Abstract
Trading a financial market involves the use of several tools that serve different purposes necessary to understand the behaviour of that particular market. One of the tools a trader must use is a method that determines the lot size of a trade, i.e., how many units are going to be bought or sold for a trade. Frequently, traders will use their experience and subjective deduction capabilities to determine the lot size, or simple mathematical formulas based on how much profit or loss they have realized during a particular period of time. This work proposes using a fuzzy inference system to determine the lot size, which uses input variables that any trading strategy should have access to (which means that any existing strategy can implement the proposed method). The experiments in this work compare basic trading strategies based on simple moving averages, one with a fixed lot size for every trade performed, and another one which uses the fuzzy inference system to establish a dynamic lot size. The results show that the dynamic lot size using the fuzzy inference system can help a trading strategy perform better.
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Hernandez-Aguila, A., Garcia-Valdez, M., Castillo, O. (2018). Money Management for a Foreign Exchange Trading Strategy Using a Fuzzy Inference System. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-71008-2_21
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DOI: https://doi.org/10.1007/978-3-319-71008-2_21
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