基于CUSUM的复杂事件处理趋势跟踪交易研究

发布时间：2018-06-08 01:53

本文选题：算法交易 + 趋势跟踪交易　；参考：《中南财经政法大学》2017年硕士论文

【摘要】：算法交易通过预先设定的计算机程序寻找市场上的交易机会并做出交易决定,然后高效、低成本地实现交易订单的执行和成交。目前,算法交易已经成为资本市场的主流。复杂事件处理引擎能够实时处理瞬息万变的市场信息数据,捕获稍纵即逝的算法交易时机,有良好的应用前景。然而,复杂事件处理在算法交易的应用还处在新兴阶段,只是通过比较前后价格的大小发现趋势。这样的阈值过于敏感,匹配到的模式多且持续时间短,有较强的偶然性,并不能准确判断趋势的出现。CUSUM(累积和)控制方法能够过滤过于微小的波动得到一段时期的整体趋势,对微小的偏移较为敏感,而且适用于流处理。针对使用复杂事件处理进行趋势跟踪时趋势的界定阀值过于敏感的问题,在CUSUM控制方法的基础上本文提出了基于CUSUM的趋势判断算法,以及根据趋势判断进行交易的两个基本的交易策略:极值点交易策略和趋势追随交易策略。设计了CUSUM趋势跟踪的复杂事件处理实现,包括事件处理网络设计、事件定义以及事件模式定义。最后,通过实证和性能测试证明基于CUSUM的趋势跟踪复杂事件处理实现是可行且高效的。为了验证所提出方法是否能够获利,本文选取华泰柏瑞沪深300ETF(510300)在2015年1月5日至12月31日的收盘价作为行情数据进行模拟交易,并使用了开源的复杂事件处理引擎Esper来实现所提出的CUSUM趋势跟踪交易策略。实验表明:1、CUSUM趋势判断方法比前后价格大小比较的趋势判断方法产生更少的趋势信号,说明CUSUM趋势判断方式能够通过阈值的控制过滤微小的波动,从而有效降低交易次数。2、在收益方面,CUSUM趋势判断方法配合趋势追随交易策略能够获得更高的收益。这种交易方法的特点是在平稳细微震荡时期较少进行交易;在大幅波动时期会进行持续时间较为短暂的做多操作,但一般操作结果为亏损;在持续上升时期会进行持续时间较长的做多操作,此时一般会有丰厚的获利。3、在性能方面,系统在稳定阶段有96%的事件能够在10微秒以内处理完成,事件平均延迟约为4.5微秒,而且在系统承受范围内规则数的增加并不影响事件的平均处理时间。另外系统在平稳运行阶段占用较少的CPU资源,内存使用随运行时间增加而增加,但最终能够保持平稳不再持续增加。
[Abstract]:The algorithm deals with the pre set computer program to find the trading opportunities in the market and make the transaction decision. Then, the transaction order is implemented and sold at low level. At present, the algorithm transaction has become the mainstream of the capital market. The complex event processing engine can deal with the changing market information data and capture the fast changing market information. However, the application of complex event processing in the algorithm transaction is still in the emerging phase, only by comparing the size of the price to find the trend. The threshold is too sensitive, the matching pattern is more and the duration is short, there is a strong chance, and the trend is not accurate to judge the trend. The emergence of.CUSUM (accumulation and) control methods can filter too small fluctuations to get a period of overall trend, sensitive to small offset, and suitable for flow processing. The problem that the threshold threshold is too sensitive to trend tracking using complex event processing, based on the CUSUM control method, is proposed in this paper. The trend judgment algorithm based on CUSUM, and two basic trading strategies based on trend judgment: extreme point trading strategy and trend following transaction strategy, design the implementation of complex event processing for CUSUM trend tracking, including event processing network design, event definition and event pattern definition. Finally, through empirical and sexual characteristics In order to verify whether the proposed method is profitable, this paper selects the closing price of huatberi Shanghai and Shenzhen 300ETF (510300) from January 5, 2015 to December 31st as the market data, and uses an open source complex event processing method to verify whether the proposed method is profitable or not. Esper to implement the proposed CUSUM trend tracking transaction strategy. The experiment shows: 1, the trend judgment method of CUSUM trend judgment method produces less trend signal than the trend judgment method compared with the front and back price. It shows that the CUSUM trend judgment method can filter the small wave motion through the control of the threshold, thus effectively reducing the transaction number.2 and in the income aspect. The CUSUM trend judgment method combined with the trend following trading strategy can gain higher returns. This transaction method is characterized by less trading during a smooth and subtle period; a short duration of operation in a period of large volatility, but the general operation results in a loss; it will continue in a sustained period of rise. With a long and long operation, there will be a generous profit.3 at this time. In terms of performance, 96% of the events in the stable phase can be processed within 10 microseconds. The average delay of the event is about 4.5 microseconds, and the increase in the number of rules within the range of the system does not affect the average processing time of the event. In addition, the system is running smoothly. The stage occupies less CPU resources, and the memory usage increases with the increase of running time, but eventually it can keep stable and no longer continue to increase.
【学位授予单位】：中南财经政法大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F832.51

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