LAUSR

Technical Traders adopt mathematical methods to formulate various technical trading rules on their trading strategies. This paper utilises two unique datasets—individual and market tick-by-tick data—to disclose the categories and characteristics of technical traders’ strategies in Chinese rebar futures market. Firstly, we use a simple multiple regression model to filter technical traders in individual dataset. By using market dataset to generate dummy signals according to six popular kinds of technical rules, we created dummy trading directions as benchmark for real trading actions. Based on the similarity between dummy signals with different technical rules and traders’ real actions, we employ k-means algorithm to classify technical traders. Through these empirical works, technical traders in my dataset are classified into 11 groups. Finally, on the basis of 11 clusters’ coordinates, the features of technical strategies in each group are summarised.