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Using LCS to Exploit Order Book Data in Artificial Markets

机译：使用LCS在人工市场中利用订单簿数据

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摘要

In the study of financial phenomena, multi-agent market order-driven simulators are tools that can effectively test different economic assumptions. Many studies have focused on the analysis of adaptive learning agents carrying on prices. But the prices are a consequence of the matching orders. Reasoning about orders should help to anticipate future prices. While it is easy to populate these virtual worlds with agents analyzing "simple" prices shapes (rising or falling, moving averages, ...), it is nevertheless necessary to study the phenomena of rationality and influence between agents, which requires the use of adaptive agents that can learn from their environment. Several authors have obviously already used adaptive techniques but mainly by taking into account prices historical. But prices are only consequences of orders, thus reasoning about orders should provide a step ahead in the deductive process. In this article, we show how to leverage information from the order books such as the best limits, the bid-ask spread or waiting cash to adapt more effectively to market offerings. Like B. Arthur, we use learning classifier systems and show how to adapt them to a multi-agent system.
机译：在研究金融现象时，多主体市场订单驱动的模拟器是可以有效测试不同经济假设的工具。许多研究集中于对进行价格调整的自适应学习主体的分析。但是价格是匹配订单的结果。关于订单的推理应该有助于预测未来的价格。尽管使用代理分析“简单的”价格形状（上升或下降，移动平均线等）来填充这些虚拟世界很容易，但是仍然有必要研究代理之间的合理性和影响现象，这需要使用可以从环境中学习的适应性主体。显然，有几位作者已经使用了自适应技术，但主要是考虑了历史价格。但是价格只是订单的结果，因此对订单的推理应该在演绎过程中领先一步。在本文中，我们展示了如何利用订单簿中的信息，例如最佳限价，买卖价差或等待现金，以更有效地适应市场报价。像B. Arthur一样，我们使用学习分类器系统，并展示如何使它们适应多主体系统。

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