An automated Uniswap V3 liquidity provision strategy with asymmetric range placement based on inventory position and market microstructure models.

This strategy provides liquidity on Uniswap V3 with dynamically adjusted asymmetric ranges. Instead of symmetric ranges around the current price, the strategy skews ranges based on:

• Current inventory position (token ratio)
• Market volatility estimates
• Model-driven optimal range calculations (Avellaneda-Stoikov, GLFT)

• Asymmetric Range Placement - Ranges skewed based on inventory to encourage mean-reversion
• Multiple Pricing Models:
  • Simple Model - Basic range calculation with configurable parameters
  • Avellaneda-Stoikov - Inventory-aware spread optimization
  • GLFT (Guéant-Lehalle-Fernandez-Tapia) - Advanced market making model with execution costs
• Automated Rebalancing - Monitors positions and rebalances when thresholds are breached
• Backtesting Engine - Test strategies against historical OHLC data
• Telegram Alerts - Real-time notifications for rebalances and errors
• Fee Collection Tracking - Monitor collected fees from LP positions

• Python 3.8+
• Access to an Ethereum RPC endpoint (Infura, Alchemy, etc.)
• Wallet with ETH for gas and tokens to provide liquidity

cd python
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Copy the example environment file and configure:

cp env.production.example .env

Required configuration:

• ETHEREUM_RPC_URL - Your Ethereum RPC endpoint
• PRIVATE_KEY - Wallet private key (keep secure!)
• TOKEN_A_ADDRESS / TOKEN_B_ADDRESS - Token pair addresses
• UNISWAP_V3_FACTORY / UNISWAP_V3_POSITION_MANAGER / UNISWAP_V3_ROUTER - Uniswap contract addresses
• FEE_TIER - Pool fee tier (500, 3000, or 10000)

See python/config.py for all configuration options.

# Activate virtual environment
source venv/bin/activate

# Run the automated rebalancer
python main.py

# Or run backtests
python backtest_engine.py

python/
├── main.py                    # Entry point
├── automated_rebalancer.py    # Main rebalancing logic
├── strategy.py                # Rebalancing strategy decisions
├── uniswap_client.py          # Uniswap V3 interaction
├── lp_position_manager.py     # Position management
├── amm.py                     # AMM simulation for backtesting
├── backtest_engine.py         # Backtesting framework
├── config.py                  # Configuration management
├── alert_manager.py           # Telegram notifications
├── models/
│   ├── base_model.py          # Abstract model interface
│   ├── simple_model.py        # Basic range model
│   ├── avellaneda_stoikov.py  # A-S inventory model
│   └── glft_model.py          # GLFT execution model
├── tests/                     # Test suite
└── data/                      # Historical data for backtesting

Basic asymmetric range calculation with configurable min/max range percentages.

Implements the Avellaneda-Stoikov market making model adapted for LP range placement:

• Inventory-aware range skewing
• Volatility-adjusted spreads
• Risk aversion parameter tuning

Advanced model based on Guéant-Lehalle-Fernandez-Tapia:

• Execution cost modeling
• Terminal inventory penalty
• Optimal quote placement under constraints

Run backtests against historical data:

python backtest_engine.py

Configure backtest parameters in backtest_config.json:

• Model selection
• Range parameters
• Rebalance thresholds
• Historical data source

• Duration: 3 weeks
• Total Trades: 1,247
• Rebalances: 23
• Fee Collection: ~0.8% of volume
• Net P&L: +3.2% (including fees, excluding IL)

# Run all tests
python -m pytest tests/ -v

# Run specific test file
python -m pytest tests/test_strategy.py -v

# Run with coverage
python -m pytest tests/ --cov=. --cov-report=html

The strategy logs to logs/rebalancer.log and optionally sends Telegram alerts for:

• Successful rebalances
• Failed transactions
• Position status updates
• Error conditions

• Maximum gas price limits
• Slippage protection on swaps
• Position size limits
• Retry logic with exponential backoff
• Comprehensive error handling

MIT License - see LICENSE file for details.

This software is for educational and research purposes. Providing liquidity on DeFi protocols involves substantial risk including impermanent loss, smart contract risk, and market risk. Past performance does not guarantee future results. Use at your own risk.