An autonomous trading system combining deep learning, reinforcement learning, and advanced risk management. Features LSTM networks with attention mechanisms, Deep Q-Learning (DQN), and adaptive ensemble learning.
- Deep Learning Models: LSTM with multi-head attention for temporal pattern recognition
- Reinforcement Learning: DQN agent for strategy optimization
- Ensemble Learning: Meta-learning system for adaptive model weighting
- Advanced Risk Management: Kelly Criterion, drawdown protection, position sizing
- Multi-Broker Support: Quotex integration with extensible adapter architecture
- LSTM Predictor with attention mechanisms for sequence learning
- Deep Q-Network (DQN) with dueling architecture and noisy layers
- Ensemble Manager using meta-learning for model combination
- Online Learning - models adapt to market changes in real-time
- Kelly Criterion position sizing
- Volatility-adjusted trade amounts
- Multi-layered circuit breakers
- Drawdown protection
- Daily/hourly trade limits
- Multi-broker support (Quotex integrated)
- Real-time market data processing
- Technical indicator library (20+)
- Backtesting with market replay
- Live trading with demo mode
- PySide6 GUI dashboard
- Real-time performance monitoring
- Comprehensive logging
- Model training scripts
- Performance analytics
git clone https://github.com/ItzSwapnil/NEXUS.git
cd NEXUS
# Create virtual environment
uv venv
.venv\Scripts\activate # Windows
# source .venv/bin/activate # Linux/Mac
# Install dependencies
uv pip install -e ".[ai,gui,ta]"
# Configure
copy .env.example .env # Windows
# cp .env.example .env # Linux/Mac
# Edit .env with your credentials
# Run
python run.py# Clone repository
git clone https://github.com/ItzSwapnil/NEXUS.git
cd NEXUS
# Setup environment
uv venv
.venv\Scripts\activate # Windows
# Install
uv pip install -e ".[gui,ta]"
uv pip install torch scikit-learn joblib
# Configure
copy .env.example .env
# Edit .env with your credentials
# Run
python run.pyFor more options, see QUICK_INSTALL.md.
Edit .env file:
# Broker Credentials
QUOTEX_EMAIL=your_quotex_email
QUOTEX_PASSWORD=your_password
# Trading Mode
TRADING_MODE=demo
# AI/ML Settings
ENABLE_AI_MODELS=true
# Risk Management
MAX_DAILY_LOSS=500.0
USE_KELLY_CRITERION=truepython run.py
# Select option 1 for GUIpython -m nexus.clipython -m nexus.main --backtestpython -m nexus.ai.train_models# Run all tests
pytest -v
# With coverage
pytest --cov=nexus --cov-report=html- Getting Started Guide
- Technical Overview
- Security Policy
- Contributing Guidelines
- Code of Conduct
- Changelog
Typical model performance (after training):
- LSTM Predictor: 65-70% directional accuracy
- DQN Agent: Positive expected value through learned policies
- Ensemble: 5-10% improvement over individual models
Past performance does not guarantee future results.
MIT License - see LICENSE for details.
IMPORTANT: NEXUS is for educational and research purposes only. Trading involves substantial risk of loss. Always:
- Start with demo mode
- Use proper risk management
- Never trade with money you can't afford to lose
- Comply with local regulations
The AI models are experimental and do not constitute financial advice.
I welcome contributions to NEXUS! Please see CONTRIBUTING.md for guidelines.
- Report bugs and issues
- Suggest new features
- Submit pull requests
- Improve documentation
- Share your trading strategies
If you find this project helpful, please:
- ⭐ Star the repository
- 🐛 Report issues
- 📖 Improve documentation
- 🔀 Submit pull requests
- Author: Swapnil De Sarkar
- GitHub: @ItzSwapnil
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