Classification of trading strategies in a forex market

Abstract

Recent popularity in algorithmic trading has spurred on researchers to investigate the variety and the evolution of the trading strategies. In this talk, we present our recent study (under review in PLOS ONE), in which the strategy distribution of limit orders is analyzed by using the high frequency data set including anonymized trader IDs. We first identify timescales for each trader to measure market-price trends by the multi-regression analysis. Clustering the timescales into several clusters, we then show the frequencies of the submissions and transactions for each cluster. Furthermore, we provide the microstructure insight to their frequencies in terms of the average shape of limit orders. Finally, we quantify the activity level of each cluster, and show that some clusters are unique to the local time in Tokyo or New York.