A Multi-agent Q-learning Framework for Optimizing Stock Trading Systems

Lee, Jae Won; Jangmin, O

doi:10.1007/3-540-46146-9_16

Cited by 19 publications

(11 citation statements)

References 8 publications

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“…In some cases, cooperative agents represent the interest of a single company or individual, and merely fulfill different functions in the trading process, such as buying and selling [68]. In other cases, self-interested agents interact in parallel with the market [48,98,125].…”

Section: Automated Tradingmentioning

confidence: 99%

“…MARL approaches to automated trading typically involve temporal-difference [118] or Q-learning agents, using approximate representations of the Q-functions to handle the large state space [48,68,125]. In some cases, cooperative agents represent the interest of a single company or individual, and merely fulfill different functions in the trading process, such as buying and selling [68].…”

Section: Automated Tradingmentioning

confidence: 99%

See 1 more Smart Citation

Multi-agent Reinforcement Learning: An Overview

Buşoniu

Babuška

Schutter

2010

Studies in Computational Intelligence

482

221

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Abstract. Multi-agent systems can be used to address problems in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must instead discover a solution on their own, using learning. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. This chapter reviews a representative selection of multi-agent reinforcement learning (MARL) algorithms for fully cooperative, fully competitive, and more general (neither cooperative nor competitive) tasks. The benefits and challenges of MARL are described. A central challenge in the field is the formal statement of a multi-agent learning goal; this chapter reviews the learning goals proposed in the literature. The problem domains where MARL techniques have been applied are briefly discussed. Several MARL algorithms are applied to an illustrative example involving the coordinated transportation of an object by two cooperative robots. In an outlook for the MARL field, a set of important open issues are identified, and promising research directions to address these issues are outlined.

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Section: Automated Tradingmentioning

confidence: 99%

Section: Automated Tradingmentioning

confidence: 99%

Multi-agent Reinforcement Learning: An Overview

Buşoniu

Babuška

Schutter

2010

Studies in Computational Intelligence

482

221

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show abstract

“…In some cases, cooperative agents represent the interest of a single company or individual, and merely fulfil different functions in the trading process, such as buying and selling [103], [104]. In other cases, self-interested agents interact in parallel with the market [102], [105], [106].…”

Section: Automated Tradingmentioning

confidence: 99%

A Comprehensive Survey of Multiagent Reinforcement Learning

Buşoniu

Babuška

Schutter

2008

IEEE Trans. Syst., Man, Cybern. C

1,710

947

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Abstract-Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must, instead, discover a solution on their own, using learning. A significant part of the research on multiagent learning concerns reinforcement learning techniques. This paper provides a comprehensive survey of multiagent reinforcement learning (MARL). A central issue in the field is the formal statement of the multiagent learning goal. Different viewpoints on this issue have led to the proposal of many different goals, among which two focal points can be distinguished: stability of the agents' learning dynamics, and adaptation to the changing behavior of the other agents. The MARL algorithms described in the literature aim-either explicitly or implicitly-at one of these two goals or at a combination of both, in a fully cooperative, fully competitive, or more general setting. A representative selection of these algorithms is discussed in detail in this paper, together with the specific issues that arise in each category. Additionally, the benefits and challenges of MARL are described along with some of the problem domains where the MARL techniques have been applied. Finally, an outlook for the field is provided.

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“…Supervised learning such as neural networks, decision trees, and SVMs (Support Vector Machines) are intrinsically well suited to the problem [5,11]. The risk management and portfolio optimization have been intensively studied in reinforcement learning [6,[8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Also the portfolios of the researches [8] are simple because they focus on switching just between two price series. The works [6,10] treat trading individual stocks in reinforcement learning but lack in asset allocation.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Asset Allocation Exploiting Predictors in Reinforcement Learning Framework

Jangmin

Lee

et al. 2004

Machine Learning: ECML 2004

Self Cite

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Abstract. Given the pattern-based multi-predictors of the stock price, we study a method of dynamic asset allocation to maximize the trading performance. To optimize the proportion of asset to be allocated to each recommendations of the predictors, we design an asset allocator called meta policy in the Q-learning framework. We utilize both the information of each predictor's recommendations and the ratio of the stock fund over the asset to efficiently describe the state space. The experimental results on Korean stock market show that the trading system with the proposed asset allocator outperforms other systems with fixed asset allocation methods. This means that reinforcement learning can bring synergy effects to the decision making problem through exploiting supervised-learned predictors.

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A Multi-agent Q-learning Framework for Optimizing Stock Trading Systems

Cited by 19 publications

References 8 publications

Multi-agent Reinforcement Learning: An Overview

Multi-agent Reinforcement Learning: An Overview

A Comprehensive Survey of Multiagent Reinforcement Learning

Dynamic Asset Allocation Exploiting Predictors in Reinforcement Learning Framework

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