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A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters

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Ozbayoglu_Adeep.pdf (726.1Kb)
Date
2017
Author
Sezer, Omer Berat
Özbayoğlu, Ahmet Murat
Dogdu, Erdogan
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Abstract
In 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.
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https://www.sciencedirect.com/science/article/pii/S1877050917318252?via%3Dihub
http://hdl.handle.net/20.500.11851/1995
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  • Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering

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