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http://dx.doi.org/10.3745/KIPSTB.2004.11B.2.207

Optimization of Stock Trading System based on Multi-Agent Q-Learning Framework  

Kim, Yu-Seop (한림대학교 정보통신공학부)
Lee, Jae-Won (성신여자대학교 컴퓨터정보공학부)
Lee, Jong-Woo (㈜아이닉스소프트)
Publication Information
The KIPS Transactions:PartB / v.11B, no.2, 2004 , pp. 207-212 More about this Journal
Abstract
This paper presents a reinforcement learning framework for stock trading systems. Trading system parameters are optimized by Q-learning algorithm and neural networks are adopted for value approximation. In this framework, cooperative multiple agents are used to efficiently integrate global trend prediction and local trading strategy for obtaining better trading performance. Agents Communicate With Others Sharing training episodes and learned policies, while keeping the overall scheme of conventional Q-learning. Experimental results on KOSPI 200 show that a trading system based on the proposed framework outperforms the market average and makes appreciable profits. Furthermore, in view of risk management, the system is superior to a system trained by supervised learning.
Keywords
Q-learning; Stock Trading; Multi Agent; Buy Signal; Buy Order; Sell Signal; Sell Order;
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