据 AlphaMoat 统计,截至 2026-09,PolyEdge - Polymarket Correlation Analyzer 累计下载 4,128 次、获 4 个星标,在全部 5,078 个 AI 技能中排名第 2,441 位,在「其他」分类(51 个)中排名第 35 位。

累计安装

142

累计下载

4,128

星标

全站排名

#2,441

全站前 49% · 「其他」前 69%

平台	累计安装	累计下载	星标	评论
ClawHub ↗	142	4,128	4	-

各平台的计数口径不同,统计时间不一致。

这个技能是做什么的

该工具分析市场对之间的价格关系，输出可能的错价信号。结果依赖输入数据与模型假设，不代表已验证的套利机会；实际决策前应核对市场状态、流动性和分析依据，且不得未经授权下单。本地分析器使用 Python；独立在线 API 为付费路径。估计不计流动性和滑点。

资料发布于 2026-10-29

可以用来做什么

比较相关市场价格
检查模型给出的偏差提示
复核输入与分析假设

什么时候用

需要探索预测市场之间的价格关系时
根据结果行动前核验市场数据、流动性和假设

示例提示词

以下是根据技能文档生成的示例,不代表实际运行结果。

解释该分析器如何寻找相关性偏差，并说明为何信号不能直接视为可执行交易。

月度数据趋势

累计下载4,128

星标4

2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。

月份	累计下载	环比	星标
2026-09	4,128	—	4
2026-08	4,032	+2.3%	4
2026-07	3,940	+5.0%	4
2026-06	3,751	—	4

SKILL.md 原文

以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 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.

展开全部(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. 🦞