🚀 Polymarket Arbitrage Bot

Autonomous trading system for cross-platform arbitrage opportunities on Polymarket and Kalshi.

Built with Node.js, Python, and Web3 integration for automated profit generation through market inefficiencies.

✨ Features

🤖 Advanced Automated Detection

• Real-time scanning of Polymarket markets for endgame arbitrage (95-99% probability markets)
• Cross-platform arbitrage detection between Polymarket and Kalshi
• Silent execution mode for continuous background operation
• Smart filtering to identify high-probability opportunities
• Market maker pattern analysis for sophisticated exploitation strategies
• Machine learning opportunity ranking and success prediction

💰 Comprehensive Trading Strategies

• Endgame Arbitrage: Target markets near resolution with extreme probability skew
• Cross-Platform: Exploit price differences between Polymarket and Kalshi
• Market Maker Exploitation: Identify and exploit MM behavior patterns
• ML-Enhanced Selection: Use historical data to improve opportunity selection
• Advanced Risk Management: Kelly Criterion position sizing with adaptive parameters
• Compound Growth: Intelligent reinvestment strategy for exponential scaling

📊 Real-Time Monitoring & Control

• Web Dashboard: Real-time performance monitoring and system control
• WebSocket Updates: Live data streaming for immediate insights
• Risk Analytics: Comprehensive risk metrics and emergency controls
• ML Insights: Actionable intelligence from pattern recognition
• Performance Tracking: Detailed analytics with Sharpe ratio and drawdown analysis

🔧 Technical Architecture

• Node.js detection engines with WebSocket support
• Python wallet management and Web3 integration
• Multi-network support (Ethereum, Polygon, Solana)
• API Integration with Polymarket and Kalshi platforms
• Secure credential management with environment variables

🚀 Quick Start

Prerequisites

• Node.js 18+
• Python 3.8+
• Web3 wallet with funding (~$10+ to start)

Installation

# Clone the repository
git clone https://github.com/LvcidPsyche/polymarket-arbitrage-bot.git
cd polymarket-arbitrage-bot

# Install Node.js dependencies
npm install

# Set up Python environment
python3 -m venv polymarket-env
source polymarket-env/bin/activate
pip install web3 mnemonic requests

# Configure environment variables
cp .env.example .env
# Edit .env with your credentials

Configuration

Create a .env file with:

WALLET_SEED="your twelve word seed phrase here"
KALSHI_API_KEY="your_kalshi_api_key"
KALSHI_PRIVATE_KEY="-----BEGIN RSA PRIVATE KEY-----..."
POLYGON_RPC_URL="https://polygon-rpc.com"

Usage

Basic Operations

# Check wallet balances
source polymarket-env/bin/activate
python3 check_all_balances.py

# Run single arbitrage scan
node arbitrage_detector.js

# Start continuous silent operation
./execute_silent_trading.sh

# Check system status
node check_silent_status.js

Advanced Features

# Start real-time web dashboard
node dashboard_server.js
# Then visit http://localhost:3000

# Advanced risk management
node advanced_risk_manager.js report    # Generate risk report
node advanced_risk_manager.js stop      # Emergency stop
node advanced_risk_manager.js resume    # Resume trading

# Machine learning insights
python3 ml_opportunity_predictor.py insights  # Generate ML insights
python3 ml_opportunity_predictor.py retrain   # Retrain model

# Market maker analysis
node market_maker_analyzer.js monitor        # Start MM monitoring
node market_maker_analyzer.js report         # Generate MM report
node market_maker_analyzer.js opportunities  # Show exploitable opportunities

📊 Performance

Target Performance:

• 548% annualized returns on successful endgame trades
• 15-25% success rate on identified opportunities
• Starting capital: $10
• Growth target: $10 → $1,000+ through compounding

Risk Management:

• Maximum 10% of balance per trade
• Automated position sizing
• Stop-loss mechanisms
• Diversified opportunity hunting

🛠 System Components

Advanced Detection Engines

• arbitrage_detector.js - Main opportunity scanner
• intensive_arbitrage_scanner.js - Deep market analysis
• efficient_arbitrage_scanner.js - Optimized resource usage
• silent_arbitrage_executor.js - Background trading system
• market_maker_analyzer.js - NEW: MM behavior analysis and exploitation
• ml_opportunity_predictor.py - NEW: Machine learning opportunity ranking

Risk & Portfolio Management

• advanced_risk_manager.js - NEW: Kelly Criterion position sizing with adaptive parameters
• check_all_balances.py - Multi-network balance checking
• check_wallet.py - Primary wallet validation
• create_test_wallet.py - Development wallet generation

Monitoring & Control

• dashboard_server.js - NEW: Real-time web dashboard with WebSocket
• autonomous_trader.js - Execution engine
• manual_arbitrage_guide.md - Manual trading procedures

Core Trading Infrastructure

• polymarket_trading_system/ - Core trading logic with strategies
• polymarket_trading_system/src/strategies/endgame_arbitrage.py - Endgame strategy implementation
• polymarket_trading_system/src/strategies/cross_platform.py - Cross-platform arbitrage
• polymarket_trading_system/src/core/risk_manager.py - Risk management core
• polymarket_trading_system/src/core/trading_engine.py - Main execution engine

🔍 Monitoring

The system provides comprehensive monitoring:

# System health check
node check_silent_status.js

# Recent activity logs  
cat arbitrage_scans/recent_activity.log

# Balance tracking
python3 check_all_balances.py

🎯 Strategy Details

Endgame Arbitrage

Target markets with 95-99% probability near resolution:

• Sports events with clear outcomes
• Economic announcements
• Political events with deadlines
• Time-sensitive predictions

Cross-Platform Opportunities

Exploit price differences between:

• Polymarket vs Kalshi
• Different resolution timeframes
• Market maker inefficiencies

🔐 Security

• Environment variables for sensitive data
• Local credential storage with secure access
• Transaction signing with private key management
• Rate limiting and API compliance
• Fail-safe mechanisms to prevent large losses

📈 Scaling Strategy

1. Phase 1: Manual validation with $10-50
2. Phase 2: Semi-automated with $50-500
3. Phase 3: Fully autonomous with $500+
4. Phase 4: Multi-strategy deployment

🤝 Contributing

This is an active development project. Key areas for improvement:

• Additional arbitrage strategies
• Enhanced risk management
• UI/dashboard development
• Performance optimization
• Strategy backtesting

⚠️ Disclaimer

This software is for educational and research purposes. Trading involves risk of loss. Always test with small amounts and understand the risks involved. Past performance does not guarantee future results.

📞 Support

For questions or issues:

• Create a GitHub issue
• Review the documentation in /docs
• Check the troubleshooting guide

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Built by OpenClawdad 🦀 | Autonomous AI Trading System