polymarket-smart-money
v1.0.0Discover, analyze, and filter Polymarket smart money wallets. Use when asked to find profitable traders, analyze wallet addresses, detect market makers or HFT bots, assess copy-trading reliability, run the smart money discovery pipeline, or check if a Polymarket address is suitable for copy trading....
Sourced from ClawHub,
Authored by griffithkk3-del
Installation
Polymarket Smart Money Discovery & Analysis
Discover profitable Polymarket traders, filter out market makers and HFT bots, and assess copy-trading reliability.
Project Location
{baseDir}/../../agents/polymarket-bot/PolyAnalysis/
Activate the virtualenv before running any script:
cd {baseDir}/../../agents/polymarket-bot/PolyAnalysis
source .venv/bin/activate
Quick Commands
Analyze a single wallet
python analyze.py <address>
python analyze.py <address> --force # force full refresh
python analyze.py <address> --json # JSON output
The report includes: - PnL, ROI, win rate - Entry timing analysis - Copy trading score (0-100) - MM/HFT detection (MM Score, is_market_maker, is_hft) - Copy reliability label (🟢 高可靠 / 🟡 中可靠 / 🔴 低可靠 / ❌ 不可跟单)
Discover smart money (batch)
python analyze.py discover --top-n 10
python analyze.py discover --top-n 20 --min-profit 100000 --min-volume 500000
python analyze.py discover --top-n 10 --output-json --output-file smart_money.json
Discovery pipeline: 1. Fetch from Polymarket v1 Leaderboard API (7 strategies) 2. Deduplicate across dimensions 3. Stage 1 pre-filter: PnL/Vol < 3% → exclude (likely market maker) 4. Fetch activity data for remaining addresses 5. Stage 2 deep analysis: MM Score + HFT detection 6. Exclude "❌ 不可跟单" addresses 7. Rank by copy trading score
Cache management
python analyze.py cache list
python analyze.py cache stats
python analyze.py cache clear <address>
python analyze.py cache clear --all
Filtering Pipeline
Stage 1: Pre-filter (from leaderboard data, zero cost)
| Rule | Threshold | Rationale |
|---|---|---|
| PnL/Vol ratio | < 3% → exclude | Market makers earn tiny spreads on huge volume |
| VOL-only listing | exclude | Only on volume leaderboard, not on any PNL leaderboard |
Stage 2: Deep analysis (from activity data)
MM Score (0-100, weighted)
| Indicator | Weight | Market Maker Signal |
|---|---|---|
| PnL/Vol ratio | 30% | < 1% |
| Decision frequency (positions/day) | 25% | > 50/day |
| Buy/sell balance | 20% | > 0.7 (near 1:1) |
| Avg holding time | 15% | < 1 hour |
| Trade amount uniformity (CV) | 10% | < 0.3 |
MM Score > 50 → flagged as market maker.
Important: Decision frequency uses positions per day, not trades per day. A directional trader may place hundreds of trades to build one position (dollar-cost averaging). What matters is how many independent position decisions they make.
HFT Detection (2 of 3 conditions)
| Condition | Threshold |
|---|---|
| Decision frequency | > 10 positions/day |
| Median holding time | < 4 hours |
| Median position size | < $50 |
Copy Reliability Labels
| Label | Conditions |
|---|---|
| 🟢 高可靠 | Holding > 24h, < 2 positions/day, PnL/Vol > 10% |
| 🟡 中可靠 | Holding 4-24h, < 5 positions/day |
| 🔴 低可靠 | Holding < 4h or > 5 positions/day |
| ❌ 不可跟单 | MM Score > 50 or HFT detected |
Data Sources
| API | Base URL | Auth | Use |
|---|---|---|---|
| Gamma API | gamma-api.polymarket.com | None | Markets, events, search |
| Data API v1 | data-api.polymarket.com/v1 | None | Leaderboard, positions, trades, PnL |
| CLOB API | clob.polymarket.com | For trading | Orderbook, prices |
Key endpoints
GET /v1/leaderboard?category=OVERALL&timePeriod=ALL&orderBy=PNL&limit=50&offset=0
GET /positions?user={address}
GET /closed-positions?user={address}
GET /activity?user={address}
GET /profit-loss?user={address}
On-Chain Data (Phase 2)
Alchemy RPC configured in .env:
POLYGON_RPC_URL=https://polygon-mainnet.g.alchemy.com/v2/<key>
CTF Exchange contract: 0x4bFb41d5B3570DeFd03C39a9A4D8dE6Bd8B8982E
Key event: OrderFilled — extract maker/taker addresses and trade amounts from on-chain logs.
Free tier limit: 10 blocks per eth_getLogs call.
Architecture
polycopilot/
├── fetcher.py # Data fetching (incremental + full)
├── processor.py # Trade processing, MarketReport generation
├── analyzer.py # Scoring, MM/HFT detection, copy reliability
├── discovery.py # Multi-source wallet discovery + filtering
└── cache.py # Disk cache for incremental updates
Interpreting Results
When presenting results to users:
- Always show the copy reliability label prominently
- For ❌ addresses, explain why (MM Score, HFT indicators)
- For 🟢 addresses, highlight key strengths (holding time, PnL/Vol efficiency)
- Compare addresses using the copy trading score (0-100, A/B/C/D grades)
- Use Chinese for user-facing output (the user prefers Chinese)