Using AI for Trading

Introduction

• The lecture discusses practical applications of AI in trading.
• The speaker has spent over 3,000 hours developing effective trading strategies using AI.

The Misconception about AI in Trading

• AI is often misunderstood as a tool for predicting prices.
• Predicting prices can lead to changes in those prices due to market reactions.
• Instead, the focus should be on understanding market regimes.

Two Effective Approaches to AI in Trading

1. Using Large Language Models (LLMs)

   • Platforms like Claude and ChatGPT can help convert ideas into code for trading systems.
   • LLMs assist in researching, backtesting, and automating trading strategies, which were previously time-consuming.

2. Hidden Markov Models (HMM)

   • Jim Simons, a renowned algorithmic trader, used data-driven strategies and favored HMMs.
   • HMMs predict hidden states in market data, allowing traders to understand market regimes (e.g., bullish, bearish, sideways).
   • The hidden states can be classified into different categories, such as consolidation or high volatility.

Key Concepts in HMM

• States and Regimes: Different states represent market conditions.
• Model Training: The model learns from historical data to predict future regimes.
• Backtesting: Strategies are tested using past data to validate their effectiveness.

Backtesting and Performance Metrics

• The speaker outlines the importance of backtesting different strategies using AI models.
• Metrics to consider include:
  • Log likelihood (higher is better)
  • Bayesian Information Criterion (BIC) (lower is better)
  • State prediction accuracy (higher is better)
• The balance between model complexity and interpretability is crucial.

Results of Various States Models

• A comparison of models with different numbers of states:
  • 2 states: Higher accuracy, but oversimplified understanding of the market.
  • 7 states: Good balance between complexity and performance.
  • 24 states: More detailed predictions but complex to interpret.
  • 10 states: Well-performing in terms of fit and predictability.
• The results suggest that more states often mean better performance in capturing market dynamics.

Practical Applications

• Understanding how to apply these models for actual trading strategies can help in decision-making.
• Developing strategies that adapt to different market conditions is essential for successful trading.

Conclusion

• The speaker emphasizes the importance of using AI responsibly and effectively in trading.
• Continuous learning and adaptation to market changes are key to mastering AI in trading.