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AI Research
arXiv CS.AI·Jun 9·5 min read
TT-DAC-PS: Twin-Target Deterministic Actor-Critic with Policy Smoothing for Optimal Trade Execution
TL;DR
Large stock sell programs often face challenges in optimal execution due to market impact and price fluctuations. TT-DAC-PS, or Twin-Target Deterministic Actor-Critic with Policy Smoothing, was developed to enhance execution strategies by integrating advanced techniques like twin critic targets and TD3-style policy smoothing.
✦ Why It Matters
Engineers can leverage TT-DAC-PS to enhance algorithmic trading strategies for better execution of large stock trades.
Key Takeaways
- Explore TT-DAC-PS for improved trade execution strategies.
- Utilize twin-target critics to enhance decision-making accuracy.
- Implement policy smoothing to reduce overestimation in trading.
- Test the model on your own trading data for performance evaluation.
How It Works
TT-DAC-PS leverages a deterministic actor-critic framework, where the actor proposes actions and the critic evaluates them using twin exponential-moving-average targets. This dual-target approach helps stabilize learning and reduces variance in action-value estimates.
The model also employs TD3-style policy smoothing, which adds noise to the target policy, encouraging exploration while maintaining a focus on optimal actions. Additionally, the use of Ornstein-Uhlenbeck noise with a hybrid schedule allows for adaptive exploration based on recent performance, further enhancing the model's effectiveness in dynamic trading environments.