hyperliquid-monitor 0.1.2.1

A Hyperliquid trade monitor package

Hyperliquid Monitor

A Python package for monitoring trades and orders on Hyperliquid DEX in real-time. This package allows you to track specific addresses and receive notifications when trades are executed or orders are placed/cancelled.

Features

• Real-time monitoring of trades and orders
• Support for multiple addresses
• Optional SQLite database storage
• Callback system for custom notifications
• Clean shutdown handling
• Proper trade type definitions using dataclasses

Installation

Using Poetry (recommended)

poetry add hyperliquid-monitor

Using pip

pip install hyperliquid-monitor

Quick Start

Simple Console Notification

Here's a basic example that monitors an address and prints trades to the console:

from hyperliquid_monitor import HyperliquidMonitor
from hyperliquid_monitor.types import Trade
from datetime import datetime

def print_trade(trade: Trade):
    """Print trade information to console with colors"""
    timestamp = trade.timestamp.strftime('%Y-%m-%d %H:%M:%S')
    
    # Color codes
    GREEN = '\033[92m'
    RED = '\033[91m'
    BLUE = '\033[94m'
    RESET = '\033[0m'
    
    # Choose color based on trade type and side
    color = GREEN if trade.side == "BUY" else RED
    
    print(f"\n{BLUE}[{timestamp}]{RESET} New {trade.trade_type}:")
    print(f"Address: {trade.address}")
    print(f"Coin: {trade.coin}")
    print(f"{color}Side: {trade.side}{RESET}")
    print(f"Size: {trade.size}")
    print(f"Price: {trade.price}")
    
    if trade.trade_type == "FILL":
        print(f"Direction: {trade.direction}")
        if trade.closed_pnl:
            pnl_color = GREEN if trade.closed_pnl > 0 else RED
            print(f"PnL: {pnl_color}{trade.closed_pnl:.2f}{RESET}")
        print(f"Hash: {trade.tx_hash}")

def main():
    # List of addresses to monitor
    addresses = [
        "0x010461C14e146ac35Fe42271BDC1134EE31C703a"  # Example address
    ]

    # Create monitor with console notifications and optional database
    monitor = HyperliquidMonitor(
        addresses=addresses,
        db_path="trades.db",  # Optional: remove to disable database
        callback=print_trade
    )

    try:
        print("Starting monitor... Press Ctrl+C to exit")
        monitor.start()
    except KeyboardInterrupt:
        monitor.stop()

if __name__ == "__main__":
    main()

Trade Object Structure

The Trade object contains the following information:

@dataclass
class Trade:
    timestamp: datetime      # When the trade occurred
    address: str            # The address that made the trade
    coin: str              # The traded coin/token
    side: Literal["BUY", "SELL"]  # Trade side
    size: float            # Trade size
    price: float           # Trade price
    trade_type: Literal["FILL", "ORDER_PLACED", "ORDER_CANCELLED"]
    direction: Optional[str] = None  # e.g., "Open Long", "Close Short"
    tx_hash: Optional[str] = None    # Transaction hash for fills
    fee: Optional[float] = None      # Trading fee
    fee_token: Optional[str] = None  # Fee token (e.g., "USDC")
    start_position: Optional[float] = None  # Position size before trade
    closed_pnl: Optional[float] = None     # Realized PnL for closing trades
    order_id: Optional[int] = None         # Order ID for orders

Database Storage

If you provide a db_path, trades will be stored in an SQLite database with two tables:

Fills Table

• timestamp: When the trade occurred
• address: Trader's address
• coin: Traded asset
• side: BUY/SELL
• size: Trade size
• price: Trade price
• direction: Trade direction
• tx_hash: Transaction hash
• fee: Trading fee
• fee_token: Fee token
• start_position: Position before trade
• closed_pnl: Realized PnL

Orders Table

• timestamp: When the order was placed/cancelled
• address: Trader's address
• coin: Asset
• action: placed/cancelled
• side: BUY/SELL
• size: Order size
• price: Order price
• order_id: Unique order ID

Database Recording Modes

The monitor supports different modes of operation for recording trades:

1. Full Monitoring with Notifications

# Records to database and sends notifications via callback
monitor = HyperliquidMonitor(
    addresses=addresses,
    db_path="trades.db",
    callback=print_trade
)

2. Silent Database Recording

# Only records to database, no notifications
monitor = HyperliquidMonitor(
    addresses=addresses,
    db_path="trades.db",
    silent=True  # Suppresses all notifications and console output
)

3. Notification-Only Mode

# Only sends notifications, no database recording
monitor = HyperliquidMonitor(
    addresses=addresses,
    callback=print_trade
)

The silent mode is particularly useful for:

• Background monitoring and data collection
• Reducing system resource usage
• Running multiple monitors concurrently
• Long-term trade data accumulation
• Server-side deployments where notifications aren't needed

Note: Silent mode requires a database path to be specified since it's meant for data recording.

Development

Setting up the Development Environment

1. Clone the repository:

git clone https://github.com/your-username/hyperliquid-monitor.git
cd hyperliquid-monitor

2. Install poetry if you haven't already:

curl -sSL https://install.python-poetry.org | python3 -

3. Install dependencies:

poetry install

Running Tests

The package includes a comprehensive test suite using pytest. To run the tests:

# Run all tests
poetry run pytest

# Run with coverage report
poetry run pytest --cov

# Run specific test file
poetry run pytest tests/test_monitor.py

# Run tests with output
poetry run pytest -v

Test Structure

Tests are organized in the following structure:

tests/
├── __init__.py
├── conftest.py          # Shared fixtures
├── test_monitor.py      # Monitor tests
├── test_database.py     # Database tests
└── test_types.py        # Type validation tests

Key test areas:

• Monitor functionality (subscriptions, event handling)
• Database operations (storage, retrieval)
• Type validation (trade object validation)
• Event processing (fills, orders)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. Make sure to:

1. Add tests for any new functionality
2. Update documentation as needed
3. Follow the existing code style
4. Run the test suite before submitting

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Built on top of the official Hyperliquid Python SDK