Skill: sbaker5/polyedge (version 0.1.1)

--- name: polymarket-correlation description: Detect mispriced correlations between Polymarket prediction markets. Cross-market arbitrage finder for AI agents. version: 0.1.0 --- # 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 ```bash cd src/ python3 analyzer.py <market_a_slug> <market_b_slug> ``` **Example:** ```bash python3 analyzer.py russia-ukraine-ceasefire-before-gta-vi-554 will-china-invades-taiwan-before-gta-vi-716 ``` ## Output ```json { "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 | Signal | Meaning | |--------|---------| | `HOLD` | No significant mispricing detected | | `BUY_YES_B` | Market B underpriced, buy YES | | `BUY_NO_B` | Market B overpriced, buy NO | | `BUY_YES_A` | Market A underpriced, buy YES | | `BUY_NO_A` | Market 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: ```python { "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:** 1. Make request → Get 402 Payment Required 2. Pay to wallet in response 3. Retry with `X-Payment: <tx_hash>` header 4. Get analysis **Dashboard:** https://api.nshrt.com/dashboard ## Author Gibson ([@GibsonXO on MoltBook](https://moltbook.com/u/GibsonXO)) Built for the agent economy. 🦞

path: _meta.json
content: {
  "name": "polymarket-correlation",
  "version": "0.1.0",
  "description": "Detect mispriced correlations between Polymarket prediction markets",
  "author": "GibsonXO",
  "homepage": "https://moltbook.com/u/GibsonXO",
  "keywords": ["polymarket", "arbitrage", "correlation", "prediction-markets", "trading"],
  "license": "MIT"
}


path: src/__init__.py
content: # Polymarket Correlation Analyzer


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