英文标题：
《Social signals and algorithmic trading of Bitcoin》
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作者：
David Garcia, Frank Schweitzer
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最新提交年份：
2015
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英文摘要：
The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behavior offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology, and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence, and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin, reaching very high profits in less than a year. We verify this high profitability with robust statistical methods that take into account risk and trading costs, confirming the long-standing hypothesis that trading based social media sentiment has the potential to yield positive returns on investment.
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中文摘要：
数字记录道上数据的可用性正以前所未有的规模增长，但从大规模数据中推断可操作的知识绝非微不足道。这对计算金融尤其重要，在计算金融中，人类行为的数字痕迹提供了推动交易策略的巨大潜力。为此，我们提供了一种一致的方法，在算法交易员的设计中集成各种数据源。这使我们能够深入了解交易策略盈利能力背后的原则。我们通过分析比特币来说明我们的方法，比特币是一种以价格大幅波动而闻名的加密货币。在我们的分析中，我们包括了美元兑换量和价格的经济信号，比特币技术的采用，以及比特币的交易量。我们添加了与信息搜索、口碑数量、情感配价和观点极化相关的社交信号，这些信息在与比特币相关的推文中表达了三年多。我们的分析表明，意见两极分化和交换量的增加先于比特币价格的上涨，而情感配价先于意见两极分化和交换量的上升。我们运用这些见解为比特币设计算法交易策略，在不到一年的时间里实现了极高的利润。我们通过考虑风险和交易成本的稳健统计方法验证了这种高盈利能力，证实了基于交易的社交媒体情绪有可能产生正投资回报的长期假设。
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分类信息：

一级分类：Computer Science 计算机科学
二级分类：Social and Information Networks 社会和信息网络
分类描述：Covers the design, analysis, and modeling of social and information networks, including their applications for on-line information access, communication, and interaction, and their roles as datasets in the exploration of questions in these and other domains, including connections to the social and biological sciences. Analysis and modeling of such networks includes topics in ACM Subject classes F.2, G.2, G.3, H.2, and I.2; applications in computing include topics in H.3, H.4, and H.5; and applications at the interface of computing and other disciplines include topics in J.1--J.7. Papers on computer communication systems and network protocols (e.g. TCP/IP) are generally a closer fit to the Networking and Internet Architecture (cs.NI) category.
涵盖社会和信息网络的设计、分析和建模，包括它们在联机信息访问、通信和交互方面的应用，以及它们作为数据集在这些领域和其他领域的问题探索中的作用，包括与社会和生物科学的联系。这类网络的分析和建模包括ACM学科类F.2、G.2、G.3、H.2和I.2的主题；计算应用包括H.3、H.4和H.5中的主题；计算和其他学科接口的应用程序包括J.1-J.7中的主题。关于计算机通信系统和网络协议（例如TCP/IP）的论文通常更适合网络和因特网体系结构（CS.NI）类别。
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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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