英文标题：
《Capturing Financial markets to apply Deep Reinforcement Learning》
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作者：
Souradeep Chakraborty
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最新提交年份：
2019
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英文摘要：
  In this paper we explore the usage of deep reinforcement learning algorithms to automatically generate consistently profitable, robust, uncorrelated trading signals in any general financial market. In order to do this, we present a novel Markov decision process (MDP) model to capture the financial trading markets. We review and propose various modifications to existing approaches and explore different techniques like the usage of technical indicators, to succinctly capture the market dynamics to model the markets. We then go on to use deep reinforcement learning to enable the agent (the algorithm) to learn how to take profitable trades in any market on its own, while suggesting various methodology changes and leveraging the unique representation of the FMDP (financial MDP) to tackle the primary challenges faced in similar works. Through our experimentation results, we go on to show that our model could be easily extended to two very different financial markets and generates a positively robust performance in all conducted experiments.
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中文摘要：
在本文中，我们探讨了如何使用深度强化学习算法在任何一般金融市场中自动生成持续盈利、稳健、不相关的交易信号。为了做到这一点，我们提出了一种新的马尔可夫决策过程（MDP）模型来捕捉金融交易市场。我们审查并提出对现有方法的各种修改，并探索不同的技术，如技术指标的使用，以简洁地捕捉市场动态，对市场进行建模。然后，我们继续使用深度强化学习，使代理（算法）能够学习如何在任何市场上独自进行有利可图的交易，同时提出各种方法变更，并利用FMDP（财务MDP）的独特表示来应对类似工作中面临的主要挑战。通过我们的实验结果，我们进一步表明，我们的模型可以很容易地扩展到两个非常不同的金融市场，并在所有进行的实验中产生了积极稳健的性能。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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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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