Cryptocurrencies transactions advisor using a genetic Mamdani-type fuzzy rules based system

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dc.creatorTaiguara Melo Tupinambás
dc.creatorRafael Aeraf Leão Cadence
dc.creatorAndré Paim Lemos
dc.date.accessioned2025-04-09T15:03:35Z
dc.date.accessioned2025-09-09T00:43:20Z
dc.date.available2025-04-09T15:03:35Z
dc.date.issued2018
dc.identifier.doi10.1109/FUZZ-IEEE.2018.8491619
dc.identifier.urihttps://hdl.handle.net/1843/81412
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofInternational Conference on Fuzzy Systems (FUZZ-IEEE)
dc.rightsAcesso Restrito
dc.subjectSistemas difusos
dc.subjectEngenharia econômica
dc.subject.otherGenetic algorithms , Bitcoin , Sociology , Statistics , Linguistics , Forecasting
dc.subject.otherFuzzy Rules , Forecasting , Set Of Rules , Model Interpretation , Fuzzy Logic , Model Inference , Fuzzy Model , Currency Appreciation , Membership Function , Individual Fitness , Market Volatility , Tournament , Fuzzy System , Market Growth , Solution Representation , Individuals In Generation , Portfolio Investment , Output Prices , Transaction Fees , Final Balance
dc.subject.otherinference models for the forecasting of cryptocurrency prices variation. The number of rules per model and antecedents per rule were limited, so that the investor could understand the designed system. To deal with the accuracy-interpretability trade off, a genetic algorithm was employed to each cryptocurrency evaluated. When tested in a real case scenario, the five best models obtained better profits than the currency appreciation itself, in average
dc.titleCryptocurrencies transactions advisor using a genetic Mamdani-type fuzzy rules based system
dc.typeArtigo de evento
local.citation.spage1
local.description.resumoCryptocurrencies prices forecasting is a complex theme due to the chaotic market behavior and the influence of external events. Therefore, inference models should offer, in addition to a satisfying accuracy, reasonable interpretability, so that investors can decide based on their own knowledge. However, many studies in this subject focus on model accuracy and leave much to be desired in terms of simplicity and interpretability. This work proposes the use of Mamdani interpretable fuzzy inference models for forecasting cryptocurrency price variation. For that, a genetic algorithm to optimize models accuracy is employed, limiting the quantity of rules and antecedents arbitrarily. A set of infeasible rules had to be discarded, in order to generate interesting models, that produce a relevant amount of trades. Data from Kraken exchange were utilized for training, validation and results assessment. Results have shown that, for the cryptocurrencies with the highest validation performances, there are gains in comparison to the simple currency appreciation. Using the interpretable aspect of the models, it should be possible to obtain even higher profits.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://ieeexplore.ieee.org/document/8491619

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