Artificial Markets and Trading Agent Strategies

Artificial Multi-Agent Stock Markets: Simple Strategies, Complex Outcomes

Artificial Financial Markets with Adaptive Trading Agents

Motivation: In the last decade there has been a surge of interest within the finance community in describing equity markets through computational agent models [7]. At the same time, financial markets are an important application area within artificial intelligence for the fields of agent-based modeling and machine learning, since agent objectives and interactions tend to be more clearly defined...

Trading Risk in Mobile-Agent Computational Markets

Mobile-agent systems allow user programs to autonomously relocate from one host site to another. This autonomy provides a powerful, flexible architecture on which to build distributed applications. The asynchronous, decentralized nature of mobile-agent systems makes them flexible, but also hinders their deployment. We argue that a marketbased approach where agents buy computational resources fr...

Classification of Trading Strategies in Adaptive Markets

In the CAT Tournament, agents facilitate transactions between buyers and sellers with the intention of maximizing profit from commission and other fees. The agent must find a well-balanced strategy that allows it to sign buyers and sellers to trade in its market while also maintaining the buyers and sellers that are currently subscribed to it. One approach is to classify the traders that intera...

Agent-based evolutionary optimisation of trading strategies

The backtesting and optimisation of trading strategies has emerged as an interesting research and experimental problem in both finance and Information Technology (IT) fields. However, it is a non-trivial task to effectively and efficiently optimise trading strategies, not to mention the optimisation in the real-world situations. This paper discusses the application of evolutionary technologies ...