Enhancing Q-learning for optimal asset allocation
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Risk sensitive reinforcement learning
Proceedings of the 1998 conference on Advances in neural information processing systems II
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
On the convergence of stochastic iterative dynamic programming algorithms
Neural Computation
Learning to trade via direct reinforcement
IEEE Transactions on Neural Networks
Adaptive stock trading with dynamic asset allocation using reinforcement learning
Information Sciences: an International Journal
| Hi-index | 0.00 |
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.