Proceedings of the International Session on Factors of Regional Extensive Development (FRED 2019)

Reinforcement Learning Approach for Market-Maker Problem Solution

Authors
K.A. Lokhacheva, D.I. Parfenov, I.P. Bolodurina
Corresponding Author
K.A. Lokhacheva
Available Online January 2020.
DOI
10.2991/fred-19.2020.52How to use a DOI?
Keywords
reinforcement learning, machine learning, algorithmic trading, market make, market liquidity
Abstract

The paper considers the implementation of machine learning technologies to algorithmic trading. The paper studies the process of the stock market trading and the role of the market maker in the trading process, methods of mathematical description of the market maker strategy, along with the possibility of applying reinforcement learning to implement the market maker strategy. The results of testing and evaluating the effectiveness of the developed algorithmic and software tools on the data of the Moscow Exchange are given.

Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the International Session on Factors of Regional Extensive Development (FRED 2019)
Series
Advances in Economics, Business and Management Research
Publication Date
January 2020
ISBN
10.2991/fred-19.2020.52
ISSN
2352-5428
DOI
10.2991/fred-19.2020.52How to use a DOI?
Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - K.A. Lokhacheva
AU  - D.I. Parfenov
AU  - I.P. Bolodurina
PY  - 2020/01
DA  - 2020/01
TI  - Reinforcement Learning Approach for Market-Maker Problem Solution
BT  - Proceedings of the International Session on Factors of Regional Extensive Development (FRED 2019)
PB  - Atlantis Press
SP  - 256
EP  - 260
SN  - 2352-5428
UR  - https://doi.org/10.2991/fred-19.2020.52
DO  - 10.2991/fred-19.2020.52
ID  - Lokhacheva2020/01
ER  -