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/).
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 -