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Multi-agent reinforcement learning approach for hedging portfolio problem

Uyen Pham, Quoc Luu, Hien Tran
2021 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
Inspired by recently achievement of deep reinforcement learning, we explore feasibility to construct a hedging strategy automatically by leveraging cooperative multi-agent in reinforcement learning techniques  ...  , tax, and settlement date of transactions) for reinforcement learning agent training.  ...  Multi-Agent Reinforcement Learning Learned deep RL agent was deployed to trade out-of-sample market data from May 17, 2019, to May 21, 2020.  ... 
doi:10.1007/s00500-021-05801-6 pmid:33897298 pmcid:PMC8054257 fatcat:4yse63ba2jcb7e7k5ra432knn4

Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking [article]

Yue Wang, Yao Wan, Chenwei Zhang, Lixin Cui, Lu Bai, Philip S. Yu
2019 arXiv   pre-print
In particular, we propose a multi-agent deep reinforcement learning model with a structure which mimics the human-psychological counterfactual thinking process to improve the competitive abilities for  ...  This paper investigates the counterfactual thinking for agents to find optimal decision-making strategies in multi-agent reinforcement learning environments.  ...  We test CFT on standard multi-agent deep reinforcement learning platforms and real-world problems.  ... 
arXiv:1908.04573v2 fatcat:ouxrpzum7fc7dpnw4mxjm2sfpu

Deep Reinforcement Learning Attention Selection for Person Re-Identification [article]

Xu Lan, Hanxiao Wang, Shaogang Gong, Xiatian Zhu
2018 arXiv   pre-print
In this work, we develop a joint learning deep model that optimises person re-id attention selection within any auto-detected person bounding boxes by reinforcement learning of background clutter minimisation  ...  Specifically, we formulate a novel unified re-id architecture called Identity DiscriminativE Attention reinforcement Learning (IDEAL) to accurately select re-id attention in auto-detected bounding boxes  ...  Re-ID Attention Selection by Reinforcement Learning The Identity DiscriminativE Attention reinforcement Learning (IDEAL) model has two subnetworks: (I) A multi-class discrimination network D by deep learning  ... 
arXiv:1707.02785v4 fatcat:3fjqw3uopfbjbaq4d4rf2ebyiu

Deep Reinforcement Learning in Agent Based Financial Market Simulation

Iwao Maeda, David deGraw, Michiharu Kitano, Hiroyasu Matsushima, Hiroki Sakaji, Kiyoshi Izumi, Atsuo Kato
2020 Journal of Risk and Financial Management  
In this study we propose a framework for training deep reinforcement learning models in agent based artificial price-order-book simulations that yield non-trivial policies under diverse conditions with  ...  to find investment strategies using deep reinforcement learning.  ...  Proposal and verification of a deep reinforcement learning framework that learns meaningful trading strategies in agent based artificial market simulations 2.  ... 
doi:10.3390/jrfm13040071 fatcat:6rpzflkjxramvntlhv6py5tuc4

A parallel-network continuous quantitative trading model with GARCH and PPO [article]

Zhishun Wang, Wei Lu, Kaixin Zhang, Tianhao Li, Zixi Zhao
2021 arXiv   pre-print
Experiments in 5 stocks from Chinese stock market show our method achieves more extra profit comparing with basical reinforcement learning methods and bench models.  ...  Thus, we propose a parallel-network continuous quantitative trading model with GARCH and PPO to enrich the basical deep reinforcement learning model, where the deep learning parallel network layers deal  ...  To tackle these challenges, we propose a multi-frequency continuousshare quantitative trading algorithm with GARCH (MCTG) based on deep reinforcement learning.  ... 
arXiv:2105.03625v2 fatcat:rof7n76si5ajjm77zqybeocyrm

FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance [article]

Xiao-Yang Liu, Jingyang Rui, Jiechao Gao, Liuqing Yang, Hongyang Yang, Zhaoran Wang, Christina Dan Wang, Jian Guo
2022 arXiv   pre-print
Deep reinforcement learning (DRL) has shown huge potentials in building financial market simulators recently.  ...  In this paper, we present a FinRL-Meta framework that builds a universe of market environments for data-driven financial reinforcement learning.  ...  Compared to traditional simulation models, deep reinforcement learning (DRL) has shown huge potentials in building financial market simulators through multi-agent systems [10] .  ... 
arXiv:2112.06753v2 fatcat:mdrhekejcngirlsxeq67mfushq

Optimizing Market Making using Multi-Agent Reinforcement Learning [article]

Yagna Patel
2018 arXiv   pre-print
In this paper, reinforcement learning is applied to the problem of optimizing market making.  ...  A multi-agent reinforcement learning framework is used to optimally place limit orders that lead to successful trades. The framework consists of two agents.  ...  Q-Learning The specific reinforcement learning algorithm used for both agents is deep Q-learning.  ... 
arXiv:1812.10252v1 fatcat:bbisnxloyjdglecu5mbcpxcnny

