Outlier detection has become essential in various fields such as defense monitoring, fiscal anomaly identification, and business industries. Nevertheless, outlier detection methods based on distance or density suffer from certain limitations. The performance of traditional outlier detection algorithms is susceptible to various factors such as data point density, shape, and other factors. To address these challenges, we propose an outlier detection algorithm, which utilizes symmetry ratio and distance ratio to determine the outlier degree of the data point. Moreover, it automatically finds the threshold of outlier degree using the interquartile range method. Experimental results on synthetic and UCI real datasets demonstrate the excellent performance of our algorithm in detecting outliers.