據 AlphaMoat 統計,截至 2026-09,PolyEdge - Polymarket Correlation Analyzer 累計下載 4,128 次、獲 4 個星標,在全部 5,078 個 AI 技能中排名第 2,441 位,在「其他」分類(51 個)中排名第 35 位。

累計安裝

142

累計下載

4128

星標

全站排名

#2,441

全站前 49% · 「其他」前 69%

平台	累計安裝	累計下載	星標	評論
ClawHub ↗	142	4128	4	-

各平台的計數口徑不同,統計時間不一致。

這個技能是做什麼的

該工具分析市場對之間的價格關係，輸出可能的錯價訊號。結果依賴輸入資料與模型假設，不代表已驗證的套利機會；實際決策前應核對市場狀態、流動性和分析依據，且不得未經授權下單。本地分析器使用 Python；獨立線上 API 為付費路徑。估計不計流動性和滑點。

資料釋出於 2026-10-29

可以用來做什麼

比較相關市場價格
檢查模型給出的偏差提示
複核輸入與分析假設

什麼時候用

需要探索預測市場之間的價格關係時
根據結果行動前核驗市場資料、流動性和假設

示例提示詞

以下是根據技能文件生成的示例,不代表實際執行結果。

解釋該分析器如何尋找相關性偏差，並說明為何訊號不能直接視為可執行交易。

月度資料趨勢

累計下載4128

星標4

2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。

月份	累計下載	環比	星標
2026-09	4128	—	4
2026-08	4032	+2.3%	4
2026-07	3940	+5.0%	4
2026-06	3751	—	4

SKILL.md 原文

以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 2026-10-23。

3.1 KB · ClawHub v0.1.1

---
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.

展開全部(共 131 行)

## 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. 🦞