Deep Hierarchical Strategy Model For Multi-Source Driven Quantitative Investment

Chunming Tang, Wenyan Zhu, Xiang Yu
2019 IEEE Access  
Using deep learning to maximize the benefits of a series of risks in the securities market was a very interesting and widely concerned problem.  ...  deep deterministic policy gradient (DDPG), which is one of the reinforcement learning algorithms to decide continuous trading position.  ...  INTRODUCTION In recent years, supervised learning and reinforcement learning have been applied to the research of stock quantitative investment [1] - [4] .  ... 
doi:10.1109/access.2019.2923267 fatcat:bur5qnonenfitg2kuad7ehsucm

TFCMA-DRL: Tolerant Flexible Coordinated Multi-Agent Deep Reinforcement Learning for Prediction of Future Stock Price Trends from Multi-Source Data

Chinnasamy Bhuvaneshwari, Kongunadu Arts and Science College, Raman Beena, Kongunadu Arts and Science College
2021 International Journal of Intelligent Engineering and Systems  
To tackle these issues, the Tolerant Flexible Coordinated Multi-Agent Deep Reinforcement Learning (TFCMA-DRL) model is proposed in this paper.  ...  Machine learning and deep learning algorithms were the most efficient techniques used for prediction by extracting public opinions and events.  ...  In this paper, Coordination policy is integrated with Multi-Agent Deep Reinforcement Learning to form TFCMA-DRL based stock price forecasting model for multi-source data.  ... 
doi:10.22266/ijies2021.0430.04 fatcat:xrsrb7yjeneorgvsbzlw5tb63y

Deep reinforcement learning for portfolio management [article]

Gang Huang, Xiaohua Zhou, Qingyang Song
2022 arXiv   pre-print
In our paper, we apply deep reinforcement learning approach to optimize investment decisions in portfolio management.  ...  The experimental results show that our model is able to optimize investment decisions and has the ability to obtain excess return in stock market, and the optimized agent maintains the asset weights at  ...  Reinforcement learning has four basic elements: agent, state or environment, action and reward. The agent takes actions to obtain rewards in the state and gets to the next state.  ... 
arXiv:2012.13773v7 fatcat:vbgiqqg6lbfyphyd44ve7h7ymu

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics [article]

Amir Mosavi, Pedram Ghamisi, Yaser Faghan, Puhong Duan
2020 arXiv   pre-print
The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased.  ...  DRL through a wide range of capabilities from reinforcement learning (RL) and deep learning (DL) for handling sophisticated dynamic business environments offers vast opportunities.  ...  Although, there were some recent research that applied multi-agent reinforcement learning (MARL) scenarios to a small set of agents but not to a large set of agents [126] .  ... 
arXiv:2004.01509v1 fatcat:4ggjzkfdi5fe3g7uwb7dwbvtue

Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms

Soyi Jung, Won Joon Yun, Joongheon Kim, Jae-Hyun Kim
2021 Electronics  
This paper proposes a novel coordinated multi-agent deep reinforcement learning (MADRL) algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to conduct big-data processing  ...  When the required energy for charging UAVs is not enough in charging towers, the energy purchase from utility company (i.e., energy source provider in local energy market) is desired, which takes high  ...  The proposed coordinated multi-agent deep reinforcement learning (DRL) (MADRL)based autonomous and intelligent energy sharing in order to minimize energy purchases from the local energy market for minimizing  ... 
doi:10.3390/electronics10050543 fatcat:jhjkpioqgveafg4fafjuy6iefq

Evolutionary DRL Environment: Transfer Learning-Based Genetic Algorithm

Badr Hirchoua, Imadeddine Mountasser, Brahim Ouhbi, Bouchra Frikh
2022 Journal of Data Intelligence  
Precisely, we train a multi-agent reinforcement learning algorithm that uses only self trades generated by different generations of agents.  ...  We address the algorithmic trading problem utilising an evolutive learning method.  ...  deep reinforcement learning, transfer learning, and multi-agent learning.  ... 
doi:10.26421/jdi3.3-3 fatcat:xls454ztibeinow2tnbpxk4ysy

Correlated Deep Q-learning based Microgrid Energy Management

Hao Zhou, Melike Erol-Kantarci
2020 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)  
In this paper, the Long Short Term Memory networks (LSTM) based deep Q-learning algorithm is introduced and the correlated equilibrium is proposed to coordinate agents.  ...  Considering the significant potential of machine learning techniques, this paper proposes a correlated deep Q-learning (CDQN) based technique for the MG energy management.  ...  In MARL, each agent is expected to learn the cooperation strategy. Naturally, MARL can also be generalized to multi-agent deep reinforcement learning (MADRL).  ... 
doi:10.1109/camad50429.2020.9209254 dblp:conf/camad/ZhouE20 fatcat:zebjmiwh7bdn7alf3irgvvzo5y

Fairness in Multi-agent Reinforcement Learning for Stock Trading [article]

Wenhang Bao
2019 arXiv   pre-print
In this paper, we propose a novel scheme that utilizes multi-agent reinforcement learning systems to derive stock trading strategies for all clients which keep a balance between revenue and fairness.  ...  First, we demonstrate that Reinforcement learning (RL) is able to learn from experience and adapt the trading strategies to the complex market environment.  ...  Multi-agent Reinforcement Learning Setting The advantage of multi-agent over single-agent reinforcement learning is the ability to incorporate high-level complexities in the system.  ... 
arXiv:2001.00918v1 fatcat:4sb7z3n3h5hr5hzubgcwghtxyq
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