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Survey on Financial Signal Representation and Trading using Deep Neural Network
Gayatri Panchwagh,  Mr. Deepak Gupta
Financial signal management is the process of constant redistribution of a fund into different financial products. 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. This proposed model is inspired by two biological-related learning concepts of deep learning (DL) and reinforcement learning (RL). In the technique, 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. We propose a task-aware back propagation 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.
Keywords- Reinforcement Learning, Recurrent Deep Neural Network, Deep learning, High Frequency Trading, Financial Market.
Unique Identification Number - IJEDR1804066Page Number(s) - 355-357Pubished in - Volume 6 | Issue 4 | December 2018DOI (Digital Object Identifier) -    Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
Gayatri Panchwagh,  Mr. Deepak Gupta,   "Survey on Financial Signal Representation and Trading using Deep Neural Network"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.6, Issue 4, pp.355-357, December 2018, Available at :http://www.ijedr.org/papers/IJEDR1804066.pdf