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Ekarat Rattagan. Enhancing market making strategies with deep reinforcement learning-based quoting decisions. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2024.
Enhancing market making strategies with deep reinforcement learning-based quoting decisions
Name: Chawanvit Ekarittikrai
Organization :
National Institute of Development Administration (NIDA). Graduate School of Applied Statistic
Email :
chawanvit.eka@stu.nida.ac.th
Name: Ekarat Rattagan
Organization :
National Institute of Development Administration (NIDA). Graduate School of Applied Statistic
Email :
ekarat@as.nida.ac.th
keyword:
Market making (Securities)
LCSH:
Reinforcement learning
LCSH:
Artificial intelligence
LCSH:
Investment analysis
LCSH:
Machine learning
Abstract:
Market making strategies often struggle to adapt to
complex market dynamics in rapidly changing conditions. This
paper explores the application of deep reinforcement learning
(DRL) techniques to enhance market making by integrating
DRL agents with the Avellaneda-Stoikov (AS) model for optimal
quoting. We propose a framework where the A2C and PPO
agents, responsible for deciding whether to quote or not, are
employed and compared against the traditional AS agent, which
continuously quotes, in a simulated market environment with
realistic order matching engine. Extensive experiments are conducted
using historical tick-level trade data from the BTCUSDT
perpetual futures market, chosen for its high volatility and
rapidly changing market dynamics, to assess the performance of
the DRL agents against the AS agent. The results indicate that
the DRL agents exhibit higher quote rates for filled quotes and
lower quote rates for expired quotes compared to the AS agent,
showcasing their ability to selectively skip unprofitable quotes.
However, the agents overall profitability is significantly impacted
by substantial losses from expired quotes due to counter-reversal
market dynamics.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2024
Modified:
2025-06-09
Issued:
2025-06-09
บทความ/Article
application/pdf
BibliograpyCitation :
In IEEE Thailand Section (IEEE Computer Society Thailand Chapter) and Prince of Songkla University. College of Computing. The 21st International Joint Conference on Computer Science and Software Engineering (JCSSE 2024)) (pp.324-329). Phuket : Prince of Songkla University
eng
©copyrights King Mongkut's University of Technology North Bangkok
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Chawanvit Ekarittikrai
| Title | Contributor | Type |
|---|---|---|
| Enhancing market making strategies with deep reinforcement learning-based quoting decisions
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Chawanvit Ekarittikrai;Ekarat Rattagan | บทความ/Article |
Ekarat Rattagan
| Title | Contributor | Type |
|---|---|---|
| Enhancing market making strategies with deep reinforcement learning-based quoting decisions
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Chawanvit Ekarittikrai;Ekarat Rattagan | บทความ/Article |