Financial markets witness high levels of activity at certain times, but remain calm at others. This makes the flow of physical time discontinuous. Therefore using physical time scales for studying financial time series, runs the risk of missing important activities. An alternative approach is the use of an event-based time that captures periodic activities in the market. In this paper, we use a special type of event, called a directional-change event, and show its usefulness in capturing periodic market activities. Our study confirms that the length of the price curve coastline as defined by directional-change events, turns out to be a long one.
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In this chapter, the authors use an Agent-Based Modeling (ABM) approach to model trading behavior in the Foreign Exchange (FX) market. They establish statistical properties (stylized facts) of the traders’ trading behavior in the FX market using a high-frequency dataset of anonymised OANDA individual traders’ historical transactions on an account level spanning 2.25 years. Using the identified stylized facts of real FX market traders’ behavior, the authors evaluate the collective behavior of the trading agents in resembling the collective behavior of the FX market traders. The study identifies the conditions under which the stylized facts of trading agents’ collective behaviors resemble those for the real FX market traders’ collective behavior. The authors perform an exploration of the market’s features in order to identify the conditions under which the stylized facts emerge.
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.
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