| name | polymarket-arbitrage-cn |
| description | Polymarket 套利 | Polymarket Arbitrage. 预测市场套利机会 | Prediction market arbitrage opportunities. 自动发现价格差异 | Auto discover price differences. 触发词：Polymarket、预测市场、套利、arbitrage. |
| metadata | {"openclaw":{"emoji":"📊","fork-of":"https://clawhub.ai"}} |
Polymarket Arbitrage
Find and execute arbitrage opportunities on Polymarket prediction markets.
Quick Start
1. Paper Trading (Recommended First Step)
Run a single scan to see current opportunities:
cd skills/polymarket-arbitrage
pip install requests beautifulsoup4
python scripts/monitor.py --once --min-edge 3.0
View results in polymarket_data/arbs.json
2. Continuous Monitoring
Monitor every 5 minutes and alert on new opportunities:
python scripts/monitor.py --interval 300 --min-edge 3.0
Stop with Ctrl+C
3. Understanding Results
Each detected arbitrage includes:
- net_profit_pct: Edge after 2% fees
- risk_score: 0-100, lower is better
- volume: Market liquidity
- action: What to do (buy/sell all outcomes)
Good opportunities:
- Net profit: 3-5%+
- Risk score: <50
- Volume: $1M+
- Type:
math_arb_buy (safer)
Arbitrage Types Detected
Math Arbitrage (Primary Focus)
Type A: Buy All Outcomes (prob sum < 100%)
- Safest type
- Guaranteed profit if executable
- Example: 48% + 45% = 93% → 7% edge, ~5% net after fees
Type B: Sell All Outcomes (prob sum > 100%)
- Riskier (requires liquidity)
- Need capital to collateralize
- Avoid until experienced
See references/arbitrage_types.md for detailed examples and strategies.
Cross-Market Arbitrage
Same event priced differently across markets (not yet implemented - requires semantic matching).
Orderbook Arbitrage
Requires real-time orderbook data (homepage shows midpoints, not executable prices).
Scripts
fetch_markets.py
Scrape Polymarket homepage for active markets.
python scripts/fetch_markets.py --output markets.json --min-volume 50000
Returns JSON with market probabilities, volumes, and metadata.
detect_arbitrage.py
Analyze markets for arbitrage opportunities.
python scripts/detect_arbitrage.py markets.json --min-edge 3.0 --output arbs.json
Accounts for:
- 2% taker fees (per leg)
- Multi-outcome fee multiplication
- Risk scoring
monitor.py
Continuous monitoring with alerting.
python scripts/monitor.py --interval 300 --min-edge 3.0 [--alert-webhook URL]
Features:
- Fetches markets every interval
- Detects arbitrage
- Alerts on NEW opportunities only (deduplicates)
- Saves state to
polymarket_data/
Workflow Phases
Phase 1: Paper Trading (1-2 weeks)
Goal: Understand opportunity frequency and quality
- Run monitor 2-3x per day
- Log opportunities in spreadsheet
- Check if they're still available when you look
- Calculate what profit would have been
Decision point: If seeing 3-5 good opportunities per week, proceed to Phase 2.
Phase 2: Micro Testing ($50-100 CAD)
Goal: Learn platform mechanics
- Create Polymarket account
- Deposit $50-100 in USDC
- Manual trades only (no automation)
- Max $5-10 per opportunity
- Track every trade in spreadsheet
Decision point: If profitable after 20+ trades, proceed to Phase 3.
Phase 3: Scale Up ($500 CAD)
Goal: Increase position sizes
- Increase bankroll to $500
- Max 5% per trade ($25)
- Still manual execution
- Implement strict risk management
Phase 4: Automation (Future)
Requires:
- Wallet integration (private key management)
- Polymarket API or browser automation
- Execution logic
- Monitoring infrastructure
Only consider after consistently profitable manual trading.
See references/getting_started.md for detailed setup instructions.
Risk Management
Critical Rules
- Maximum position size: 5% of bankroll per opportunity
- Minimum edge: 3% net (after fees)
- Daily loss limit: 10% of bankroll
- Focus on buy arbs: Avoid sell-side until experienced
Red Flags
- Edge >10% (likely stale data)
- Volume <$100k (liquidity risk)
- Probabilities recently updated (arb might close)
- Sell-side arbs (capital + liquidity requirements)
Fee Structure
Polymarket charges:
- Maker fee: 0%
- Taker fee: 2%
Conservative assumption: 2% per leg (assume taker)
Breakeven calculation:
- 2-outcome market: 2% × 2 = 4% gross edge needed
- 3-outcome market: 2% × 3 = 6% gross edge needed
- N-outcome market: 2% × N gross edge needed
Target: 3-5% NET profit (after fees)
Common Issues
"High edge but disappeared"
Homepage probabilities are stale or represent midpoints, not executable prices. This is normal. Real arbs disappear in seconds.
"Can't execute at displayed price"
Liquidity issue. Low-volume markets show misleading probabilities. Stick to $1M+ volume markets.
"Edge is too small after fees"
Increase --min-edge threshold. Try 4-5% for more conservative filtering.
Files and Data
All monitoring data stored in ./polymarket_data/:
markets.json - Latest market scanarbs.json - Detected opportunitiesalert_state.json - Deduplication state (which arbs already alerted)
Advanced Topics
Telegram Integration (Future)
Pass webhook URL to monitor script for alerts:
python scripts/monitor.py --alert-webhook "https://api.telegram.org/bot<token>/sendMessage?chat_id=<id>"
Position Sizing
For a 2-outcome math arb with probabilities p₁ and p₂ where p₁ + p₂ < 100%:
Optimal allocation:
- Bet on outcome 1: (100% / p₁) / [(100%/p₁) + (100%/p₂)] of capital
- Bet on outcome 2: (100% / p₂) / [(100%/p₁) + (100%/p₂)] of capital
This ensures equal profit regardless of which outcome wins.
Simplified rule: For small edges, split capital evenly across outcomes.
Execution Speed
Arbs disappear fast. If planning automation:
- Use websocket connections (not polling)
- Place limit orders simultaneously
- Have capital pre-deposited
- Monitor gas fees on Polygon
Resources
Support
For skill issues:
- Check
references/arbitrage_types.md for strategy details
- Check
references/getting_started.md for setup help
- Review output files in
polymarket_data/
- Ensure dependencies installed:
pip install requests beautifulsoup4
