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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:

Donation QR Code

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

Small-Boats 🚀

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:

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  • 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

LSTM

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

LSTM

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
  1. Install Required Packages:
pip install numpy pandas tensorflow scikit-learn ta ccxt matplotlib
  1. Configure Exchange: Update main.py with your Bybit API credentials:
API_KEY = "your_api_key"    # From Bybit dashboard
API_SECRET = "your_api_secret"
  1. 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
  1. 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
  1. Monitor the Logs:
  • Check trading_bot_derivatives.log for detailed operation logs
  • Review trade history in trades_log.json
  1. 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