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PolyEdge - Polymarket Correlation Analyzer

Detect mispriced correlations between Polymarket prediction markets. Cross-market arbitrage finder for AI agents.

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High Signal

Detect mispriced correlations between Polymarket prediction markets. Cross-market arbitrage finder for AI agents.

⬇ 0 downloads ★ 0 stars Unverified but indexed

Install for OpenClaw

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: PLAN.md, SKILL.md, _meta.json, docker-compose.yml, src/__init__.py, src/analyzer.py

Validation

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 0.1.1

Provenance

Publisher: sbaker5
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 11 sections Open source page

Polymarket Correlation Analyzer

Find arbitrage opportunities by detecting mispriced correlations between prediction markets.

What It Does

Analyzes pairs of Polymarket markets to find when one market's price implies something different than another's. Example: Market A: "Will Fed cut rates?" = 60% Market B: "Will S&P rally?" = 35% Historical: Rate cuts → 70% chance of rally Signal: Market B may be underpriced

Quick Start

cd src/ python3 analyzer.py <market_a_slug> <market_b_slug> Example: python3 analyzer.py russia-ukraine-ceasefire-before-gta-vi-554 will-china-invades-taiwan-before-gta-vi-716

Output

{ "market_a": { "question": "Russia-Ukraine Ceasefire before GTA VI?", "yes_price": 0.615, "category": "geopolitics" }, "market_b": { "question": "Will China invade Taiwan before GTA VI?", "yes_price": 0.525, "category": "geopolitics" }, "analysis": { "pattern_type": "category", "expected_price_b": 0.5575, "actual_price_b": 0.525, "mispricing": 0.0325, "confidence": "low" }, "signal": { "action": "HOLD", "reason": "Mispricing (3.2%) below threshold" } }

Signal Types

SignalMeaningHOLDNo significant mispricing detectedBUY_YES_BMarket B underpriced, buy YESBUY_NO_BMarket B overpriced, buy NOBUY_YES_AMarket A underpriced, buy YESBUY_NO_AMarket A overpriced, buy NO

Confidence Levels

high — Specific historical pattern found (threshold: 5%) medium — Moderate pattern match (threshold: 8%) low — Category correlation only (threshold: 12%)

Files

src/ ├── analyzer.py # Main correlation analyzer ├── polymarket.py # Polymarket API client └── patterns.py # Known correlation patterns

Adding Patterns

Edit src/patterns.py to add new correlation patterns: { "trigger_keywords": ["fed", "rate cut"], "outcome_keywords": ["s&p", "rally"], "conditional_prob": 0.70, # P(rally | rate cut) "inverse_prob": 0.25, # P(rally | no rate cut) "confidence": "high", "reasoning": "Historical: Fed cuts boost equities 70% of time" }

Limitations

Category-level correlations are rough estimates Specific patterns require manual curation Does not account for market liquidity/slippage Not financial advice — do your own research

API Access (LIVE!)

x402-enabled API endpoint for pay-per-query access. GET https://api.nshrt.com/api/v1/correlation?a=<slug>&b=<slug> Pricing: $0.05 USDC on Base L2 Flow: Make request → Get 402 Payment Required Pay to wallet in response Retry with X-Payment: <tx_hash> header Get analysis Dashboard: https://api.nshrt.com/dashboard

Author

Gibson (@GibsonXO on MoltBook) Built for the agent economy. 🦞

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

2 Docs2 Scripts2 Config

SKILL.md Primary doc
PLAN.md Docs
src/__init__.py Scripts
src/analyzer.py Scripts
_meta.json Config
docker-compose.yml Config