Modelling the trading behaviour in high-frequency markets

We use an agent-based approach to model trading behaviour in high-frequency markets. This study focuses on the Foreign Exchange (FX) market. The initial part of this study is to observe the micro-behaviour of traders to define the stylized facts of their trading activities. This is performed using a high-frequency dataset of anonymised individual traders´ historical transactions on an account level, provided by OANDA Ltd. This dataset is considered to be the biggest available high-frequency dataset dealing with the individual FX market traders´ trading activities. The second step is to build agent-based models of traders. The traders are modelled to respond to physical time to account for the different market seasonalities. The key elements in modeling the traders are zero-intelligence directional-change events trading strategy, historical prices, actual FX market traders´ behaviour, limit orders, FX market trading sessions and market holidays. Using the identified stylized facts of FX market trading activity, we evaluate the trading agents´ collective trading behaviour. The results of this comparison indicate that the trading agents´ collective trading behaviour resembles, to a certain extent, the collective trading behaviour of FX market traders.