金融市场上日内的异常波动可能与重大经济事件相关.因此通过分析高频市场数据的异常与市场内在事件属性及其联系,可以帮助市场参与者更深刻地理解市场动态特性.采用了基于最大模量小波转换(WTMM)的多重分形形式,通过上海证券市场的日内指数价格数据来判断高频时间序列下异常点的存在和定位.结果表明,上述小波方法在检测和定位高频金融时间序列数据中的异常点方面是有效的. The abnormal fluctuations in the financial markets during the day may be related to major economic events, so analyzing the abnormality of the high-frequency market data and the inherent properties of the market and their relations can help market participants understand the market dynamics more deeply and adopt the most significant (WTMM) in the form of multifractal, the existence and location of abnormal points in high-frequency time series are judged by the intra-day index price data of Shanghai Stock Market.The results show that the above wavelet method can detect and locate high-frequency financial time series data In the abnormal point is effective.