Hyperliquid Algo Trading Bot

An advanced algorithmic trading bot for the Hyperliquid DEX, featuring a Bollinger Bands + RSI + ADX strategy, comprehensive backtesting framework, and machine learning optimization capabilities. This bot is designed for algorithmic trading on perpetual futures contracts, with a focus on automated strategy execution and optimization.

Features

• Multiple Trading Strategies:

  • Bollinger Bands + RSI + ADX strategy (BBRSI)
  • ML-enhanced strategies with optimized parameters
  • Customizable entry/exit conditions

• Advanced Backtesting System:

  • Historical data analysis with realistic trade simulation
  • Performance metrics and statistics
  • Visualization of equity curves and trade performance
  • Multi-symbol testing capabilities
  • Risk management analysis

• Machine Learning Optimization:

  • Automated parameter optimization using ML models
  • Feature importance analysis
  • Support for different ML models (Random Forest, XGBoost, Neural Networks)
  • Optimized strategy parameter generation

• Risk Management:

  • Configurable position sizing
  • Take profit mechanisms
  • Liquidation prevention
  • Performance analysis

• Visualization Tools:

  • Equity curve visualization
  • Trade performance charts
  • Drawdown analysis
  • Interactive HTML reports

Prerequisites

• Node.js (v14 or higher)
• npm (Node Package Manager)
• Python 3.x (for ML optimization features)
• Python packages: numpy, pandas, scikit-learn, xgboost, shap, matplotlib

Installation

1. Clone the repository:

   git clone <repository-url>
   cd hyperliquidalgobot

2. Install Node.js dependencies:

   npm install

3. Install Python dependencies (for ML features):

   pip install numpy pandas scikit-learn xgboost shap matplotlib

4. Create a .env file in the root directory:

   PRIVATE_KEY=your_private_key_here

Configuration

The bot's behavior can be customized through the configuration files in the config/ directory:

• default.json: Default trading settings
• backtest.json: Specific settings for backtesting

Key Configuration Parameters

Trading Parameters:

• market: Trading pair (default: BTC-PERP)
• positionSize: Position size as a decimal (e.g., 0.1 for 10%)
• leverage: Trading leverage
• timeframe: Candlestick interval
• profitTarget: Take profit percentage

Indicator Parameters:

• RSI settings: period, overbought, oversold levels
• Bollinger Bands settings: period, standard deviation
• ADX settings: period, threshold

Usage

Live Trading

Start the trading bot (not fully implemented yet):

npm start

Backtesting

Run a backtest with default settings:

npm run backtest

Run a backtest with specific configuration:

node src/backtesting/run.js --config backtest

ML-Enhanced Backtesting

Run a backtest with ML-optimized parameters:

node src/backtesting/run.js --config backtest --use-ml

Specify a particular ML model:

node src/backtesting/run.js --config backtest --use-ml --ml-model BTC-PERP_15m_randomforest

Visualization

Visualize backtest results:

node src/backtesting/visualize.js

ML Optimization

Generate ML-optimized parameters:

node src/backtesting/ml_optimize.js --market BTC-PERP --timeframe 15m

Trading Strategies

BBRSI Strategy

A strategy that combines Bollinger Bands, RSI, and ADX indicators:

• Long Entry Conditions:

  • Price crosses below the lower Bollinger Band
  • RSI is below the oversold level
  • ADX is above the threshold

• Short Entry Conditions:

  • Price crosses above the upper Bollinger Band
  • RSI is above the overbought level
  • ADX is above the threshold

• Exit Conditions:

  • Take profit at the configured target
  • Cross of price under/over middle Bollinger Band
  • RSI extreme levels (>80 for longs, <20 for shorts)

ML-Enhanced Strategy

Extends the base strategies by applying machine learning optimized parameters:

• Uses the same signal generation logic as the base strategy
• Parameters are optimized using machine learning models
• Models analyze historical performance to find optimal settings
• Feature importance analysis identifies key indicators

Project Structure

├── config/                      # Configuration files
│   ├── default.json             # Default trading settings
│   └── backtest.json            # Backtesting settings
├── src/
│   ├── application/             # Main application code
│   │   └── controller.js        # Application controller
│   ├── backtesting/             # Backtesting framework
│   │   ├── Backtester.js        # Core backtesting engine
│   │   ├── RiskManager.js       # Risk management module
│   │   ├── ml_optimizer.js      # ML optimization implementation
│   │   ├── ml_optimize.js       # ML optimization CLI
│   │   ├── visualization.js     # Visualization library
│   │   ├── visualize.js         # Visualization CLI
│   │   ├── run.js               # Backtesting runner
│   │   ├── strategies/          # Strategy implementations for backtesting
│   │   │   └── MLEnhancedStrategy.js  # ML-enhanced strategy
│   │   └── data/                # Historical data for backtesting
│   ├── hyperliquid/             # Hyperliquid integration
│   │   ├── marketInfo.js        # Market information fetching
│   │   ├── trade.js             # Trading functions
│   │   └── websocket.js         # WebSocket connection
│   └── strategy/                # Trading strategies
│       ├── BBRSIStrategy.js     # Bollinger Bands + RSI + ADX strategy
│       ├── ScalpingStrategy.js  # Scalping strategy
│       └── indicators/          # Technical indicators
│           ├── ema.js           # EMA calculation
│           └── index.js         # Indicator utilities
├── .env                         # Environment variables (not in repo)
└── package.json                 # Project dependencies

Backtesting Results

The backtesting system generates several output files:

• equity_curve.json: Equity curve data
• backtest_trades.json: Detailed trade information
• trade_statistics.json: Performance statistics
• equity_curve_chart.html: Visual equity curve chart
• trade_performance_chart.html: Trade performance visualization
• drawdown_chart.html: Drawdown analysis
• backtest_summary.html: Complete backtest summary

ML Optimization Results

The ML optimization process generates:

• Optimized parameter files in src/backtesting/ml_models/
• Feature importance analysis
• Performance metrics
• HTML reports

Safety Features

• Configurable position sizing
• Take-profit mechanisms
• Risk management controls
• Comprehensive error handling

Logging

The bot maintains detailed logs in:

• Console output for important information
• Log files for debugging and detailed operations

Disclaimer

This bot is for educational purposes only. Cryptocurrency trading carries significant risks. Always test thoroughly on testnet before using real funds.

License

MIT

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request