英文标题：
《Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM
Neural Networks》
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作者：
David W. Lu
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最新提交年份：
2017
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英文摘要：
With the breakthrough of computational power and deep neural networks, many areas that we haven\'t explore with various techniques that was researched rigorously in past is feasible. In this paper, we will walk through possible concepts to achieve robo-like trading or advising. In order to accomplish similar level of performance and generality, like a human trader, our agents learn for themselves to create successful strategies that lead to the human-level long-term rewards. The learning model is implemented in Long Short Term Memory (LSTM) recurrent structures with Reinforcement Learning or Evolution Strategies acting as agents The robustness and feasibility of the system is verified on GBPUSD trading.
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中文摘要：
随着计算能力和深度神经网络的突破，许多我们过去没有用各种技术进行严格研究的领域是可行的。在本文中，我们将探讨实现机器人式交易或咨询的可能概念。为了实现类似水平的绩效和通用性，就像人类交易员一样，我们的代理人会自己学习创建成功的策略，从而获得人类水平的长期回报。该学习模型在长-短期记忆（LSTM）循环结构中实现，强化学习或进化策略作为代理。该系统的鲁棒性和可行性在英镑兑美元交易中得到验证。
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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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