + engagement_boost + 0.1)

Issues:

1. Position dominance: A market at position 2 with $0 volume scores higher than a market at position 8 with $1M volume
2. limit parameter is a no-op: The Gamma API always returns exactly 5 events per page regardless of limit. Our DEPTH_CONFIG values (5, 10, 20) do nothing
3. Rich quality signals are ignored: Event-level volume1mo, volume1wk, competitive, commentCount fields are available but unused
4. Price movement is displayed but not scored: Markets with dramatic price swings get no ranking boost
5. No text-similarity scoring: A tangential market that happens to mention "OpenAI" ranks the same as one directly about OpenAI

API Findings (Verified)

Pagination: ?page=N works as 1-indexed offset. Each page returns exactly 5 events. hasMore: true indicates more pages exist. totalResults gives total count.

Event-level quality fields (confirmed via live API):

API naturally sorts well: Page 1 has active high-volume markets ($1M+ monthly volume), page 3 is all dead historical markets ($0 volume). So the API's own ranking is decent - the problem is our scoring doesn't preserve this quality signal.

Proposed Solution

1. Replace position-based relevance with quality-signal relevance

New formula in parse_polymarket_response():

# Text similarity: does the event title contain the search topic?
core = _extract_core_subject(topic).lower()
title_lower = title.lower()
if core and core in title_lower:
    text_score = 1.0
else:
    # Token overlap fallback
    topic_tokens = set(core.lower().split())
    title_tokens = set(title_lower.split())
    overlap = len(topic_tokens & title_tokens)
    text_score = overlap / max(len(topic_tokens), 1)

# Volume signal: log-scaled monthly volume (most stable signal)
vol_score = min(1.0, math.log1p(event_volume1mo) / 16)  # ~$9M = 1.0

# Liquidity signal
liq_score = min(1.0, math.log1p(event_liquidity) / 14)  # ~$1.2M = 1.0

# Price movement: largest absolute change, capped
max_change = max(
    abs(oneDayPriceChange or 0) * 3,    # Daily weighted 3x
    abs(oneWeekPriceChange or 0) * 2,   # Weekly weighted 2x
    abs(oneMonthPriceChange or 0) * 1,  # Monthly weighted 1x
)
movement_score = min(1.0, max_change * 5)  # 20% change = 1.0

# Competitive bonus: markets near 50/50 are more interesting
competitive_score = event_competitive or 0

# Final relevance
relevance = (
    0.30 * text_score +
    0.30 * vol_score +
    0.15 * liq_score +
    0.15 * movement_score +
    0.10 * competitive_score
)

2. Fix DEPTH_CONFIG to use pagination

# Pages to fetch per query (API returns 5 events per page)
DEPTH_CONFIG = {
    "quick": 1,    # 5 events/query, ~5-10 unique after dedup
    "default": 2,  # 10 events/query, ~10-15 unique after dedup
    "deep": 3,     # 15 events/query, ~15-25 unique after dedup
}

3. Use event-level volume fields

Extract volume1mo, volume1wk, liquidity, and competitive from the event object (not just the top market). These are more stable signals than market-level volume24hr.

4. Cap results after re-ranking

After pagination, merge, dedup, and re-ranking, cap at a reasonable number before sending to the scoring pipeline:

RESULT_CAP = {
    "quick": 5,
    "default": 10,
    "deep": 20,
}

Technical Approach

Implementation Plan

Phase 1: Fix pagination and DEPTH_CONFIG

• scripts/lib/polymarket.py - Change DEPTH_CONFIG to page counts: {"quick": 1, "default": 2, "deep": 3}
• scripts/lib/polymarket.py - Add RESULT_CAP dict: {"quick": 5, "default": 10, "deep": 20}
• scripts/lib/polymarket.py - Update _search_single_query() to accept a page parameter
• scripts/lib/polymarket.py - Update search_polymarket() to fetch multiple pages per query in parallel (fire all (query, page) combinations into ThreadPoolExecutor at once)
• scripts/lib/polymarket.py - Apply RESULT_CAP after merge + dedup, before returning events
• tests/test_polymarket.py - Update TestDepthConfig tests for new page-count values

Phase 2: Extract event-level quality signals

• scripts/lib/polymarket.py - In parse_polymarket_response(), extract event-level fields: volume1mo, volume1wk, liquidity, competitive, commentCount
• scripts/lib/polymarket.py - Pass topic to parse_polymarket_response() (already has the parameter, just need to use it)
• fixtures/polymarket_sample.json - Add event-level fields: volume1mo, volume1wk, competitive, commentCount, volume24hr, liquidity

Phase 3: Replace relevance formula

• scripts/lib/polymarket.py - Replace position-based relevance formula with quality-signal formula (text similarity + volume + liquidity + price movement + competitive)
• scripts/lib/polymarket.py - Add _compute_text_similarity(topic, title) helper
• tests/test_polymarket.py - Add TestTextSimilarity test class
• tests/test_polymarket.py - Add TestQualityRanking test: given events with varying volume/liquidity/text-match, verify high-volume title-matching events rank above low-volume tangential ones

Phase 4: Update engagement scoring

• scripts/lib/schema.py - No changes needed (Engagement already has volume and liquidity)
• scripts/lib/polymarket.py - Use event-level volume1mo instead of market-level volume24hr for the volume24hr field passed to normalization (or add a new field)
• scripts/lib/normalize.py - Update normalize_polymarket_items() to use volume1mo for engagement volume if available, fallback to volume24hr

Phase 5: Tests and verification

• Run full test suite
• Manual test: /last30days "OpenAI" --emit=compact - verify top markets are the most actively traded
• Manual test: /last30days "Anthropic" --emit=compact - verify quality ranking
• Manual test: /last30days "best rap songs 2026" --emit=compact - verify graceful zero results
• Run bash scripts/sync.sh to deploy

Acceptance Criteria

• "OpenAI" search surfaces IPO market cap, product announcements, and GPT benchmark markets (high volume) before niche/dead markets
• Markets with $0 monthly volume are filtered out (already handled by liquidity filter, but verify)
• DEPTH_CONFIG actually affects result count (quick=5, default=10, deep=~20)
• Price movement is factored into ranking (markets with large swings rank higher)
• Text-matching markets rank above tangential keyword matches
• All existing tests pass (71 polymarket + full suite: 218 passed, 5 pre-existing failures)
• No performance regression - pagination adds latency but stays within timeout budgets

Dependencies & Risks

No blockers. This is a scoring/ranking improvement within the existing Polymarket module. No new API keys, no new dependencies.

Risk: Over-tuning the formula. The weights (0.30/0.30/0.15/0.15/0.10) are educated guesses. May need iteration after testing with real queries. Mitigation: the formula is in one place (parse_polymarket_response) and easy to adjust.

Risk: Pagination latency. Deep mode with 3 pages x 4 queries = 12 API calls. All run in parallel via ThreadPoolExecutor. Gamma API is fast (~200-500ms per call), so worst case ~1-2s total. Well within the 45s deep timeout.

Sources & References

• Polymarket Gamma API: GET https://gamma-api.polymarket.com/public-search?q={topic}&page={N}
• Current implementation: scripts/lib/polymarket.py
• Scoring pipeline: scripts/lib/score.py
• Original Polymarket plan: docs/plans/2026-02-25-feat-polymarket-prediction-market-source-plan.md