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⚠️ IMPORTANT DISCLAIMER ⚠️

This trading bot is currently in EXPERIMENTAL/BETA testing phase. By using this software:

1. Cryptocurrency Trading Risks: You acknowledge that trading cryptocurrencies involves substantial risks, including the potential loss of your invested capital.
2. Technological Limitations: The bot utilizes Artificial Intelligence and Machine Learning (AI/ML) models that are still undergoing testing and improvements. This may lead to unforeseen errors or inaccurate signals.
3. Liability for Losses: You accept full responsibility for any financial losses that may occur as a result of using this bot.
4. No Guarantee of Performance: Past performance does not guarantee future results. The cryptocurrency market is highly volatile and can change rapidly.
5. Capital Management: Trade only with funds you can afford to lose. Do not invest money that is necessary for your living expenses or other essential purposes.
6. No Financial Advice: This software is NOT financial advice. Use it at your own risk and consult with professional financial advisors before making investment decisions.

Support the Project 💖

If you've found this project helpful or profitable, consider supporting its development:

Your donations help maintain and improve the project, ensuring its continued development and enhancement.

Small-Boats 🚀

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Overview 📊

CryptoBoat is a semi-automated cryptocurrency trading system that emphasizes manual control over key trading decisions while managing multiple positions simultaneously. Unlike fully automated bots, this strategy allows you to:

• Manually control Take-Profit and Stop-Loss levels
• Open multiple strategic positions across different assets
• Benefit from risk diversification through position spreading
• Offset potential losses with gains from other positions

Technical Details 🔧

AI Models Used

1. LSTM (Long Short-Term Memory)
   • Specialized neural network for time series prediction
   • Capable of learning long-term dependencies
   • Optimized for cryptocurrency price movement patterns
   • Uses multiple technical indicators for enhanced accuracy

2. Random Forest
   • Ensemble learning algorithm
   • Combines multiple decision trees
   • Reduces overfitting through aggregation
   • Provides robust market trend predictions

Technical Indicators

• RSI (Relative Strength Index)
• EMA (Exponential Moving Average)
• MACD (Moving Average Convergence Divergence)
• Bollinger Bands
• Ichimoku Cloud
• VWAP (Volume Weighted Average Price)
• ATR (Average True Range)

Performance Features 🎯

• Real-time market data processing
• Multi-timeframe analysis
• Advanced risk management system
• Position size optimization
• Automated entry/exit signals
• Portfolio rebalancing
• Custom indicator combinations

Technical Architecture 🔧

System Components

1. Neural Networks

   • LSTM Network: 2-layer bidirectional architecture
   • Input shape: (60, 18) - 60 timeframes, 18 features
   • Hidden layers: 100 units each with dropout (0.3)
   • Output: Binary classification (buy/sell signal)

2. Random Forest Classifier

   • Ensemble of 100 decision trees
   • Feature flattening: 1080 dimensions (60 timeframes × 18 features)
   • Class balancing with SMOTE
   • Parallel prediction processing

3. Technical Indicators

   • Price-based: EMA, Bollinger Bands, Ichimoku
   • Momentum: RSI, MACD, Stochastic
   • Volume: VWAP
   • Volatility: ATR

Installation & Setup 🛠️

Prerequisites

• Python 3.8+
• GPU recommended for faster model training
• Minimum 4GB RAM
• Bybit account with API access

Step-by-Step Installation

1. Initialize Project:

git clone https://github.com/Solrikk/CryptoBoat.git
cd CryptoBoat

2. Install Required Packages:

pip install numpy pandas tensorflow scikit-learn ta ccxt matplotlib

3. Configure Exchange: Update main.py with your Bybit API credentials:

API_KEY = "your_api_key"    # From Bybit dashboard
API_SECRET = "your_api_secret"

4. Configure Risk Parameters: In main.py, adjust trading parameters:

risk_percentage = 0.3  # Risk per trade (0.3%)
TRADE_COOLDOWN = 60   # Seconds between trades

Running the Bot

1. Initial Launch:

python main.py

2. Monitor Operations:

• Check trading_bot_derivatives.log for real-time status
• Review trades_log.json for trade history

System Architecture

Input Data → Feature Engineering → Model Prediction → Trading Logic
    ↓              ↓                    ↓               ↓
OHLCV Data → Technical Indicators → LSTM + RF → Position Management

Performance Monitoring

• Log files track all operations
• Real-time balance updates
• Trade execution confirmations
• Error handling and reporting

Risk Management

• Position sizing based on account balance
• Maximum risk per trade: 0.3%
• Trade cooldown period: 60 seconds
• Automatic error recovery
• Multiple validation layers

Error Handling

• Network disconnect protection
• API error recovery
• Invalid data detection
• Balance verification
• Order validation

Usage Guide 📚

1. Start the Bot:

python main.py

2. Monitor the Logs:

• Check trading_bot_derivatives.log for detailed operation logs
• Review trade history in trades_log.json

3. Configure Risk Parameters:

• Adjust position sizes in calculate_position_size()
• Modify risk percentage (default: 0.3%)
• Set custom stop-loss levels

⚠️ Remember: Past performance does not guarantee future results. Always trade responsibly and within your risk tolerance.

Community & Support 🤝

• Join our community discussions
• Share your trading strategies
• Report issues and suggest improvements
• Help others get started