Lecture Notes: Deep Seek AI and Automated Trading

Introduction to Deep Seek AI

• Deep Seek AI is described as cheap, good, and fast.
• Claims to be faster and 96% cheaper than OpenAI, with comparable or better quality.
• Deep Seek AI originated as a side project from a Quant firm in China.
• The AI achieves the "cheap, good, fast" meme, which was previously considered impossible.

Application in Trading

• Deep Seek AI is applied to automated trading using an RBI system.
• RBI System: Research, Backtest, Implement.
  • Research: Find good ideas and alpha generation techniques via Google Scholar, YouTube, podcasts, etc.
  • Backtest: Use open-high-low-close volume data to validate past performance.
  • Implement: If backtested successfully, it might work in the future.

AI Agent Flow in Trading

• Developed AI agents to automate trading processes using Deep Seek and OpenAI.
• Agent 1: Research agent watches videos, reads academic papers, and extracts trading strategies.
• Backtest Agent: Codes the backtest based on research findings.
• Challenges: Debugging the backtest code was a significant issue.

Cost Efficiency

• The AI model reduces costs associated with running AI agents by 96% compared to other models.
• Implementing these agents was previously expensive but now more accessible with Deep Seek.

Development and Deployment

• The lecture emphasizes continuous development and updating of AI models.
• Discussion about building open-source models and running them locally or on servers.
• Challenges of integrating and debugging code are addressed using AI.

Technical and Development Insights

• Importance of debugging and coding environment for testing AI models.
• Emphasis on collaboration and sharing of code via GitHub for community development.
• Various AI agents developed include chart analysis, coin analysis, and trading strategy agents.

Roadmap and Resources

• Resources such as GitHub, readme files, and other documents are available for learning and collaboration.
• Emphasis on open-source sharing and continuous improvement of code.

Conclusion

• Encouragement to explore automated trading using emerging AI technologies like Deep Seek.
• Continuous improvement and competition in AI development are crucial.
• The lecture concludes with motivational quotes and affirmations to inspire ongoing development and exploration in AI and trading.