A sophisticated alpha factor generation system that uses Ollama with financial language models to generate, test, and submit alpha factors to WorldQuant Brain. This system replaces the previous Kimi interface with a local Ollama-based solution for better performance and control.

• Local LLM Integration: Uses Ollama with llama3.2:3b or llama2:7b models
• GPU Acceleration: Full NVIDIA GPU support for faster inference
• Web Dashboard: Real-time monitoring and control interface
• Automated Orchestration: Continuous alpha generation, mining, and submission
• WorldQuant Brain Integration: Direct API integration for testing and submission
• Docker Support: Easy deployment with Docker and Docker Compose
• Daily Rate Limiting: Ensures compliance with WorldQuant submission limits

• Docker & Docker Compose: For containerized deployment
• NVIDIA GPU (optional): For GPU acceleration
• NVIDIA Container Toolkit: For GPU support in Docker
• WorldQuant Brain Account: For alpha testing and submission

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Web Dashboard │    │ Alpha Generator │    │  WorldQuant API │
│   (Flask)       │◄──►│   (Ollama)      │◄──►│   (External)    │
│   Port 5000     │    │   Port 11434    │    │                 │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         │              ┌─────────────────┐              │
         └──────────────►│ Alpha Orchestrator │◄─────────────┘
                        │   (Python)      │
                        └─────────────────┘
                                │
                                ▼
                        ┌─────────────────┐
                        │   Results &     │
                        │   Logs Storage  │
                        └─────────────────┘

Create credential.txt with your WorldQuant Brain credentials:

["your.email@worldquant.com", "your_password"]

# Start the complete system with GPU acceleration
docker-compose -f docker-compose.gpu.yml up -d

# Or use the convenience script
start_gpu.bat

Open your browser and navigate to:

• Main Dashboard: http://localhost:5000
• Ollama WebUI: http://localhost:3000
• Ollama API: http://localhost:11434

The web dashboard provides real-time monitoring and control:

• GPU Status: Memory usage, utilization, temperature
• Ollama Status: Model loading, API connectivity
• Orchestrator Status: Generation activity, mining schedule
• WorldQuant Status: API connectivity, authentication
• Statistics: Generated alphas, success rates, 24h metrics

• Generate Alpha: Trigger single alpha generation
• Trigger Mining: Run alpha expression mining
• Trigger Submission: Submit successful alphas
• Refresh Status: Update all metrics

• Alpha Generator Logs: Filtered logs showing alpha generation activity
• System Logs: Complete system activity
• Recent Activity: Timeline of recent events

Variable	Default	Description
OLLAMA_URL	http://localhost:11434	Ollama API endpoint
MODEL_NAME	llama3.2:3b	LLM model to use
MINING_INTERVAL	6	Hours between mining runs
BATCH_SIZE	3	Alphas per generation batch

• naive-ollma: Main alpha generation container with GPU support
• ollama-webui: Web interface for Ollama management
• alpha-dashboard: Flask web dashboard for monitoring

• naive-ollma: Main alpha generation container (CPU only)
• ollama-webui: Web interface for Ollama management

naive-ollama/
├── alpha_generator_ollama.py      # Main alpha generation script
├── alpha_orchestrator.py          # Orchestration and scheduling
├── alpha_expression_miner.py      # Alpha expression mining
├── successful_alpha_submitter.py  # Alpha submission to WorldQuant
├── web_dashboard.py               # Flask web dashboard
├── templates/
│   └── dashboard.html             # Dashboard HTML template
├── results/                       # Generated alpha results
├── logs/                          # System logs
├── Dockerfile                     # Docker image definition
├── docker-compose.gpu.yml         # GPU-enabled deployment
├── docker-compose.yml             # CPU-only deployment
├── requirements.txt               # Python dependencies
├── credential.txt                 # WorldQuant credentials
├── start_gpu.bat                  # Windows GPU startup script
├── start_dashboard.bat            # Windows dashboard startup script
└── README_Docker.md               # Detailed Docker documentation

• Continuous Mode: Generates alphas every 6 hours
• Batch Processing: Generates 3 alphas per batch
• Ollama Integration: Uses local LLM for alpha idea generation
• WorldQuant Testing: Tests each alpha immediately

• Expression Mining: Analyzes promising alphas for variations
• Pattern Recognition: Identifies successful alpha patterns
• Optimization: Suggests improvements to existing alphas

• Daily Limit: Submits only once per day
• Success Filtering: Only submits alphas with good performance
• Rate Limiting: Respects WorldQuant API limits

• Alpha Generation Rate: Alphas generated per hour
• Success Rate: Percentage of successful alphas
• GPU Utilization: Memory and compute usage
• API Response Times: WorldQuant API performance

• Alpha Generator Logs: Filtered for alpha-related activity
• System Logs: Complete system activity
• Error Tracking: Failed generations and API calls

# Check NVIDIA drivers
nvidia-smi

# Verify Docker GPU support
docker run --rm --gpus all nvidia/cuda:11.6.2-base-ubuntu20.04 nvidia-smi

# Check Ollama service
docker logs naive-ollma-gpu

# Test Ollama API
curl http://localhost:11434/api/tags

# Verify credentials format
cat credential.txt

# Check authentication in logs
docker logs naive-ollma-gpu | grep "Authentication"

• Reduce BATCH_SIZE in environment variables
• Monitor GPU memory usage in dashboard
• Consider using smaller model (llama2:7b)

• Increase BATCH_SIZE for faster processing
• Use GPU acceleration for better performance
• Monitor Ollama response times

• Local Processing: All LLM inference happens locally
• Credential Protection: Credentials stored in mounted volume
• Network Isolation: Docker network isolation
• API Rate Limiting: Respects external API limits

• INFO: Normal operation messages
• WARNING: Non-critical issues
• ERROR: Critical failures
• DEBUG: Detailed debugging information

• Container Logs: docker logs naive-ollma-gpu
• Application Logs: ./logs/ directory
• Web Dashboard: Real-time log display

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test with Docker setup
5. Submit a pull request

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

• WorldQuant Brain: For providing the alpha testing platform
• Ollama: For the local LLM serving framework
• NVIDIA: For GPU acceleration support
• Flask: For the web dashboard framework

For issues and questions:

1. Check the troubleshooting section
2. Review Docker logs
3. Check the web dashboard for real-time status
4. Open an issue on GitHub

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Happy Alpha Generation! 🚀