Effective trading with given pattern-based multi-predictors of stock price needs an intelligent asset allocation strategy. In this paper, we study a method of dynamic asset allocation, called the meta policy, which decides how much the proportion of asset should be allocated to each recommendation for trade. The meta policy makes a decision considering both the recommending information of multi-predictors and the current ratio of stock funds over the total asset. We adopt evolutionary computation to optimize the meta policy. The experimental results on the Korean stock market show that the trading system with the proposed meta policy outperforms other systems with fixed asset allocation methods.

This paper describes a stock trading system based on reinforcement learning, regarding the process of stock price changes as Markov decision process (MDP). The system adopts two popular reinforcement learning algorithms, temporal-difference (TD) and Q, for selecting stocks and optimizing trading parameters, respectively. Input features of the system are devised using technical analysis and value functions are approximated by feedforward neural networks. Multiple cooperative agents are used for Q-learning to efficiently integrate global trend prediction with local trading strategy. Agents communicate with others sharing training episodes and learned policies, while keeping the overall scheme of conventional Q-learning. Experimental results on the Korean stock market show that our trading system outperforms the market average and makes appreciable profits. Furthermore, we can find that our system is superior to a system trained by supervised learning in view of risk management.