py_polymarket_hft_mm

Professional high frequency trading (HFT) market making system designed for Bitcoin 15-minute binary markets on Polymarket. This advanced trading infrastructure combines real-time signal detection, automated order execution, and intelligent hedging strategies to achieve consistent profitability in fast-moving prediction markets.

System Performance:

Win Rate: 99% profitable trades
Market Focus: Bitcoin 15-min prediction markets
Strategy: HFT market making with intelligent hedging
Architecture: Python async with CPU optimization
Uptime: Continuous 24/7 autonomous operation

System Architecture

Core Components

┌───────────────────────────────────────────────────┐
│                TRADING ENGINE                     │
├───────────────────────────────────────────────────┤
│  OrderBook       │  Signal Gen     │  Risk Mgmt   │
│  ├─ WebSocket    │  ├─ Micro-price │  ├─ Position │
│  ├─ Real-time    │  ├─ BPS Signal  │  ├─ Limits   │
│  └─ Updates      │  └─ Filtering   │  └─ Exposure │
├───────────────────────────────────────────────────┤
│             EXECUTION LAYER                       │
│  Market Maker    │  Hedging Bot    │  Monitoring  │
│  ├─ Order Mgmt   │  ├─ Fill Track  │  ├─ Logging  │
│  ├─ Spread Calc  │  ├─ Auto Hedge  │  ├─ Alerts   │
│  └─ PnL Track    │  └─ State Mgmt  │  └─ Health   │
└───────────────────────────────────────────────────┘

Performance Engineering

1. Garbage Collection Management

gc.disable()  # Eliminates GC pauses during trading

Why: Python's garbage collector can cause trading delays. We disable it during active trading and manually trigger cleanup during quiet periods.

2. CPU Optimization

def set_cpu_affinity():
    affinity_cores = [cpu_count - 2, cpu_count - 1]  # Use dedicated cores
    process.cpu_affinity(affinity_cores)
    process.nice(psutil.HIGH_PRIORITY_CLASS)  # High priority

Benefits:

Dedicated CPU cores for trading operations
High process priority for consistent performance
Reduced interference from other system processes

3. Async Architecture

# WebSocket on separate thread
self.thread = threading.Thread(target=self._connect, daemon=True)
# Order processing with async
await asyncio.create_task(send_hedge(...))

Advantages:

Non-blocking network operations
Concurrent order processing
Thread-safe state management

4. Efficient Data Processing

def _on_message(self, ws, message):
    data = json.loads(message)  # Fast JSON parsing
    if event_type == "price_change":
        self._update_orderbook_incremental(data)  # Efficient updates

Trading Strategies

Bitcoin Market Making

15-Minute Markets: Focus on Bitcoin price prediction markets with 15-minute expiry
Micro-price Signals: Volume-weighted bid-ask analysis for entry timing
Risk Management: Position limits and automated stop-losses

Smart Hedging System (`sep_hedge.py`)

# Real-time fill monitoring with duplicate prevention
async def handle_message(message):
    if anchor_id in state.hedged_anchors:  # Prevents double-hedging
        return

    # Safe state management
    async with state.lock:
        state.processing_anchors.add(anchor_id)

Features:

Instant fill detection via WebSocket
Automatic hedge placement on every trade
State tracking to prevent duplicate orders
Retry logic for failed orders

System Optimizations

Memory Management

Connection pooling for HTTP requests
Efficient orderbook updates using bisect operations
Thread-safe data structures for concurrent access

Network Optimization

WebSocket connections with automatic reconnection
Optimized timeouts for Polymarket API calls
Keep-alive connections to reduce overhead

Signal Processing

Real-time price calculations for market analysis
BPS monitoring with configurable thresholds
State persistence to avoid duplicate processing

Getting Started

System Requirements

CPU: 4 cores recommended
RAM: 2GB+ for orderbook processing
Network: Stable internet connection
OS: Linux, Windows, or macOS

Installation

git clone https://github.com/nawaz0x1/py_polymarket_hft_mm
cd py_polymarket_hft_mm
pip install -r requirements.txt

# Setup environment
cp .env.sample .env
# Add your Polymarket credentials

# Run the system
sudo env "PATH=$PATH" python main.py    # Main trading bot
python sep_hedge.py                     # Hedging companion

Configuration

# config.py - Key settings
TRADING_BPS_THRESHOLD = 50    # Signal sensitivity
PROFIT_MARGIN = 0.02          # Minimum profit per trade
MARKET_SESSION_SECONDS = 900  # 15-minute market periods

Support Development

Found this trading system profitable? Consider supporting further development:

USDT/USDC Donations:

Ethereum (ETH): 0x89ae2f064cf2cb06a5e66a8e9ea6b653dcb93cfa
Solana (SOL): 2V4g71bG6dJyqv4REZeZSCtiF4pQauDvRiLy8MDNjWNv

Your support helps maintain and improve the trading algorithms!

Contact

GitHub: @nawaz0x1
X (twitter): @nawaz0x1
LinkedIn: Shah Nawaz Haider

Disclaimer

This software is for educational and research purposes. Trading involves substantial risk of loss. Users are responsible for compliance with applicable financial regulations. Past performance does not guarantee future results.

README.md

Summary