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This blog is based on our paper: Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy, presented at ICAIF 2020: ACM International Conference on AI in Finance.
Our codes are available on Github.
AI4Finance-LLC/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020
If you want to cite our paper, the reference format is as follows:
Hongyang Yang, Xiao-Yang Liu, Shan Zhong, and Anwar Walid. 2020. Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. In ICAIF ’20: ACM International Conference on AI in Finance, Oct. 15–16, 2020, Manhattan, NY. ACM, New York, NY, USA.
Overview
One can hardly overestimate the crucial role stock trading strategies play in investment.
Profitable automated stock trading strategy is vital to investment companies and hedge funds. It is applied to optimize capital allocation and maximize investment performance, such as expected return. Return maximization can be based on the estimates of potential return and risk. However, it is challenging to design a profitable strategy in a complex and dynamic stock market.
Every player wants a winning strategy. Needless to say, a profitable strategy in such a complex and dynamic stock market is not easy to design.
Yet, we are to reveal a deep reinforcement learning scheme that automatically learns a stock trading strategy by maximizing investment return.
#reinforcement-learning #deep-reinforcement #stock-trading #machine-learning #markov-decision-process
