The Applicability of Self-Play Algorithms to Trading and Forecasting Financial Markets

Area of Science:

• Artificial Intelligence
• Computational Finance
• Algorithmic Trading

Background:

• Self-Play AI is typically used in zero-sum games like chess or Go.
• Financial markets present unique challenges: a diffuse adversary, profit maximization objective, and noisy data.
• Existing academic research on Self-Play has largely excluded financial applications, particularly FX, commodities, and bond markets.

Purpose of the Study:

• To investigate the applicability of the AI Self-Play methodology to financial markets.
• To address the unique challenges of applying Self-Play in financial trading environments.
• To explore the potential of Self-Play for economic forecasting.

Main Methods:

• Conceptual analysis of Self-Play in game theory versus financial markets.
• Identification of challenges including market complexity, objective functions, and data quality.
• Review of existing academic literature on Self-Play applications.

Main Results:

• Self-Play has not been extensively applied to finance, with prior research limited to stock markets.
• Significant challenges exist in adapting Self-Play for financial markets due to their complexity and data characteristics.
• The study identifies substantial potential for Self-Play in financial and economic forecasting.

Conclusions:

• Applying Self-Play to financial markets requires adapting strategies beyond traditional game-playing.
• Overcoming data and market structure challenges is crucial for successful implementation.
• Self-Play offers promising avenues for advancing AI in finance and economic prediction.