英文标题：
《Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book
Financial Market》
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作者：
Arthur le Calvez and Dave Cliff
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最新提交年份：
2018
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英文摘要：
We report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.
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中文摘要：
我们报告了使用深度学习神经网络（DLNNs）纯粹通过观察来学习电子市场中盈利交易者的行为的成功结果，该电子市场密切模仿了在现实世界全球金融市场中常见的股票（股票和股票）、货币、债券、商品和衍生品的限额指令簿（LOB）市场机制。成功的真人交易员和先进的自动算法交易系统，从经验中学习，并随着时间的推移适应市场条件的变化；我们的DLNN学习复制这种自适应交易行为。我们工作的一个新方面是，我们不涉及试图预测可交易证券价格时间序列的传统方法。相反，我们通过只观察市场上成功的销售交易员发布的报价、交易员正在执行的订单的详细信息，以及交易员活跃期间LOB上可用的数据（通常由集中交易所提供），来收集大量培训数据。在本文中，我们证明了适当配置的DLNN可以学习复制成功的自适应自动交易者的交易行为，这是一种先前证明优于人类交易者的算法系统。我们还证明，与提供培训数据的交易者相比，DLNNs可以学习更好的表现（即更具盈利能力）。我们认为，这是首次证明DLNNs可以成功复制一个在真实世界金融市场的真实模拟中操作的类人或超人适应性交易者。我们的结果可以被视为概念证明，DLNN原则上可以观察真实金融市场中人类交易者的行为，随着时间的推移，学会与人类交易者一样进行交易，甚至可能更好。
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分类信息：

一级分类：Computer Science 计算机科学
二级分类：Computational Engineering, Finance, and Science 计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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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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