英文标题：
《Multi-Agent Deep Reinforcement Learning for Liquidation Strategy
  Analysis》
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作者：
Wenhang Bao, Xiao-yang Liu
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最新提交年份：
2019
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英文摘要：
  Liquidation is the process of selling a large number of shares of one stock sequentially within a given time frame, taking into consideration the costs arising from market impact and a trader\'s risk aversion. The main challenge in optimizing liquidation is to find an appropriate modeling system that can incorporate the complexities of the stock market and generate practical trading strategies. In this paper, we propose to use multi-agent deep reinforcement learning model, which better captures high-level complexities comparing to various machine learning methods, such that agents can learn how to make the best selling decisions. First, we theoretically analyze the Almgren and Chriss model and extend its fundamental mechanism so it can be used as the multi-agent trading environment. Our work builds the foundation for future multi-agent environment trading analysis. Secondly, we analyze the cooperative and competitive behaviours between agents by adjusting the reward functions for each agent, which overcomes the limitation of single-agent reinforcement learning algorithms. Finally, we simulate trading and develop an optimal trading strategy with practical constraints by using a reinforcement learning method, which shows the capabilities of reinforcement learning methods in solving realistic liquidation problems.
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中文摘要：
清算是指在给定的时间范围内，考虑到市场影响和交易者风险厌恶所产生的成本，按顺序出售一只股票的大量股票的过程。优化清算的主要挑战是找到一个合适的建模系统，该系统可以结合股票市场的复杂性并生成实用的交易策略。在本文中，我们建议使用多agent深度强化学习模型，与各种机器学习方法相比，该模型能够更好地捕获高级复杂性，从而使agent能够学习如何做出最佳销售决策。首先，我们从理论上分析了Almgren和Chriss模型，并对其基本机制进行了扩展，使其可以作为多agent交易环境。我们的工作为未来的多agent环境交易分析奠定了基础。其次，通过调整每个agent的奖励函数来分析agent之间的合作和竞争行为，克服了单agent强化学习算法的局限性。最后，我们通过使用强化学习方法模拟交易，并开发出具有实际约束的最优交易策略，这表明了强化学习方法在解决实际清算问题方面的能力。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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