Sezer, Ömer BeratÖzbayoğlu, MuratDoğdu, Erdoğan2019-12-182019-12-182017Sezer, Omer Berat; Ozbayoglu, Murat; Dogdu, Erdogan (2017). A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters, Conference: Complex Adaptive Systems Conference on Engineering Cyber Physical Systems (CAS) Location: Chicago, IL Date: OCT 30-NOV 01, 2017, Complex Adaptive Systems Conference With Theme: Engineering Cyber Physical Systems, Cas, 114, 473-480.1877-0509http://hdl.handle.net/20.500.12416/2180In this study, we propose a stock trading system based on optimized technical analysis parameters for creating buy-sell points using genetic algorithms. The model is developed utilizing Apache Spark big data platform. The optimized parameters are then passed to a deep MLP neural network for buy-sell-hold predictions. Dow 30 stocks are chosen for model validation. Each Dow stock is trained separately using daily close prices between 1996-2016 and tested between 2007-2016. The results indicate that optimizing the technical indicator parameters not only enhances the stock trading performance but also provides a model that might be used as an alternative to Buy and Hold and other standard technical analysis models. (c) 2017 The Authors. Published by Elsevier B.V.eninfo:eu-repo/semantics/openAccessStock TradingStock MarketDeep Neural-NetworkEvolutionary AlgorithmsTechnical AnalysisConference Object11447348010.1016/j.procs.2017.09.031