Deep Direct Reinforcement Learning for Financial Signal Representation and Trading

IEEE Transactions on Neural Networks and Learning Systems
Yue DengQionghai Dai

Abstract

Can we train the computer to beat experienced traders for financial assert trading? In this paper, we try to address this challenge by introducing a recurrent deep neural network (NN) for real-time financial signal representation and trading. Our model is inspired by two biological-related learning concepts of deep learning (DL) and reinforcement learning (RL). In the framework, the DL part automatically senses the dynamic market condition for informative feature learning. Then, the RL module interacts with deep representations and makes trading decisions to accumulate the ultimate rewards in an unknown environment. The learning system is implemented in a complex NN that exhibits both the deep and recurrent structures. Hence, we propose a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training. The robustness of the neural system is verified on both the stock and the commodity future markets under broad testing conditions.

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Citations

Jul 9, 2016·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Yue Deng Qionghai Dai
May 18, 2018·IEEE Transactions on Neural Networks and Learning Systems·Jie PanXuesong Wang
Jul 11, 2018·IEEE Transactions on Neural Networks and Learning Systems· Fuyong Xing Lin Yang
Jul 11, 2018·IEEE Transactions on Neural Networks and Learning Systems·Hong YuJun Guo
Apr 26, 2019·Nature·Iyad RahwanMichael Wellman
Jul 10, 2019·Computational and Mathematical Methods in Medicine·Ziang PeiWufan Chen
Mar 29, 2021·Neural Networks : the Official Journal of the International Neural Network Society·Avraam TsantekidisAnastasios Tefas
Aug 24, 2021·The Journal of Supercomputing·Jing-You LuTian-Shyr Dai
Sep 2, 2021·Knowledge and Information Systems·Junde ChenY A Nanehkaran
Oct 7, 2021··Daniel Hasan DalipRenato Arantes de Oliveira

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