Using tensorflow to solve the problems of financial forecasting for high-frequency trading

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Using tensorflow to solve the problems of financial forecasting for high-frequency trading. / Bogdanov, A. V.; Stankus, A. S.

In: CEUR Workshop Proceedings, Vol. 2267, 2018, p. 513-517.

Research output: Contribution to journal › Conference article › peer-review

BibTeX

@article{b8da4c7f9b904ecdb49a85b016729b0d,

title = "Using tensorflow to solve the problems of financial forecasting for high-frequency trading",

abstract = "The use of neural networks significantly expands the possibilities of analyzing financial data and improves the quality indicators of the financial market. In article we examine various aspects of working with neural networks and Frame work TensorFlow, such as choosing the type of neural networks, preparing data and analyzing the results. The work was carried out on the real data of the financial instrument Si-6.16 (futures contract on the US dollar rate).",

keywords = "Artificial Intelligence, Financial market forecasting, Recurrent neural network (RNN), TensorFlow",

author = "Bogdanov, {A. V.} and Stankus, {A. S.}",

note = "Publisher Copyright: {\textcopyright} 2018 Alexander V. Bogdanov, Alexey S. Stankus. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 8th International Conference {"}Distributed Computing and Grid-Technologies in Science and Education{"}, GRID 2018 ; Conference date: 10-09-2018 Through 14-09-2018",

year = "2018",

language = "English",

volume = "2267",

pages = "513--517",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

publisher = "RWTH Aahen University",

}

RIS

TY - JOUR

T1 - Using tensorflow to solve the problems of financial forecasting for high-frequency trading

AU - Bogdanov, A. V.

AU - Stankus, A. S.

PY - 2018

Y1 - 2018

N2 - The use of neural networks significantly expands the possibilities of analyzing financial data and improves the quality indicators of the financial market. In article we examine various aspects of working with neural networks and Frame work TensorFlow, such as choosing the type of neural networks, preparing data and analyzing the results. The work was carried out on the real data of the financial instrument Si-6.16 (futures contract on the US dollar rate).

AB - The use of neural networks significantly expands the possibilities of analyzing financial data and improves the quality indicators of the financial market. In article we examine various aspects of working with neural networks and Frame work TensorFlow, such as choosing the type of neural networks, preparing data and analyzing the results. The work was carried out on the real data of the financial instrument Si-6.16 (futures contract on the US dollar rate).

KW - Artificial Intelligence

KW - Financial market forecasting

KW - Recurrent neural network (RNN)

KW - TensorFlow

UR - http://www.scopus.com/inward/record.url?scp=85060110278&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85060110278

VL - 2267

SP - 513

EP - 517

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

T2 - 8th International Conference "Distributed Computing and Grid-Technologies in Science and Education", GRID 2018

Y2 - 10 September 2018 through 14 September 2018

ER -

ID: 76157557