polymarket-correlation

Review·Scanned 2/18/2026

This skill analyzes Polymarket markets for mispriced correlations and exposes a paid HTTP API plus CLI usage. It calls https://gamma-api.polymarket.com and multiple Base RPCs, reads env vars like PAYMENT_WALLET and BASESCAN_API_KEY, and instructs running python3 analyzer.py locally.

from clawhub.ai·v0.1.0·56.0 KB·0 installs
Scanned from 0.1.1 at 4766a3e · Transparency log ↗
$ vett add clawhub.ai/sbaker5/polymarket-correlationReview findings below

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

SignalMeaning
HOLDNo significant mispricing detected
BUY_YES_BMarket B underpriced, buy YES
BUY_NO_BMarket B overpriced, buy NO
BUY_YES_AMarket A underpriced, buy YES
BUY_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:

  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)

Built for the agent economy. 🦞