Financial Trading Algorithm

A production-grade algorithmic trading system that uses large language models for market analysis and trading decisions.

Features

• Multi-model market analysis using OpenAI, Perplexity, and Ollama
• Secure API key management with encryption
• Connection pooling for optimal API performance
• Comprehensive technical and fundamental analysis
• Sentiment analysis and market psychology insights
• Portfolio optimization and risk management
• Real-time market data processing
• Modular and extensible architecture

Prerequisites

• Python 3.10 or higher
• Ollama running locally (for local LLM support)
• API keys for OpenAI and Perplexity
• Sufficient system memory (8GB+ recommended)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/Financial-Trading-Algorithm.git
cd Financial-Trading-Algorithm

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables:

# Create .env file
touch .env

# Add your API keys
echo "API_MASTER_KEY=your_master_key_here" >> .env
echo "OPENAI_API_KEY=your_openai_key_here" >> .env
echo "PERPLEXITY_API_KEY=your_perplexity_key_here" >> .env

Configuration

The system uses a configuration file (config.ini) for various settings. Key configurations:

[API]
openai_model = gpt-4o
perplexity_model = llama-3.1-sonar-large-128k-online
ollama_model = llama3.2:1b

[Trading]
max_position_size = 1000
risk_tolerance = 0.02
min_portfolio_size = 30
max_portfolio_size = 120

Usage

1. Start the trading system:

python main.py

2. Monitor the dashboard:

python -m financial_trading_algorithm.utils.dashboard

3. View trading recommendations:

python -m financial_trading_algorithm.trading.recommendations

Project Structure

financial_trading_algorithm/
  ├── api/                 # API client implementations
  │   ├── openai_client.py
  │   ├── perplexity_client.py
  │   └── ollama_client.py
  ├── data/               # Data handling and storage
  │   ├── stock_data.py
  │   └── parquet_handler.py
  ├── analysis/           # Analysis modules
  │   ├── technical.py
  │   ├── sentiment.py
  │   └── fundamentals.py
  ├── trading/            # Trading logic
  │   ├── agent.py
  │   ├── portfolio.py
  │   └── executor.py
  ├── utils/             # Utility functions
  │   ├── config_manager.py
  │   └── api_key_manager.py
  └── main.py            # Entry point

Security

• API keys are encrypted using Fernet encryption
• Master key required for API key decryption
• Connection pooling prevents resource exhaustion
• Rate limiting and retry mechanisms implemented
• Regular security audits recommended

Performance Optimization

• Connection pooling for API clients
• Caching for expensive calculations
• Batch processing for API requests
• Asynchronous operations where possible
• Efficient data storage using Parquet format

Contributing

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

Testing

Run the test suite:

pytest tests/

Run specific test categories:

pytest tests/test_api/
pytest tests/test_trading/
pytest tests/test_analysis/

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• OpenAI for GPT-4o API
• Perplexity for advanced language models
• Ollama for local LLM support
• Contributors and maintainers

Support

For support, please open an issue in the GitHub repository or contact the maintainers.