COMMENTARY: Reflections on “Cluster Analysis for Evaluating Trading Strategies”

Jeffrey M. Bacidore
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Abstract

Our paper on Cluster Analysis was inspired by our need to group client data by trading strategy, when the data we were provided did not contain any information on trading strategy whatsoever. We ended up relying on a well-known statistical technique, k-means, which surprisingly had not been used widely in trading applications. At the time, non-quant traders were still reluctant to use quantitative techniques, especially black box applications like k-means. Fortunately, a lot has changed since that time, as quants are now using much more sophisticated techniques, like deep learning. And even more important, non-quant traders and business leaders have become much more accepting of such techniques, making it easier for such advanced techniques to be incorporated into trading applications.
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评论:关于“聚类分析评价交易策略”的思考
我们关于聚类分析的论文的灵感来自于我们需要根据交易策略对客户数据进行分组,当我们提供的数据不包含任何有关交易策略的信息时。我们最终依赖于一种著名的统计技术,k-means,令人惊讶的是,它并没有在交易应用中得到广泛应用。当时,非量化交易员仍然不愿意使用定量技术,尤其是像k-means这样的黑箱应用。幸运的是,从那时起,很多事情都发生了变化,因为量化分析师现在使用了更复杂的技术,比如深度学习。更重要的是,非量化交易员和商业领袖已经变得更加接受这些技术,使这些先进的技术更容易被纳入交易应用程序。
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