Automating chart pattern recognition is a relevant issue addressed by researchers and practitioners when designing a system that considers technical analysis for trading purposes. This article proposes the design of a trading system that takes into account any generic pattern that has been proven to be profitable in the past, without restricting the search to the specific technical patterns reported in the literature, hence the term generic pattern recognition. A fast version of dynamic time warping, the University College Riverside subsequence search suite (called the UCR suite), is employed for the pattern recognition task in an effort to produce trading signals in realistic timescales. This article evaluates the significance of the relation between the system's profitability and (a) the pattern length, (b) the take-profit and stop-loss levels and (c) the performance consensus of past patterns. The trading system is assessed under the mean–variance perspective by using 560 NYSE stocks. The results obtained by the different parameter configurations are reported, controlling for both data-snooping and transaction costs. On average, the proposed system dominates the market index in the mean–variance sense. Although transaction costs reduce the profitability of the proposed trading system, 92.5% of the experiments are profitable if the analysis is reduced to the parameter values aligned with the technical analysis.

中文翻译：

---

是什么让基于图表模式识别的交易策略有利可图？

自动图表模式识别是研究人员和从业人员在设计考虑技术分析用于交易目的的系统时解决的相关问题。本文提出了一种交易系统的设计，该系统考虑了过去已被证明有利可图的任何通用模式，而不将搜索限制在文献中报告的特定技术模式，因此术语通用模式识别. 动态时间扭曲的快速版本，即大学学院河滨子序列搜索套件（称为 UCR 套件），被用于模式识别任务，以努力在现实的时间尺度内产生交易信号。本文评估了系统盈利能力与 (a) 形态长度、(b) 止盈和止损水平以及 (c) 过去形态的表现共识之间关系的重要性。该交易系统使用 560 只纽约证券交易所股票在均值-方差视角下进行评估。报告了通过不同参数配置获得的结果，控制了数据侦听和交易成本。平均而言，所提出的系统在均值-方差意义上主导了市场指数。尽管交易成本降低了拟议交易系统的盈利能力，92。