Exploring Trading Strategies and their Effects in the FX Market

One of the most critical issues that developers face in developing automatic systems or software agents for electronic markers is that of endowing the agents with appropriate trading strategies. In this paper, we examine the problem in the Foreign Exchange (FX) market and we use an agent-based FX market simulation to examine which trading strategies lead to market states in which the stylized facts (statistical properties) of the simulation match the stylised facts of the actual FX market transactions data. In particular, our goal is to explore the emergence of the stylized facts of the transactions data, when the simulated market is populated with agents using three different strategies: a variation of the zero-intelligence with a constraint (ZI-CV) strategy; the zero-intelligence directional-change event (ZI-DCT0) strategy; and a genetic programmingbased (GP) strategy. A series of experiments were conducted in an existing agent-based FX market with these three strategies and the results were compared against those of a high-frequency transactions dataset from the FX market. Our results show that the ZI-DCT0 agents best reproduce and explain the properties and phenomena observed in the FX market real transactions data. Our study suggests that the observed stylized facts of the FX market transactions data could be the result of introducing a threshold which triggers the agents to respond to fixed periodic patterns in the price time series. The results of this study can be used further to develop decision support systems and autonomous trading agent strategies for the FX market.