How can Multi-Agents AI Systems help Reduce Biases in Trading Algorithms?

Abstract

Algorithmic trading is now the most common form of trading in financial markets, and it has been estimated that it accounts for 60-75% of the total trading volume in major markets. However, algorithmic trading is still accompanied by cognitive and algorithmic biases such as overconfidence, confirmation bias, and anchoring effects that can result in suboptimal decisions and higher levels of risk. These biases are due to the excess reliance on certain kinds of data, historical overfitting, and the absence of mechanisms to adapt to changing market environments. We propose in this paper, the use of multi-agent AI systems (MAIS) to tackle these biases through collaboration, role differentiation, and learning. In this manner, MAIS design various agents that perform specific tasks, for instance, fundamental analysts, sentiment analysts, and technical analysts to ensure that the analysis is holistic yet without concentrating on a single kind of data. Thus, debate protocols and risk management teams ensure that the generation and evaluation of trading ideas are properly structured and that overconfidence and groupthink are avoided. Furthermore, there are market observer agents and reflective agents that provide online learning of model drift and offline learning of historical performance, respectively. Our architecture framework was tested in a simulated environment in which MAIS traded against human traders and rule-based algorithms using historical market data. The results showed that there were great quantitative improvements in the Sharpe ratios and drawdowns, which show that the system is good at improving riskadjusted returns and decreasing volatility. The last section of the paper contains a conclusion and the suggestions for future research.

Keywords

• multi-agent ai systems (mais)
• confirmation bias
• disposition effect
• trading algorithms
• sharpe ratio
• natural language processing
• institutional trading